Fig 2(b) Observed velocity field for aspect ratio 0.25(Sukhodolov 2002)

고정 베드의 불침투성 토양에서 흐름 패턴의 수치 시뮬레이션

NUMERICAL SIMULATION OF FLOW PATTERN IN SERIES OF IMPERMEABLE GROYNES IN FIXED BED

Kafle, Mukesh Raj1
1Asst. Professor, Department of Civil Engineering, Institute of Engineering, Pulchowk Campus, Nepal
Email: mkafle@pcampus.edu.np

Abstract

This paper presents a numerical simulation of recirculating flow patterns in groyne fields. Moreover, it entails the concept determination of proper spacing of vertical unsubmerged and impermeable groynesin seriesto control the bank erosion. Flow pattern between the groynes varies along their space. The flow in groyne field may significantly affect the flow change, bed change, bank erosion and condition of habitat. In this regard, an assessment of flow along the space of groynes will yield important data needed to diversify the object of groyne installation. So, knowledge about determination of the proper spacing of groynes in groyne field is important. Space of vertical groynes was set from 1.5 to 10 times the length of groynes. The velocity field between groynes was simulated by using Computational Fluid Dynamics (CFD) model Nays 2D. Simulated velocity field was compared with existing experimentaldata for the same parameter, which agreed satisfactorily. Based on simulated results,the optimal spacing of vertical groynes to control the bank erosion was recommended.

이 논문은 groyne 필드에서 재순환 흐름 패턴의 수치 시뮬레이션을 제공합니다. 더욱이, 그것은 제방 침식을 제어하기 위해 수직 비침수 및 불침투성 그로이네신 시리즈의 적절한 간격의 개념 결정을 수반합니다. groynes 사이의 흐름 패턴은 공간에 따라 다릅니다. groyne field의 흐름은 흐름 변화, 하상 변화, 제방 침식 및 서식지 상태에 중대한 영향을 미칠 수 있습니다. 이와 관련하여, groyne 공간을 따른 흐름의 평가는 groyne 설치 대상을 다양화하는 데 필요한 중요한 데이터를 산출할 것입니다. 따라서, groyne field에서 groyne의 적절한 간격 결정에 대한 지식이 중요합니다. 수직 여백의 간격은 여아 길이의 1.5배에서 10배 사이로 설정하였다. groyne 사이의 속도장은 CFD(Computational Fluid Dynamics) 모델 Nays 2D를 사용하여 시뮬레이션되었습니다. 시뮬레이션된 속도장은 동일한 매개변수에 대해 기존 실험 데이터와 비교되었으며 만족스럽게 일치했습니다. 모의 결과를 바탕으로 제방 침식을 억제하기 위한 최적의 수직 제방 간격을 제안하였다.

  1. Introduction
    Spur dikes or groynes are used to protect river banks from erosion and also keep the channel
    navigable.Depending upon the flow characteristics, spur-dikes may be classified as submerged and unsubmerged. Also, based on the permeability, spur dikes are further classified as permeable and
    impermeable. Herein, un-submerged !impermeable spur dikes are dealt. These structures are built from the river bank into the stream flow and usually built in group. Construction of groyne against the flow causes significant changes in flow pattern in channel. Those changes may result in scour phenomenon around groynes which may lead structure instability and changes in river morphology. Moreover, in series of groynes, spacing of groynes leads different types of recirculating flow patterns.Therefore, investigating the characteristics of flow pattern around groynes have been a great interest in river engineering. Numerous researchers like Sukhodolov et al. (2002), Hao Zhang et al.(2009), Beheshti (2010), Duan (2009), Naji(2010), Karami(2011) made a variety of experiments in order to determine the flow pattern around groynes. Most of these researchers studied effect of single groyne, while using series of groynes is more effective in protection of rivers. Besides experimental studies, variety of CFD models have been developed for computing flow pattern around hydraulic structures; like Fluent, Flow 3D, Nays 2D, Nays CUBE and SSIIM. In this study, Nays 2D numerical modelling has been used to investigate flow and recirculating pattern around a series of groynes and streamlines including components of velocities.
  1. Flow pattern in groyne fields
    Under conditions where the groynes are not submerged, the groyne fields are not really part of the wetted cross section of a river. Because of that, the flow pattern in the groyne-field is not directly the result of the discharge in the main channel. Reducing the main stream velocity has no effect on the flow pattern itself, whereas lowering the water level does (Uijttewaal et al.2001). Moreover, the flow pattern inside a groyne field may change with the change of its geometry, location along the river (inner curve, outer curve, or straight part), and/ or the groynes orientation( Przedwojski et al.1995). However, there is an indirect effect of the discharge on the flow pattern in the groyne field. Because of the flow that is diverted from the main channel into the groyne fields, water flows into the groyne field with low velocity through the downstream half of the interfacial section between the groyne field and the main channel. This water flows back to the main channel through a small width of, just downstream the upstream groyne of the groyne field ( Termes et al.1991). Flow separates on a groyne head and forms a secondary flow represented by a large scale vortex with a vertical axis of rotation called primary gyre. Deflection of the flow inside the groyne field by banks and upstream groynes leads to the development of a secondary gyre with an opposite direction of rotation to the primary gyre. Location, mutual interactions, and energy exchange between gyres are the factors that create a specific recirculation pattern, and, consequently assuming correspondence with sedimentation processes, they define deposition patterns.
  2. Model Formulation
    The CFD model selected for this study is the publically available software NAYS 2D (iRIC 2.0), which is an analytical solver for calculation of unsteady two-dimensional plane flow and riverbed deformation using boundary-fitted coordinates within general curvilinear coordinates. A numerical channel of length 8.0m and width 0.9m was created with grid size of 0.01m im stream wise and 0.03m in cross stream directions. Groynes or spur dikes of length 0.15 and width 0.01m were chosen in series. Groyne field with various aspect ratio (b/x) 0.7, 0.25, 0.17, 0.125 and 0.10, where b=length of spur dike, x=spacing of two dikes. Discharge of 0.0175 m3 /s was applied. For boundary conditions, water surface at downstream and velocity at upstream were considered as uniform flow. Relaxation coefficient for water surface calculation was considered as 0.8. For the finite-difference method, the CIP method was applied to the advection terms in equations of motion. For the turbulent field calculation, Constant eddy viscosity, Zero-equation model and k-G models were applied and compared. The model!s accuracy in predicting the velocity magnitudes is evaluated using statistical parameters- mean absolute error (MAE), mean square error(MSE), and root mean square error (RMSE). The comparison of results shows the importance of selecting an appropriate turbulence model in simulating flow field around a spur dike. From the comparison, k-I model is found superior over zero energy model and eddy viscosity model. So, k-I model is chosen as appropriate turbulence closure model.
  3. Model!s Validation
    The capability of CFD model Nays 2D to simulate the velocity field and recirculation pattern in groyne field was compared with experimental data of laboratory experiments by Sukhodolov et al. (2002). The numerical simulation was validated for aspect ratio (R=b/x=0.7) and R=0.25. For aspect ratio R=0.7, one gyre system occupies the whole area of the groyne field. The areas with lower-than-average velocity values are clearly seen in the central part of the gyre and near its corners. Velocities increase towards the margins of the gyre. For aspect ratio R=0.25, two gyre velocity fields were observed in the groyne field. In the downstream part of the groyne field a large gyre, covering two-thirds of the area is clearly visible. The left part(upstream) contains second gyre rotating much more slowly and in the direction opposed to the primary gyre. The simulated and observed velocity field pattern and gyre found satisfactorily agreed. Now, after validation, the model was used for further analysis of velocity field for various aspect ratios.
Fig 2(b) Observed velocity field for aspect ratio 0.25(Sukhodolov 2002)
Fig 2(b) Observed velocity field for aspect ratio 0.25(Sukhodolov 2002)
  1. Results and Discussions
    The calibrated model was applied to five different cases of un-submerged and impermeable groyne fields with aspect ratios R=0.70,0.25,0.17,0.125 & 0.10 and flow pattern was numerically simulated. For aspect ratio R=0.7 i.e x/b=1.5, Fig 1(a) only one lateral primary gyre was formed inside the groyne field. The circulation pattern in this case is distinguished by the main flow that is deflected outside the groyne field. The developed primary gyre prevents the main flow from penetrating the groyne field. Therefore, this pattern is desirable for navigation purposes as a continuous deep channel is maintained along the face of the groyne field. Simulated velocity pattern satisfactorily agrees with the observed velocity field Fig 1(b) for the same aspect ratio by Sukhodolov (2002). The spacing of the groyne was further increased maintaining aspect ratio R= 0.25 i. e x/b=4 Fig 2(a) and flow pattern inside the groyne field was simulated. In this case, in the downstream part of the groyne field, a primary gyre occupying almost two-third area was formed. In addition, deflection of the flow inside the groyne field by banks and upstream groynes leads to the development of a secondary gyre with an opposite direction of rotation to the primary gyre covering almost one-third part of the groyne field. Likewise in the first case, the main current is maintained deflected outside the groyne field. Simulated velocity pattern satisfactorily agrees with the observed velocity field Fig 2(b) for the same aspect ratio by Sukhodolov (2002). The spacing of the groyne was further increased maintaining aspect ratio R=0.17 i.e x/b=6. In this case the flow pattern was similar to the aspect ratio R=0.25. The spacing of the groynes was further increased maintaining aspect ratio R=0.125 i. e x/b=8. In this case, similar to the previous scenarios two longitudinal gyres but with different positions are formed. The main current is directed in to the groyne field (Fig 3) creating a much more stronger eddy near the upstream groyne and greater turbulence along the upstream face and at the groyne lower head. As the spacing between groynes increased maintaining aspect ratio R=0.10 i. e x/b=10 (Fig 4), still primary and secondary gyres are generated. The formed gyres deflect the main flow thus preventing to enter in to the groyne field in upstream part. However, in the downstream of the primary gyre and just upstream of the second groyne, the flow attacks the bank directly. The resultant velocity profiles at the deflected region y/b=3 were plotted and how the spacing of second groyne affect the result was analyzed. Spacing of groynes makes little change in upstream resultant velocity. However, in the deflected region, its effect is significant. Higher value of spacing of groyne leads higher average deviation in resultant velocity. For aspect ratio R=0.7, the average deviation estimated as 0.02%. In the case of aspect ratio R=0.25, this value was reached to 1.57%. Further increment of spacing i. e decreasing the aspect ratio R=0.17, average deviation was found 3.82%. For the aspect ratio R=0.125, that value was estimated as 4.16%.
  2. Conclusions
    Geometry of the groyne fields; width and length of the groyne field mainly cause the specific flow patterns including number and shape of eddies or gyres. Eddies developed inside the groyne field deflects the main flow preventing it entering into the dead zone. An aspect ratio close to unity gives rise to a single eddy. A smaller aspect ratio (higher spacing between groynes) gives room to two stationary eddies, a large one called primary eddy, in the downstream part of the groyne field, and a smaller secondary eddy emerges near the upstream groyne. The extreme long groyne field -case of length to width ratio of larger thaneight shows penetration of main flow into the groyne field. The two eddies remain in a relatively stable position, while the main flow zone starts to penetrate into groyne field further downstream. In all cases, there is an eddy detaches from the upstream groyne tip that travels along the main channel groyne field interface and eventually merges with the primary eddy. The simulated results indicate that the spacing of groynes or spur dikes from the controlling of bank erosion point of view should be limited within six times the length of groyne.
Fig 3 Computed velocity field for aspect ratio 0.125
Fig 3 Computed velocity field for aspect ratio 0.125
Fig 4 Computed velocity field for aspect ratio 0.10
Fig 4 Computed velocity field for aspect ratio 0.10
Fig 5 Resultant velocity profiles at y/b=3
Fig 5 Resultant velocity profiles at y/b=3
Fig 5 Resultant velocity profiles at y/b=3
Fig 5 Resultant velocity profiles at y/b=3

References

  1. Holtz, K.P  Numerical simulation of recirculating flow at groynes.Å Computer Methods in Water Resources, Vol 2, No 2 (1991).
  2. Hossein, Bassar; Abdollah, Ardeshir; Hojat, Karami.  Numerical simulation of flow pattern around spur dikes series in rigid bed.Å 9th international congress on civil engineering, May 8- 10,2012, Isfahan University of Technology (IUT) , Isfahan, Iran (2012).
  3. Kang, J.G; Yeo, H.K; Kim,S.J An experimental study on a characteristics of flow around groyne area by install conditions.Å www.SciRP.org/journal/eng(2012).
  4. Shimizu,Y; Nelson,JIntroduction of Nays solver in iRIC.Åwww.i-ric.org(2012).
  5. Sukhodolov, A. Uijttewaal, W. S. J., and Engelhardt, C. On the correspondence between morphological and hydro dynamical patterns of groyne fields.Å Earth Surf. Processes Landforms, 27(3) (2002).
  6. Uijttewall, W.S.J; Lehman,D; VanMazijk,A.  Exchange process between a river and its groyne fields-model experiments.Å Journal of Hydraulic Engineering, ASCE, 127(11) (2001).
  7. Uijttewall, W.S.J Groyne field velocity patterns determined with particle tracking
    velocimetryÅ.28th IAHR congress, Graz, Austria (1999).
  8. Yossef, Mohamed  Flow details near groynes: Experimental investigations.Å Journal of Hydraulic Engineering, ASCE, 137 (2011).
Figura 4.5 – Superfície de contorno de velocidades a 3,9 m do piso após 20 s de acionamento da motosserra.

전기톱 절단 시험실에서 배기 시스템의 CFD 시뮬레이션

CFD Simulation of an exhaust system in chainsaw cutting test room

Área de Concentração: Energia e Fenômenos de Transporte
Orientador: Prof. Diogo Elias da Vinha Andrade
Comissão de Avaliação:
Profa
. Letícia Jenisch Rodrigues
Prof. Francis Henrique Ramos França
Prof. Paulo Smith Schneider

Abstract

The objective of the present work is to improve an exhaust system for a chain saw
cutting test room through a fluid dynamic computational simulation (CFD). The purpose of the
designed system is to remove combustion gases, such as carbon monoxide (CO), which is
extremely toxic, colourless and inodorous. The current system consists of a set of exhaust fans,
a hood and an insufflation set. From experimental tests, the input data of the simulation were
collected to define the variables and boundary conditions such as volumetric flow of CO, its
temperature and density and the supply of fresh air in the room. The necessary means of
instrumentation are presented so that it is possible to obtain the correlation with the results of
the simulation and, once validated, a study of mesh refinement was carried out. With this, the
possible solutions to the problem are evaluated through a case study involving the geometry of
the hood and the exhaust and insufflation systems. By changing the hood geometry, the most
satisfactory result was obtained for the problem, as it was shown to be able to remove all CO
from the room, respecting the proposed operational limits.

현재 연구의 목적은 유체 역학 계산 시뮬레이션(CFD)을 통해 체인 톱 절단 시험실의 배기 시스템을 개선하는 것입니다. 설계된 시스템의 목적은 매우 유독하고 무색이며 냄새가 나는 일산화탄소(CO)와 같은 연소 가스를 제거하는 것입니다. 현재 시스템은 배기 팬 세트, 후드 및 흡입 세트로 구성됩니다. 실험 테스트에서 시뮬레이션의 입력 데이터는 CO의 체적 유량, 온도 및 밀도, 실내의 신선한 공기 공급과 같은 변수 및 경계 조건을 정의하기 위해 수집되었습니다. 시뮬레이션 결과와의 상관관계를 얻을 수 있도록 필요한 계측 수단을 제시하고 검증 후 메쉬 미세화 연구를 수행했습니다. 이를 통해 후드의 기하학적 구조와 배기 및 흡입 시스템과 관련된 사례 연구를 통해 문제에 대한 가능한 솔루션을 평가합니다. 후드 형상을 변경함으로써 제안된 작동 한계를 준수하면서 실내에서 모든 CO를 제거할 수 있는 것으로 나타났기 때문에 문제에 대해 가장 만족스러운 결과를 얻었습니다.

Keywords

carbon monoxide, exhaust system, CFD simulation.

Figura 3.2 – Geometria simplificada da sala de testes da primeira versão.
Figura 3.2 – Geometria simplificada da sala de testes da primeira versão.
Figura 3.4 – Velocidade nos sensores de velocidade para verificar independência de malha para cada refino após 20 s do acionamento da motosserra.
Figura 3.4 – Velocidade nos sensores de velocidade para verificar independência de malha para cada refino após 20 s do acionamento da motosserra.
Figura 3.4 – Velocidade nos sensores de velocidade para verificar independência de malha para cada refino após 20 s do acionamento da motosserra.
Figura 3.4 – Velocidade nos sensores de velocidade para verificar independência de malha para cada refino após 20 s do acionamento da motosserra.
Figura 3.5 – Vista em detalhe da coifa e os elementos que a compõe.
Figura 3.5 – Vista em detalhe da coifa e os elementos que a compõe.
Figura 3.6 – Geometrias das versões simuladas do Teste de Casos.
Figura 3.6 – Geometrias das versões simuladas do Teste de Casos.
Figura 4.1 – Concentração de CO medida pelos sensores da simulação.
Figura 4.1 – Concentração de CO medida pelos sensores da simulação.
Figura 4.2 – Plano indicando os três cortes realizados na simulação para as superfícies de contorno sendo (1) a altura do escape da máquina, (2) a altura dos detectores de CO e (3) a altura dos exaustores
Figura 4.2 – Plano indicando os três cortes realizados na simulação para as superfícies de contorno sendo (1) a altura do escape da máquina, (2) a altura dos detectores de CO e (3) a altura dos exaustores
Figura 4.3 – Superfície de contorno de velocidades a 1,3 m do piso após 20 s de acionamento da motosserra.
Figura 4.3 – Superfície de contorno de velocidades a 1,3 m do piso após 20 s de acionamento da motosserra.
Figura 4.4 – Superfície de contorno de velocidades a 1,5 m do piso após 20 s de acionamento da motosserra.
Figura 4.4 – Superfície de contorno de velocidades a 1,5 m do piso após 20 s de acionamento da motosserra.
Figura 4.5 – Superfície de contorno de velocidades a 3,9 m do piso após 20 s de acionamento da motosserra.
Figura 4.5 – Superfície de contorno de velocidades a 3,9 m do piso após 20 s de acionamento da motosserra.
Figura 4.6 – Superfície de contorno de massas específicas a 1,3 m do piso após 20 s de acionamento da motosserra.
Figura 4.6 – Superfície de contorno de massas específicas a 1,3 m do piso após 20 s de acionamento da motosserra.
Figura 4.7 – Superfície de contorno de massas específicas a 1,5 m do piso após 20 s de acionamento da motosserra.
Figura 4.7 – Superfície de contorno de massas específicas a 1,5 m do piso após 20 s de acionamento da motosserra.
Figura 4.8 – Superfície de contorno de massas específicas a 3,9 m do piso após 20 s de acionamento da motosserra.
Figura 4.8 – Superfície de contorno de massas específicas a 3,9 m do piso após 20 s de acionamento da motosserra.
Figura 4.9 – Volume total de monóxido ao longo do tempo na sala.
Figura 4.9 – Volume total de monóxido ao longo do tempo na sala.
Figura 4.9 – Vazão volumétrica de CO ao longo do tempo através da superfície de controle. As linhas contínuas representam curvas de ajuste aos dados simulados.
Figura 4.9 – Vazão volumétrica de CO ao longo do tempo através da superfície de controle. As linhas contínuas representam curvas de ajuste aos dados simulados.

REFERENCIAS

CROWL, Daniel A.; LOUVAR, Joseph F. Chemical process safety: fundamentals with applications. Second Edition, Pearson Education, 2001. BURNETT, J.; CHAN, M. Y. Criteria for air quality in enclosed car parks. Em: Proceedings of the Institution of Civil Engineers-Transport. Thomas Telford-ICE Virtual Library, 1997. Disponível em: < http://www.icevirtuallibrary.com/doi/10.1680/itran.1997.29379> SITTISAK, P.; CHARINPANITKUL T.; CHALERMSINSUWAN, B. Enhancement of carbon monoxide removal in an underground car park using ventilation system with single and twin jet fans. Em: Tunnelling and Underground Space Technology. Volume 97, 2020. VERSTEEG, H.K.; MALALASEKERA, W. Computational Fluid Dynamics: The Finite Volume Method. Second Edition, Pearson Education, 2007. BULIŃSKA, A.; POPIOŁEK, Z.; BULIŃSKI, Z.; Experimentally validated CFD analysis on sampling region determination of average indoor carbon dioxide concentration in occupied space. Em: Building and Environment. Volume 72, 2014. KARIMI, H.; RIAZI, B.; MOHHAMMADI, M. Application of Computational Fluid Dynamics in the Simulation of Carbon Monoxide Distribution, a Case Study: Sayad Underground Tunnel in Tehran. Disponível em: YAKHOT, V.; ORSZAG, S. Renormalization group analysis of turbulence. I. Basic theory. Journal of scientific computing, v. 1, n. 1, p. 3-51, 1986. VAN HOOFF, T.; BLOCKEN, B. CFD evaluation of natural ventilation of indoor environments by the concentration decay method: CO2 gas dispersion from a semi-enclosed stadium. Building and Environment, v. 61, p. 1-17, 2013. Disponível em: < https://www.sciencedirect.com/science/article/pii/S0360132312003216> YANG, L., YE, M., HE, B. CFD simulation research on residential indoor air quality. Em Science of The Total Environment. Volume 472, 2014. Disponível em: < https://www.sciencedirect.com/science/article/pii/S0048969713014228> Flow-3D. Flow-3D User’s Guide. Versão 12, 2020. LAUNDER, B. E. e SPALDING, D. B. The numerical computation of turbulent flows. Em Computer Methods in Applied Mechanics and Engineering, vol. 3, 1974. pp. 269-289 MALISKA, Clovis R. Transferência de Calor e Mecânica dos Fluidos Computacional: fundamentos e coordenadas generalizadas. Segunda Edição. Rio de Janeiro, LTC, 2004. ROACHE, P. J. Perspective: A Method for Uniform Reporting of Grid Refinement Studies, Journal of Fluids Engineering, Vol. 116, 1994; 405-413.

Computational Fluid Dynamics, 온실

CFD 사용: 유압 구조 및 농업에서의 응용

USO DE CFD COMO HERRAMIENTA PARA LA MODELACIÓN Y  PREDICCIÓN NUMÉRICA DE LOS FLUIDOS: APLICACIONES EN  ESTRUCTURAS HIDRÁULICAS Y AGRICULTURA

Cruz Ernesto Aguilar-Rodriguez1*; Candido Ramirez-Ruiz2; Erick Dante Mattos Villarroel3 

1Tecnológico Nacional de México/ITS de Los Reyes. Carretera Los Reyes-Jacona, Col. Libertad. 60300.  Los Reyes de Salgado, Michoacán. México. 

ernesto.ar@losreyes.tecnm.mx – 3541013901 (*Autor de correspondencia) 

2Instituto de Ciencias Aplicadas y Tecnología, UNAM. Cto. Exterior S/N, C.U., Coyoacán, 04510, Ciudad  de México. México.  3Riego y Drenaje. Instituto Mexicano de Tecnología del Agua. Paseo Cuauhnáhuac 8532, Progreso,  Jiutepec, Morelos, C.P. 62550. México.

Abstract

공학에서 유체의 거동은 설명하기에 광범위하고 복잡한 과정이며, 유체역학은 유체의 거동을 지배하는 방정식을 통해 유체 역학 현상을 분석할 수 있는 과학 분야이지만 이러한 방정식에는 전체 솔루션이 없습니다. . 전산유체역학(Computational Fluid Dynamics, 이하 CFD)은 수치적 기법을 통해 방정식의 해에 접근할 수 있는 도구로, 신뢰할 수 있는 계산 모델을 얻기 위해서는 물리적 모델의 실험 데이터로 평가해야 합니다. 수력구조물에서 선형 및 미로형 여수로에서 시뮬레이션을 수행하고 배출 시트의 거동과 현재의 폭기 조건을 분석했습니다. 침강기에서 유체의 특성화를 수행하고 필요한 특성에 따라 사체적, 피스톤 또는 혼합의 분수를 수정하는 것이 가능합니다. 농업에서는 온실 환경을 특성화하고 환경에 대한 재료의 디자인, 방향 및 유형 간의 관계를 찾는 데 사용할 수 있습니다. 발견된 가장 중요한 결과 중 온실의 길이와 설계가 환기율에 미칠 수 있는 영향으로 온실의 길이는 높이의 6배 미만인 것이 권장됩니다.

키워드: Computational Fluid Dynamics, 온실,

Spillway, Settler 기사: COMEII-21048 소개 

CFD는 유체 운동 문제에 대한 수치적 솔루션을 얻어 수리학적 현상을 더 잘 이해할 수 있게 함으로써 공간 시각화를 가능하게 하는 수치 도구입니다. 예를 들어, 수력 공학에서 벤츄리(Xu, Gao, Zhao, & Wang, 2014) 워터 펌핑(ȘCHEAUA, 2016) 또는 개방 채널 적용( Wu et 알., 2000). 

문헌 검토는 실험 연구에서 검증된 배수로의 흐름 거동에 대한 수리학적 분석을 위한 CFD 도구의 효율성을 보여줍니다. 이 검토는 둑의 흐름 거동에 대한 수리학적 분석을 위한 CFD의 효율성을 보여줍니다. Crookston et al. (2012)는 미로 여수로에 대해 Flow 3D로 테스트를 수행했으며, 배출 계수의 결과는 3%에서 7%까지 다양한 오류로 실험적으로 얻은 결과로 허용 가능했으며 연구 결과 측면에 저압 영역이 있음을 발견했습니다. 익사 방식으로 작업할 때 위어의 벽. Zuhair(2013)는 수치 모델링 결과를 Mandali weir 원형의 실험 데이터와 비교했습니다.  

최근 연구에서는 다양한 난류 모델을 사용하여 CFD를 적용할 가능성이 있음을 보여주었습니다. 그리고 일부만이 음용수 처리를 위한 침적자의 사례 연구를 제시했으며, 다른 설계 변수 중에서 기하학적인 대안, 수온 변화 등을 제안했습니다. 따라서 기술 개발로 인해 설계 엔지니어가 유체 거동을 분석하는 데 CFD 도구를 점점 더 많이 사용하게 되었습니다. 

보호 농업에서 CFD는 온실 환경을 모델링하고 보조 냉방 또는 난방 시스템을 통해 온실의 미기후 관리를 위한 전략을 제안하는 데 사용되는 기술이었습니다(Aguilar Rodríguez et al., 2020).  

2D 및 3D CFD 모델을 사용한 본격적인 온실 시뮬레이션은 태양 복사 모델과 현열 및 잠열 교환 하위 모델의 통합을 통해 온실의 미기후 분포를 연구하는 데 사용되었습니다(Majdoubi, Boulard, Fatnassi, & Bouirden, 2009). 마찬가지로 이 모델을 사용하여 온실 설계(Sethi, 2009), 덮개 재료(Baxevanou, Fidaros, Bartzanas, & Kittas, 2018), 시간, 연중 계절( Tong, Christopher, Li, & Wang, 2013), 환기 유형 및 구성(Bartzanas, Boulard, & Kittas, 2004). 

CFD 거래 프로그램은 사용자 친화적인 플랫폼으로 설계되어 결과를 쉽게 관리하고 이해할 수 있습니다.  

Figura 1. Distribución de presiones y velocidades en un vertedor de pared delgada.
Figura 2. Perfiles de velocidad y presión en la cresta vertedora.
Figura 3. Condiciones de aireación en vertedor tipo laberinto. (A)lámina adherida a la pared del
vertedor, (B) aireado, (C) parcialmente aireado, (D) ahogado.
Figura 4. Realización de prueba de riego.
Figura 5. Efecto de la posición y dirección de los calefactores en un invernadero a 2 m del suelo.
Figura 5. Efecto de la posición y dirección de los calefactores en un invernadero a 2 m del suelo.
Figura 6. Indicadores ambientales para medir el confort ambiental de los cultivos.
Figura 6. Indicadores ambientales para medir el confort ambiental de los cultivos.
Figura 7. Líneas de corriente dentro del sedimentador experimental en estado estacionario  (Ramirez-Ruiz, 2019).
Figura 7. Líneas de corriente dentro del sedimentador experimental en estado estacionario (Ramirez-Ruiz, 2019).

Referencias Bibliográficas

Aguilar-Rodriguez, C.; Flores-Velazquez, J.; Ojeda-Bustamante, W.; Rojano, F.; Iñiguez-
Covarrubias, M. 2020. Valuation of the energyperformance of a greenhouse with

an electric heater using numerical simulations. Processes, 8, 600.

Aguilar-Rodriguez, C.; Flores-Velazquez, J.; Rojano, F.; Ojeda-Bustamante, W.; Iñiguez-
Covarrubias, M. 2020. Estimación del ciclo de cultivo de tomate (Solanum

lycopersicum L.) en invernadero, con base en grados días calor (GDC) simulados
con CFD. Tecnología y ciencias del agua, ISSN 2007-2422, 11(4), 27-57.
Al-Sammarraee, M., y Chan, A. (2009). Large-eddy simulations of particle sedimentation
in a longitudinal sedimentation basin of a water treatment plant. Part 2: The effects
of baffles. Chemical Engineering Journal, 152(2-3), 315-321.
doi:https://doi.org/10.1016/j.cej.2009.01.052.
Bartzanas, T.; Boulard, T.; Kittas, C. 2004. Effect of vent arrangement on windward
ventilation of a tunnel greenhouse. Biosystems Engineering, 88(4).
Baxevanou, C.; Fidaros, D.; Bartzanas, T.; Kittas, C. 2018. Yearly numerical evaluation of
greenhouse cover materials. Computers and Electronics in Agriculture, 149, 54–

  1. DOI: https://doi.org/10.1016/j.compag.2017.12.006.
    Crookston, B. M., & Tullis, B. P. 2012. Labyrinth weirs: Nappe interference and local
    submergence. Journal of Irrigation and Drainage Engineering, 138(8), 757-765.
    Fernández, J. M. 2012. Técnicas numéricas en Ingeniería de Fluidos: Introducción a la
    Dinámica de Fluidos Computacional (CFD) por el Método de Volumen Finito;
    Reverté, Barcelona, pp. 98-294.
    Goula, A., Kostoglou, M., Karapantsios, T., y Zouboulis, A. (2008). The effect of influent
    temperature variations in a sedimentation tank for potable water treatment— A
    computational fluid dynamics study. Water Research, 42(13), 3405-3414.
    doi://doi.org/10.1016/j.watres.2008.05.002.
    Majdoubi, H.; Boulard, T.; Fatnassi, H.; Bouirden, L. 2009. Airflow and microclimate
    patterns in a one-hectare Canary type greenhouse: an experimental and CFD
    assisted study. Agricultural and Forest Meteorology, 149(6-7), 1050-1062.
    Ramirez-Ruiz Candido (2019). Estudio hidrodinámico de sedimentadores de alta tasa en
    plantas potabilizadoras utilizando dinámica de fluidos computacional (CFD).
    Universidad Nacional Autónoma de México. Tesis de maestría.
    Sánchez, J. M. C., & Elsitdié, L. G. C. 2011. Consideraciones del mallado aplicadas al
    cálculo de flujos bifásicos con las técnicas de dinámica de fluidos computacional.
    J. Introd. Inv. UPCT., 4, 33-35.
    Sethi, V.P. 2009. On the selection of shape and orientation of a greenhouse: Thermal
    modeling and experimental validation, Sol. Energy, 83, 21–38.
    ȘCHEAUA, F. 2016. AGRICULTURAL FIELD IRRIGATION SOLUTION BASED ON
    VENTURI NOZZLE γ 2 g γ 2 g. JOURNAL OF INDUSTRIAL DESIGN AND
    ENGINEERING GRAPHICS, 2(1), 31–35.

Tong, G.; Christopher, D.; Li, T.; Wang, T. 2013. Passive solar energy utilization: a review
of cross-section building parameter selection for Chinese solar greenhouses.
Renewable and Sustainable Energy Reviews, 26, 540-548.

Xu, Y., Gao, L., Zhao, Y., & Wang, H. 2014. Wet gas overreading characteristics of a long-
throat Venturi at high pressure based on CFD. Flow Measurement and

Instrumentation, 40, 247–255. https://doi.org/10.1016/j.flowmeasinst.2014.09.004
Wu, W., Rodi, W y Wenka, T. 2000. 3D numerical modeling of flow and sediment transport
in open channels. ASCE Journal of Hydraulic Engineering. Vol 126 Num 1.
Zuhair al zubaidy, Riyadh. 2013. Numerical Simulation of Two-Phase Flow.
En:International Journal of Structural and Civil Engineering Research. Vol 2, No 3;
13p

Fig. 3. Experimental angled top-view setup for laser welding of zinc-coated steel with a laser illumination.

Effect of zinc vapor forces on spattering in partial penetration laser welding of zinc-coated steels

Yu Hao a, Nannan Chen a,b, Hui-Ping Wang c,*, Blair E. Carlson c, Fenggui Lu a,*
a Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai,
200240, PR China b Department of Industrial and Manufacturing Eng

ABSTRACT

A three-dimensional thermal-fluid numerical model considering zinc vapor interaction with the molten pool was developed to study the occurrence of zinc vapor-induced spatter in partial penetration laser overlap welding of zinc-coated steels. The zinc vapor effect was represented by two forces: a jet pressure force acting on the keyhole rear wall as the vapor bursts into the keyhole and a drag force on the upper keyhole wall as the vapor escapes upwards. The numerical model was calibrated by comparing the predicted keyhole shape with the keyhole shape observed by high-speed X-ray imaging and applied for various weld schedules. The study showed that large jet pressure forces induced violent fluctuations of the keyhole rear wall, resulting in an unstable keyhole and turbulent melt flow. A large drag force pushed the melt adjacent to the keyhole surface upward and accelerated the movement of the melt whose velocities reached 1 m/s or even higher, potentially inducing spatter. Increased heat input facilitated the occurrence of large droplets of spatter, which agreed with experimental observations captured by high-speed camera.

아연도금강의 부분용입 레이저 겹침용접에서 아연증기유도 스패터의 발생을 연구하기 위하여 용융풀과의 아연증기 상호작용을 고려한 3차원 열유체 수치모델을 개발하였습니다.

아연 증기 효과는 증기가 열쇠 구멍으로 폭발할 때 키홀 뒤쪽 벽에 작용하는 제트 압력력과 증기가 위쪽으로 빠져나갈 때 위쪽 키홀 벽에 작용하는 항력의 두 가지 힘으로 표시됩니다.

수치 모델은 예측된 열쇠 구멍 모양과 고속 X선 영상으로 관찰된 키홀 모양을 비교하여 보정하고 다양한 용접 일정에 적용했습니다.

이 연구는 큰 제트 압력이 키홀 뒷벽의 격렬한 변동을 유발하여 불안정한 열쇠 구멍과 난류 용융 흐름을 초래한다는 것을 보여주었습니다. 큰 항력은 키홀 표면에 인접한 용융물을 위로 밀어올리고 속도가 1m/s 이상에 도달한 용융물의 이동을 가속화하여 잠재적으로 스패터를 유발할 수 있습니다.

증가된 열 입력은 고속 카메라로 포착한 실험적 관찰과 일치하는 큰 방울의 스패터 발생을 촉진했습니다.

Fig. 1. Schematic of zero-gap laser welding of zinc-coated steel.
Fig. 1. Schematic of zero-gap laser welding of zinc-coated steel.
Fig. 2. Experimental setup for capturing a side view of the laser welding of zinc-coated steel enabled by use of high-temperature glass.
Fig. 2. Experimental setup for capturing a side view of the laser welding of zinc-coated steel enabled by use of high-temperature glass.
Fig. 3. Experimental angled top-view setup for laser welding of zinc-coated steel with a laser illumination.
Fig. 3. Experimental angled top-view setup for laser welding of zinc-coated steel with a laser illumination.
Fig. 4. Schematic of the rotating Gaussian body heat source.
Fig. 4. Schematic of the rotating Gaussian body heat source.
Fig. 5. Schematic of jet pressure force caused by zinc vapor: (a) locating the outlet of zinc vapor (point A), (b) schematic of assigning the jet pressure force.
Fig. 5. Schematic of jet pressure force caused by zinc vapor: (a) locating the outlet of zinc vapor (point A), (b) schematic of assigning the jet pressure force.
Fig. 6. Schematic of drag force caused by zinc vapor.
Fig. 6. Schematic of drag force caused by zinc vapor.
Fig. 7. Procedure for calculating the outgassing velocity of zinc vapor.
Fig. 7. Procedure for calculating the outgassing velocity of zinc vapor.
Fig. 8. Schematic related to calculating the zone of vaporized zinc.
Fig. 8. Schematic related to calculating the zone of vaporized zinc.
Fig. 9. The meshed domains for the thermal-fluid simulation of laser welding.
Fig. 9. The meshed domains for the thermal-fluid simulation of laser welding.
Fig. 10. The calculated temperature field and validation: (a) 3-D temperature field; (b)-(f) Comparison of experimental and simulated weld cross section: (b) P = 2000 W, v = 50 mm/s; (c) P = 2500 W, v = 50 mm/s; (d) P = 3000 W, v = 50 mm/s; (e) P = 3000 W, v = 60 mm/s; (f) P = 3000 W, v = 70 mm/s.
Fig. 10. The calculated temperature field and validation: (a) 3-D temperature field; (b)-(f) Comparison of experimental and simulated weld cross section: (b) P = 2000 W, v = 50 mm/s; (c) P = 2500 W, v = 50 mm/s; (d) P = 3000 W, v = 50 mm/s; (e) P = 3000 W, v = 60 mm/s; (f) P = 3000 W, v = 70 mm/s.
Fig. 11. Comparison of X-Ray images of in-process keyhole profiles and the numerical predictions: (a) Single sheet penetration (P = 480 W, v = 150 mm/s); (b) Two sheet penetration (P = 532 W, v = 150 mm/s).
Fig. 11. Comparison of X-Ray images of in-process keyhole profiles and the numerical predictions: (a) Single sheet penetration (P = 480 W, v = 150 mm/s); (b) Two sheet penetration (P = 532 W, v = 150 mm/s).
Fig. 12. High-speed images of dynamic keyhole in laser welding of steels: (a) without zinc coating (b) with zinc coating.
Fig. 12. High-speed images of dynamic keyhole in laser welding of steels: (a) without zinc coating (b) with zinc coating.
Fig. 13. Mass loss and molten pool observation under different laser power and welding velocity for 1.2 mm + 1.2 mm HDG 420LA stack-up
Fig. 13. Mass loss and molten pool observation under different laser power and welding velocity for 1.2 mm + 1.2 mm HDG 420LA stack-up
Fig. 14. Numerical results of keyhole and flow field in molten pool: (a) without zinc vapor forces, (b) with zinc vapor forces.
Fig. 14. Numerical results of keyhole and flow field in molten pool: (a) without zinc vapor forces, (b) with zinc vapor forces.
Fig. 18. Calculated velocity fields for different welding parameters: (a) P = 2 kW, v = 50 mm/s, (b) P = 2.5 kW, v = 50 mm/s, (c) P = 3 kW, v = 50 mm/s, (d) P = 3 kW, v = 60 mm/s, (e) P = 3 kW, v = 70 mm/s.
Fig. 18. Calculated velocity fields for different welding parameters: (a) P = 2 kW, v = 50 mm/s, (b) P = 2.5 kW, v = 50 mm/s, (c) P = 3 kW, v = 50 mm/s, (d) P = 3 kW, v = 60 mm/s, (e) P = 3 kW, v = 70 mm/s.
Fig. 19. Schematic of the generation of spatter in different sizes: (a) small size, (b) large size.
Fig. 19. Schematic of the generation of spatter in different sizes: (a) small size, (b) large size.

References

Ai, Y., Jiang, P., Wang, C., et al., 2018. Experimental and numerical analysis of molten
pool and keyhole profile during high-power deep-penetration laser welding. Int. J.
Heat Mass Transf. 126 (part-A), 779–789.
Chen, Z., Yang, S., Wang, C., et al., 2014. A study of fiber laser welding of galvanized
steel using a suction method. J. Mater. Process. Technol. 214 (7), 1456–1465.
Cho, W.I., Na, S.J., Thomy, C., et al., 2012. Numerical simulation of molten pool
dynamics in high power disk laser welding. J. Mater. Process. Technol. 212 (1),
262–275.
Deng, S., Wang, H.P., Lu, F., et al., 2019. Investigation of spatter occurrence in remote
laser spiral welding of zinc-coated steels. Int. J. Heat Mass Transf. 140 (9), 269–280.
Fabbro, R., Coste, F., Goebels, D., et al., 2006. Study of CW Nd-Yag laser welding of Zncoated steel sheets. J. Phys. D Appl. Phys. 39 (2), 401.
Gao, Z., Wu, Y., Huang, J., 2009. Analysis of weld pool dynamic during stationary
laser–MIG hybrid welding. Int. J. Adv. Manuf. Technol. 44 (9), 870–879.
Kaplan, A., 1994. A model of deep penetration laser welding based on calculation of the
keyhole profile. J. Phys. D Appl. Phys. 27 (9), 1805.
Kim, J., Oh, S., Ki, H., 2015. A study of keyhole geometry in laser welding of zinc-coated
and uncoated steels using a coaxial observation method. J. Mater. Process. Technol.
225, 451–462.
Kim, J., Oh, S., Ki, H., 2016. Effect of keyhole geometry and dynamics in zero-gap laser
welding of zinc-coated steel sheets. J. Mater. Process. Technol. 232, 131–141.
Koch, H., KaGeler, C., Otto, A., et al., 2011. Analysis of welding zinc coated steel sheets
in zero gap configuration by 3D simulations and high-speed imaging. Phys. Procedia
12 (part-B), 428–436.
Kouraytem, N., Li, X., Cunningham, R., et al., 2019. Effect of laser-matter interaction on
molten pool flow and keyhole dynamics. Phys. Rev. Appl. 11 (6), 54–64.
Li, S., Chen, G., Katayama, S., et al., 2014. Relationship between spatter formation and
dynamic molten pool during high-power deep-penetration laser welding. Appl. Surf.
Sci. 303 (6), 481–488.
Ma, J., 2013. Experimental and Numerical Studies on the Issues in Laser Welding of
Galvanized High-Strength Dual-Phase Steels in a Zero-Gap Lap Joint Configuration,
PhD Thesis. Southern Methodist University.
Pan, Y., 2011. Laser Welding of Zinc Coated Steel Without a Pre-Set Gap, PhD Thesis.
Delft University of Technology.
Schmidt, M., Otto, A., 2008. Analysis of YAG laser lap-welding of zinc coated steel sheets.
CIRP Ann. Manuf. Technol. 57, 213–216.
Semak, V., Matsunawa, A., 1999. The role of recoil pressure in energy balance during
laser materials processing. J. Phys. D Appl. Phys. 30 (18), 2541.
Wu, S., Zhao, H., Wang, Y., Zhang, X., 2004. A new heat source model in numerical
simulation of high energy beam welding. Trans. China Weld. 21, 99–102.
Yaws, C.L., 2015. The Yaws Handbook of Vapor Pressure: Antoine Coefficients.
Zhou, J., Tsai, H.L., 2008. Modeling of transport phenomena in hybrid laser-MIG keyhole
welding. Int. J. Heat Mass Transf. 51 (17–18), 4353–4366.

Figure 16: Velocity Vectors of Flow at Ghulmet

댐 붕괴 홍수파 및 범람 매핑 시뮬레이션: A
아타바드 호수 사례 연구

Simulation of Dam-Break Flood Wave and Inundation Mapping: A
Case study of Attabad Lake

Wasim Karam1, Fayaz A. Khan2, Muhammad Alam3, Sajjad Ali4
1Lab. Engineer, Department of Civil Engineering, University of Engineering and Technology Mardan, Pakistan,
wasim10karam@gmail.com
2Assistant Professor, National Institute of Urban Infrastructure Planning, University of Engineering and Technology Peshawar,
Pakistan, fayazuet@yahoo.com
3,4Assistant Professor, Department of Civil Engineering, University of Engineering and Technology Mardan, Pakistan,
emalam82@gmail.com, sajjadali@uetmardan.edu.pk

ABSTRACT

산사태 또는 제방 댐의 파손 연구는 구성이 불확실하고 자연적이며 재해에 대해 적절하게 설계되지 않았기 때문에 다른 자연적 사건에 대한 대응 지식이 부족하기 때문에 더 중요합니다. 이 논문은 댐 ​​파괴의 수력학적 모델링의 다양한 방법을 개선하는 것을 목표로 합니다.

현재 이 연구에서 Attabad 호수의 댐 붕괴는 전산 유체 역학 기술을 사용하여 시뮬레이션됩니다. 수치 모델(FLOW-3D)은 Reynolds 평균 Navier-Stoke 방정식을 완전히 3D로 풀어서 다양한 단면에서의 피크 유량 깊이, 피크 속도, 피크 방전, 피크 깊이까지의 시간 및 피크 방전까지의 시간을 예측하기 위해 개발되었습니다.

표준 RNG 난류 모델을 사용하여 난류를 시뮬레이션한 다음 마을의 흐름에 대한 홍수 범람 지도와 속도 벡터를 그립니다. 결과는 Hunza 강의 수로를 통해 모델링된 홍수파의 대부분이 Hunza 강의 범람원에 포함되지만 Hunza 강의 범람원 내부에 위치한 Miaun 및 chalat와 같은 일부 마을의 경우 더 높은 위험에 있음을 보여줍니다.

그러나 이들 마을의 예상 홍수 도달 시간은 각각 31분과 44분으로 인구를 안전한 지역으로 대피시키기에 충분한 시간인 반면, 알리 아바드에 인접한 하산 아바드와 같은 일부 마을의 경우 침수 위험이 더 높은 반면 마을의 예상 홍수 도착 시간은 12분으로 인구 대피에 충분하지 않으므로 홍수 억제를 위한 추가 홍수 보호 구조가 필요합니다.

최고속도의 추정치는 하천평야의 더 높은 전단응력, 심한 침식의 위험, 농경지 피해, 주거지 및 형태학적 변화가 예상됨을 의미한다. 댐 파손 분석(예: 최고 깊이, 최고 속도, 홍수 도달 시간 및 홍수 범람 지도)은 향후 위험 분석 및 홍수 관리의 지침으로만 사용해야 합니다.

Figure 2: Case Study Location on Map of Pakistan
Figure 2: Case Study Location on Map of Pakistan
Figure 3: Lake Condition 3 months after Landslide
Figure 3: Lake Condition 3 months after Landslide
Figure 5: 3D Model from the Merged DEM
Figure 5: 3D Model from the Merged DEM
Figure 7: Free Surface Elevation relative to local origin
Figure 7: Free Surface Elevation relative to local origin
Figure 8: Model of lake referenced over Google Earth Image
Figure 8: Model of lake referenced over Google Earth Image
Figure 9: Meshing in the 3D Terrain Model
Figure 9: Meshing in the 3D Terrain Model
Figure 10: Flow Depth Hydrographs of the downstream villages  (A) Karim Abad (B) Ghulmet (C) Thol (D) Chalat (E) Nomal
Figure 10: Flow Depth Hydrographs of the downstream villages (A) Karim Abad (B) Ghulmet (C) Thol (D) Chalat (E) Nomal
Figure 11: Flow Hydrograph at Karim Abad and Nomal Bridge
Figure 11: Flow Hydrograph at Karim Abad and Nomal Bridge
Figure 12: Flood Inundation Map of Karim Abad
Figure 12: Flood Inundation Map of Karim Abad
Figure 13: Flood Inundation Map of Ghulmet
Figure 13: Flood Inundation Map of Ghulmet
Figure 14: Flood Inundation Map of Chalat
Figure 14: Flood Inundation Map of Chalat
Figure 15: Velocity Vectors of flow at Karim Abad
Figure 15: Velocity Vectors of flow at Karim Abad
Figure 16: Velocity Vectors of Flow at Ghulmet
Figure 16: Velocity Vectors of Flow at Ghulmet
Figure 17: Velocity Vectors of Flow at Chalat
Figure 17: Velocity Vectors of Flow at Chalat

REFERENCES

[1]. Zhang, L. & Peng, M. & Chang, D.S. & Xu, Y. (2015).
Dam Failure Mechanisms and Risk Assessment, First
Ed. John Wiley and Sons, Singapore 473 pp.
10.1002/9781118558522.
[2]. T. L. Wahl, “Dam Breach Modeling – an Overview of
Analysis Methods,” 2nd Jt. Fed. Interagency Conf. Las
Vegas, NV, pp. 1–12, 2010.
[3]. Khosravi K. “Dam Break Analysis and Flood
Inundation Mapping : The Case Study of Sefid-Rud
Dam,” no. August 2019. DOI:
10.1016/B978-0-12-815998-9.00031-2
[4]. Robb, D. M., & Vasquez, J. A. (2015). Numerical
simulation of dam-break flows using depth-averaged
hydrodynamic and three-dimensional CFD models.
22nd Canadian Hydrotechnical Conference, (June).
[5]. Mohammad Rostami, M. S. (2015). Human Life Saving
by Simulation of Dam Break using Flow-3D. Trend in
Life Sciences, 4(3), 308–316
[6]. Gharbi, M., Soualmia, A., Dartus, D., & Masbernat, L.
(2016). Comparison of 1D and 2D hydraulic models
for floods simulation on the Medjerda River in
Tunisia. Journal of Materials and Environmental
Science, 7(8), 3017–3026. https://doi.org/10.1080/153
[7]. Andrei, A., Robert, B., & Erika, B. (2017). Numerical
Limitations of 1D Hydraulic Models Using MIKE11
or HEC-RAS software – A case study of Baraolt
River, Romania. IOP Conference Series: Materials
Science and Engineering, 245(7).
https://doi.org/10.1088/1757-899X/245/7/072010
[8]. Henderson, F.M. (1966). Open Channel Flow. MacMillan
Company, New York, USA, P. No 304-313
[9]. Betsholtz, A., & Nordlöf, B. (2017). Potentials and
limitations of 1D, 2D and coupled 1D-2D flood
modeling in HEC-RAS. Lund University, 128.
https://doi.org/10.1016/S0300-9440(03)00139-5
[10].Ozmen-Cagatay, H., & Kocaman, S. (2011). Dam-break
flow in the presence of obstacle: Experiment and CFD
simulation. Engineering Applications of Computational
Fluid Mechanics, 5(4), 541–552.
https://doi.org/10.1080/19942060.2011.11015393
[11].Toombes, L., & Chanson, H. (2011). Numerical
Limitations of Hydraulic Models. 10th Hydraulics
Conference, (July), 2322–2329.
https://doi.org/10.1016/j.jalz.2016.06.1613
[12].Zarein, M. (2015). Modeling Dam-Break Flows Using
a 3d Mike 3 Flow Model, (January).
[13].George, A. C., & Nair, B. T. (2015). Dam Break
Analysis Using BOSS DAMBRK. Aquatic Procedia,
4(Icwrcoe), 853–860.
https://doi.org/10.1016/j.aqpro.2015.02.10
[14].S. Roga and K. M. Pandey, “Computational Analysis of
Supersonic Flow Regime Using Ramp Injector with
Standard K- ω Turbulence Model” .World Academy of
research in Science and Engineering, vol. 2, no. 1, pp.
31–40, 2013.http:// doi.org/10.1.1.348.5862.

Fig. 1. A typical Boiling Water Reactor (BWR) and selected segment of study for simulation

Understanding dry-out mechanism in rod bundles of boiling water reactor

끓는 물 원자로 봉 다발의 건조 메커니즘 이해

Liril D.SilviaDinesh K.ChandrakercSumanaGhoshaArup KDasb
aDepartment of Chemical Engineering, Indian Institute of Technology, Roorkee, India
bDepartment of Mechanical Engineering, Indian Institute of Technology, Roorkee, India
cReactor Engineering Division, Bhabha Atomic Research Centre, Mumbai, India

Abstract

Present work reports numerical understanding of interfacial dynamics during co-flow of vapor and liquid phases of water inside a typical Boiling Water Reactor (BWR), consisting of a nuclear fuel rod bundle assembly of 7 pins in a circular array. Two representative spacings between rods in a circular array are used to carry out the simulation. In literature, flow boiling in a nuclear reactor is dealt with mechanistic models or averaged equations. Hence, in the present study using the Volume of Fluid (VOF) based multiphase model, a detailed numerical understanding of breaking and making in interfaces during flow boiling in BWR is targeted. Our work will portray near realistic vapor bubble and liquid flow dynamics in rod bundle scenario. Constant wall heat flux for fuel rod and uniform velocity of the liquid at the inlet patch is applied as a boundary condition. The saturation properties of water are taken at 30 bar pressure. Flow boiling stages involving bubble nucleation, growth, merging, local dry-out, rewetting with liquid patches, and complete dry-out are illustrated. The dry-out phenomenon with no liquid presence is numerically observed with phase fraction contours at various axial cut-sections. The quantification of the liquid phase fraction at different axial planes is plotted over time, emphasizing the progressive dry-out mechanism. A comparison of liquid-vapor distribution for inner and outer rods reveals that the inner rod’s dry-out occurs sooner than that of the outer rod. The heat transfer coefficient to identify the heat dissipation capacity of each case is also reported.

현재 작업은 원형 배열에 있는 7개의 핀으로 구성된 핵연료봉 다발 어셈블리로 구성된 일반적인 끓는 물 원자로(BWR) 내부의 물의 증기 및 액체상의 동시 흐름 동안 계면 역학에 대한 수치적 이해를 보고합니다.

원형 배열의 막대 사이에 두 개의 대표적인 간격이 시뮬레이션을 수행하는 데 사용됩니다. 문헌에서 원자로의 유동 비등은 기계론적 모델 또는 평균 방정식으로 처리됩니다.

따라서 VOF(Volume of Fluid) 기반 다상 모델을 사용하는 본 연구에서는 BWR에서 유동 비등 동안 계면의 파괴 및 생성에 대한 자세한 수치적 이해를 목표로 합니다.

우리의 작업은 막대 번들 시나리오에서 거의 사실적인 증기 기포 및 액체 흐름 역학을 묘사합니다. 연료봉에 대한 일정한 벽 열유속과 입구 패치에서 액체의 균일한 속도가 경계 조건으로 적용됩니다. 물의 포화 특성은 30bar 압력에서 취합니다.

기포 핵 생성, 성장, 병합, 국소 건조, 액체 패치로 재습윤 및 완전한 건조를 포함하는 유동 비등 단계가 설명됩니다. 액체가 존재하지 않는 건조 현상은 다양한 축 단면에서 위상 분율 윤곽으로 수치적으로 관찰됩니다.

다른 축 평면에서 액상 분율의 정량화는 점진적인 건조 메커니즘을 강조하면서 시간이 지남에 따라 표시됩니다. 내부 막대와 외부 막대의 액-증기 분포를 비교하면 내부 막대의 건조가 외부 막대보다 더 빨리 발생함을 알 수 있습니다. 각 경우의 방열 용량을 식별하기 위한 열 전달 계수도 보고됩니다.

Fig. 1. A typical Boiling Water Reactor (BWR) and selected segment of study for simulation
Fig. 1. A typical Boiling Water Reactor (BWR) and selected segment of study for simulation
Fig. 2. (a-c) dimensions and mesh configuration for G = 6 mm; (d-f) dimensions and mesh configuration for G = 0.6 mm
Fig. 2. (a-c) dimensions and mesh configuration for G = 6 mm; (d-f) dimensions and mesh configuration for G = 0.6 mm
Fig. 3. Simulating the effect of spacer (a) Spacer configuration around rod bundle (b) Mesh structure in spacer zone (c) Distribution of vapor bubbles in a rod bundle with spacer (d) Liquid phase fraction comparison for geometry with and without spacer (e,f,g) Wall temperature comparison for geometry with and without spacer; WS: With Spacer, WOS: Without Spacer; Temperature in the y-axis is in (f) and (g) is same as (e).
Fig. 3. Simulating the effect of spacer (a) Spacer configuration around rod bundle (b) Mesh structure in spacer zone (c) Distribution of vapor bubbles in a rod bundle with spacer (d) Liquid phase fraction comparison for geometry with and without spacer (e,f,g) Wall temperature comparison for geometry with and without spacer; WS: With Spacer, WOS: Without Spacer; Temperature in the y-axis is in (f) and (g) is same as (e).
Fig. 4. Validation of the present numerical model with crossflow boiling over a heated cylindrical rod [40]
Fig. 4. Validation of the present numerical model with crossflow boiling over a heated cylindrical rod [40]
Fig. 5. Grid-Independent study in terms of vapor volume in 1/4th of computational domain
Fig. 5. Grid-Independent study in terms of vapor volume in 1/4th of computational domain
Fig. 6. Interface contour for G = 6 mm; ul = 1.2 m/s; q˙ w = 396 kW/m2; they are showing nucleation, growth, merging, and pseudo-steady-state condition.
Fig. 6. Interface contour for G = 6 mm; ul = 1.2 m/s; q˙ w = 396 kW/m2; they are showing nucleation, growth, merging, and pseudo-steady-state condition.
Fig. 7. Interface contours for G = 0.6 mm; ul = 1.2 m/s; q˙ w = 396 kW/m2; It shows dry-out at pseudo-steady-state near the exit
Fig. 7. Interface contours for G = 0.6 mm; ul = 1.2 m/s; q˙ w = 396 kW/m2; It shows dry-out at pseudo-steady-state near the exit
Fig. 8. Vapor-liquid distribution across various distant cross-sections (Black color indicates liquid; Gray color indicates vapor); Magnification factor: 1 × (for a and b), 1.5 × (for c and d)
Fig. 8. Vapor-liquid distribution across various distant cross-sections (Black color indicates liquid; Gray color indicates vapor); Magnification factor: 1 × (for a and b), 1.5 × (for c and d)
Fig. 21. Two-phase flow mixture velocity (u¯z); for G = 6 mm, r = 5 means location at inner heated wall and r = 25 means location at outer adiabatic wall; for G = 0.66 mm, r = 5 means location at inner heated wall and r = 16.6 mm means location at outer adiabatic wall.
Fig. 21. Two-phase flow mixture velocity (u¯z); for G = 6 mm, r = 5 means location at inner heated wall and r = 25 means location at outer adiabatic wall; for G = 0.66 mm, r = 5 means location at inner heated wall and r = 16.6 mm means location at outer adiabatic wall.

References

[1] J. Würtz, An Experimental and Theoretical Investigation of Annular Steam-Water Flow in Tubes and Annuli at 30 to 90 Bar, Risø National Laboratory,
Roskilde, 1978.
[2] W. Tian, A. Myint, Z. Li, S. Qiu, G.H. Su, D. Jia, Experimental study on dryout point in vertical narrow annulus under low flow conditions, in: International Conference on Nuclear Engineering, 4689, 2004, pp. 643–648. Jan
1Vol.
[3] K.M. Becker, C.H. Ling, S. Hedberg, G. Strand, An experimental investigation of
post dryout heat transfer, R. Inst. Technol. (1983).
[4] K.M. Becker, A Burnout Correlation for Flow of Boiling Water in Vertical Rod
Bundles, AB Atomenergi, 1967.
[5] Jr J.R. Barbosa, G.F. Hewitt, S.M. Richardson, High-speed visualisation of nucleate boiling in vertical annular flow, Int. J. Heat Mass Transf. 46 (26) (2003)
5153–5160 1, doi:10.1016/S0017-9310(03)00255-2.
[6] Y. Mizutani, A. Tomiyama, S. Hosokawa, A. Sou, Y. Kudo, K. Mishima, Twophase flow patterns in a four by four rod bundle, J. Nucl. Sci. Technol. 44 (6)
(2007) 894–901 1, doi:10.1080/18811248.2007.9711327.
[7] S.S. Paranjape, Two-Phase Flow Interfacial Structures in a Rod Bundle Geometry, Purdue University, 2009.
[8] D. Lavicka, J. Polansky, Model of the cooling of a nuclear reactor fuel rod, Multiph. Sci. Technol. 25 (2-4) (2013), doi:10.1615/MultScienTechn.v25.i2-4.90.
[9] M. Thurgood, J. Kelly, T. Guidotti, R. Kohrt, K. Crowell, Tech. rep., Pacific Northwest National Laboratory, 1983.
[10] S. Sugawara, Droplet deposition and entrainment modeling based on the
three-fluid model, Nucl. Eng. Des. 122 (1-3) (1990) 67–84, doi:10.1016/
0029-5493(90)90197-6.
[11] C. Adamsson, J.M. Le Corre, Modeling and validation of a mechanistic tool
(MEFISTO) for the prediction of critical power in BWR fuel assemblies, Nucl.
Eng. Des. 241 (8) (2011) 2843–2858, doi:10.1016/j.nucengdes.2011.01.033.
[12] S. Talebi, H. Kazeminejad, A mathematical approach to predict dryout in a rod
bundle, Nucl. Eng. Des. 249 (2012) 348–356, doi:10.1016/j.nucengdes.2012.04.
016.
[13] H. Anglart, O. Nylund, N. Kurul, M.Z. Podowski, CFD prediction of flow and
phase distribution in fuel assemblies with spacers, Nucl. Eng. Des. 177 (1-3)
(1997) 215–228, doi:10.1016/S0029-5493(97)00195-7.
[14] H. Li, H. Anglart, CFD model of diabatic annular two-phase flow using the
Eulerian–Lagrangian approach, Ann. Nucl. Energy 77 (2015) 415–424, doi:10.
1016/j.anucene.2014.12.002.
[15] G. Sorokin, A. Sorokin, Experimental and numerical investigation of liquid metal boiling in fuel subassemblies under natural circulation conditions, Prog. Nucl. Energy 47 (1-4) (2005) 656–663, doi:10.1016/j.pnucene.2005.
05.069.
[16] W.D. Pointer, A. Tentner, T. Sofu, D. Weber, S. Lo, A. Splawski, Eulerian
two-phase computational fluid dynamics for boiling water reactor core analysis, Joint International Topical Meeting on Mathematics and Computation and
Supercomputing in Nuclear Applications (M and C± SNA), 2007.
[17] K. Podila, Y. Rao, CFD modelling of supercritical water flow and heat transfer
in a 2 × 2 fuel rod bundle, Nucl. Eng. Des. 301 (2016) 279–289, doi:10.1016/j.
nucengdes.2016.03.019.
[18] H. Pothukuchi, S. Kelm, B.S. Patnaik, B.V. Prasad, H.J. Allelein, Numerical investigation of subcooled flow boiling in an annulus under the influence of eccentricity, Appl. Therm. Eng. 129 (2018) 1604–1617, doi:10.1016/j.applthermaleng.
2017.10.105.
[19] H. Pothukuchi, S. Kelm, B.S. Patnaik, B.V. Prasad, H.J. Allelein, CFD modeling of
critical heat flux in flow boiling: validation and assessment of closure models,
Appl. Therm. Eng. 150 (2019) 651–665, doi:10.1016/j.applthermaleng.2019.01.
030.
[20] W. Fan, H. Li, H. Anglart, A study of rewetting and conjugate heat transfer
influence on dryout and post-dryout phenomena with a multi-domain coupled CFD approach, Int. J. Heat Mass Transf. 163 (2020) 120503, doi:10.1016/j.
ijheatmasstransfer.2020.120503.
[21] R. Zhang, T. Cong, G. Su, J. Wang, S. Qiu, Investigation on the critical heat
flux in typical 5 by 5 rod bundle at conditions prototypical of PWR based
on CFD methodology, Appl. Therm. Eng. 179 (2020) 115582, doi:10.1016/j.
applthermaleng.2020.115582.

[22] L.D. Silvi, A. Saha, D.K. Chandraker, S. Ghosh, A.K. Das, Numerical analysis of
pre-dryout sequences through the route of interfacial evolution in annular gasliquid two-phase flow with phase change, Chem. Eng. Sci. 212 (2020) 115356,
doi:10.1016/j.ces.2019.115356.
[23] L.D. Silvi, D.K. Chandraker, S. Ghosh, A.K. Das, On-route to dryout through sequential interfacial dynamics in annular flow boiling around temperature and
heat flux controlled heater rod, Chem. Eng. Sci. 229 (2021) 116014, doi:10.1016/
j.ces.2020.116014.
[24] J.U. Brackbill, D.B. Kothe, C. Zemach, A continuum method for modeling surface
tension, J. Comput. Phys. 100 (2) (1992) 335–354, doi:10.1016/0021-9991(92)
90240-Y.
[25] B. Lafaurie, C. Nardone, R. Scardovelli, S. Zaleski, G. Zanetti, Modelling merging
and fragmentation in multiphase flows with SURFER, J. Comput. Phys. 113 (1)
(1994) 134–147, doi:10.1006/jcph.1994.1123.
[26] I. Tanasawa, Advances in condensation heat transfer, Ad. Heat Transf. 21 (1991)
55–139 Vol, doi:10.1016/S0065-2717(08)70334-4.
[27] V.H. Del Valle, D.B. Kenning, Subcooled flow boiling at high heat flux, Int.
J. Heat Mass Transf. 28 (10) (1985) 1907–1920, doi:10.1016/0017-9310(85)
90213-3.
[28] B. Matzner, G.M. Latter, Reduced pressure drop space for boiling water reactor
fuel bundles, US Patent US5375154A, (1993)
[29] C. Unal, O. Badr, K. Tuzla, J.C. Chen, S. Neti, Pressure drop at rod-bundle spacers
in the post-CHF dispersed flow regime, Int. J. Multiphase Flow 20 (3) (1994)
515–522, doi:10.1016/0301-9322(94)90025-6.
[30] D.K. Chandraker, A.K. Nayak, V.P. Krishnan, Effect of spacer on the dryout of
BWR fuel rod assemblies, Nucl. Eng. Des. 294 (2015), doi:10.1016/j.nucengdes.
2015.09.004.
[31] S.K Verma, S.L. Sinha, D.K. Chandraker, A comprehensive review of the spacer
effect on performance of nuclear fuel bundle using computational fluid dynamics methodology, Mater. Today: Proc. 4 (2017) 100030–110034, doi:10.
1016/j.matpr.2017.06.315.
[32] S.K Verma, S.L. Sinha, D.K. Chandraker, Experimental investigation on the effect
of space on the turbulent mixing in vertical pressure tube-type boiling water
reactor, Nucl. Sci. Eng. 190 (2) (2018), doi:10.1080/00295639.2017.1413874.
[33] T. Zhang, Y. Liu, Numerical investigation of flow and heat transfer characteristics of subcooled boiling in a single rod channel with/without spacer grid,
Case Stud. Therm. Eng. 20 (2020) 100644, doi:10.1016/j.csite.2020.100644.
[34] K.M. Becker, G. Hernborg, M. Bode, O. Eriksson, Burnout data for flow of boiling water in vertical round ducts, annuli and rod clusters, AB Atomenergi
(1965).
[35] A. Saha, A.K. Das, Numerical study of boiling around wires and influence of
active or passive neighbours on vapour film dynamics, Int. J. Heat Mass Transf.
130 (2019) 440–454, doi:10.1016/j.ijheatmasstransfer.2018.10.117.
[36] M. Reimann, U. Grigull, Heat transfer with free convection and film boiling in
the critical area of water and carbon dioxide, Heat Mass Transf. 8 (1975) 229–
239, doi:10.1007/BF01002151.
[37] M.S. Plesset, S.A. Zwick, The growth of vapor bubbles in superheated liquids, J.
Appl. Phys. 25 (4) (1954) 493–500, doi:10.1063/1.1721668.
[38] N. Samkhaniani, M.R. Ansari, Numerical simulation of superheated vapor bubble rising in stagnant liquid, Heat Mass Transf. 53 (9) (2017) 2885–2899,
doi:10.1007/S00231-017-2031-6.
[39] N. Samkhaniani, M.R. Ansari, The evaluation of the diffuse interface method
for phase change simulations using OpenFOAM, Heat Transf. Asian Res. 46 (8)
(2017) 1173–1203, doi:10.1002/htj.21268.
[40] P. Goel, A.K. Nayak, M.K. Das, J.B. Joshi, Bubble departure characteristics in a
horizontal tube bundle under cross flow conditions, Int. J. Multiph. Flow 100
(2018) 143–154, doi:10.1016/j.ijmultiphaseflow.2017.12.013.
[41] K.M. Becker, J. Engstorm, B.Scholin Nylund, B. Sodequist, Analysis of the dryout
incident in the Oskarshamn 2 boiling water reactor, Int. J. Multiph. Flow 16 (6)
(1990) 959–974, doi:10.1016/0301-9322(90)90101-N.
[42] H.G. Weller, A New Approach to VOF-Based Interface Capturing Methods
for Incompressible and Compressible Flow, A New Approach to VOF-Based
Interface Capturing Methods for Incompressible and Compressible Flow, 4,
OpenCFD Ltd., 2008 Report TR/HGW.
[43] G. Boeing, Visual analysis of nonlinear dynamical systems: chaos, fractals, selfsimilarity and the limits of prediction, Systems 4 (4) (2016) 37, doi:10.3390/
systems4040037.

Fig. 4. Meshed quarter aluminum model with HAZ regions and support steel plates.

Benchmark study on slamming response of flat-stiffened plates considering fluid-structure interaction

유체-구조 상호작용을 고려한 평판 보강판의 슬래밍 응답에 대한 벤치마크 연구

Dac DungTruongabBeom-SeonJangaCarl-ErikJansoncJonas W.RingsbergcYasuhiraYamadadKotaTakamotofYasumiKawamuraeHan-BaekJua
aResearch Institute of Marine Systems Engineering, Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, South Korea
bDepartment of Engineering Mechanics, Nha Trang University, Nha Trang, Viet Nam
cDivision of Marine Technology, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
dNational Maritime Research Institute, National Institute of Maritime, Port and Aviation Technology, Tokyo, Japan
eDepartment of Systems Design for Ocean-Space, Yokohama National University, Kanagawa, Japan
fDepartment of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

ABSTRACT

이 논문은 해양구조물의 평보강판의 슬래밍 반응에 대한 벤치마크 연구를 제시합니다. 목표는 유체-구조 상호작용(FSI) 시뮬레이션 방법론, 모델링 기술 및 슬래밍 압력 예측에 대한 기존 연구원의 경험을 비교하는 것이었습니다.

수치 FSI 시뮬레이션을 위해 가장 일반적인 상용 소프트웨어 패키지를 사용하는 3개의 연구 그룹(예: LS-Dyna ALE, LS-Dyna ICFD, ANSYS CFX 및 Star-CCM+/ABAQUS)이 이 연구에 참여했습니다.

공개 문헌에서 입수할 수 있는 경량 선박과 같은 바닥 구조의 평평한 강화 알루미늄 판에 대한 습식 낙하 시험 데이터는 FSI 모델링의 검증에 활용되었습니다. 형상 모델 및 재료 속성을 포함한 실험 조건의 요약은 시뮬레이션 전에 참가자에게 배포되었습니다.

충돌 속도와 강판의 강성이 슬래밍 응답에 미치는 영향을 조사하기 위해 해양 설비에 사용되는 실제 치수를 갖는 평판 보강 강판에 대한 매개변수 연구를 수행했습니다. 보강판에 작용하는 전체 수직력에 대한 FE 시뮬레이션 결과와 이러한 힘에 대한 구조적 반응을 참가자로부터 획득하여 분석 및 비교하였다.

앞서 언급한 상용 FSI 소프트웨어 패키지를 사용하여 슬래밍 부하에 대한 신뢰할 수 있고 정확한 예측을 평가했습니다. 또한 FSI 시뮬레이션에서 관찰된 동일한 영구 처짐을 초래하는 등가 정적 슬래밍 압력을 보고하고 분류 표준 DNV에서 제안한 해석 모델 및 슬래밍 압력 계산을 위한 기존 실험 데이터와 비교했습니다.

연구 결과는 등가 하중 모델이 물 충돌 속도와 플레이트 강성에 의존한다는 것을 보여주었습니다. 즉, 등가정압계수는 충돌속도가 증가함에 따라 감소하고 충돌구조가 더 단단해지면 증가한다.

This paper presents a benchmark study on the slamming responses of offshore structures’ flat-stiffened plates. The objective was to compare the fluid-structure interaction (FSI) simulation methodologies, modeling techniques, and established researchers’ experiences in predicting slamming pressure. Three research groups employing the most common commercial software packages for numerical FSI simulations (i.e. LS-Dyna ALE, LS-Dyna ICFD, ANSYS CFX, and Star-CCM+/ABAQUS) participated in this study. Wet drop test data on flat-stiffened aluminum plates of light-ship-like bottom structures available in the open literature was utilized for validation of the FSI modeling. A summary of the experimental conditions including the geometry model and material properties, was distributed to the participants prior to their simulations. A parametric study on flat-stiffened steel plates having actual scantlings used in marine installations was performed to investigate the effect of impact velocity and plate rigidity on slamming response. The FE simulation results for the total vertical forces acting on the stiffened plates and their structural responses to those forces, as obtained from the participants, were analyzed and compared. The reliable and accurate predictions of slamming loads using the aforementioned commercial FSI software packages were evaluated. Additionally, equivalent static slamming pressures resulting in the same permanent deflections, as observed from the FSI simulations, were reported and compared with analytical models proposed by the Classification Standards DNV and existing experimental data for calculation of the slamming pressure. The study results showed that the equivalent load model depends on the water impact velocity and plate rigidity; that is, the equivalent static pressure coefficient decreases with an increase in impact velocity, and increases when impacting structures become stiffer.

Fig. 4. Meshed quarter aluminum model with HAZ regions and support steel plates.
Fig. 4. Meshed quarter aluminum model with HAZ regions and support steel plates.
Fig. 6. (a) Boundary conditions of water hitting case and (b) water jets at end of the simulation.
Fig. 6. (a) Boundary conditions of water hitting case and (b) water jets at end of the simulation.
Fig. 7. Comparison of prediction and test results for deflection time history of (a) D1 and (b) D2 for Vi = 2.3 m/s.
Fig. 7. Comparison of prediction and test results for deflection time history of (a) D1 and (b) D2 for Vi = 2.3 m/s.
Fig. 8. Comparison of prediction and test results for maximum deflection with different impact velocities.
Fig. 8. Comparison of prediction and test results for maximum deflection with different impact velocities.
Fig. 16. Boundary conditions applied to present FSI simulations (Sym. denotes symmetric, and Cons. denotes constrained)
Fig. 16. Boundary conditions applied to present FSI simulations (Sym. denotes symmetric, and Cons. denotes constrained)
Fig. 24. Distribution of deflections at moment of maximum deflection in: (a) LS-Dyna ALE, (b) Star-CCM+/ABAQUS, (c) ANSYS CFD, and (d) LSDyna ICFD (unit: m).

Keywords

Benchmark studyEquivalent static pressureFlat-stiffened plateFluid-structure interactionPermanent deflectionSlamming pressure coefficient

References

[1] Von Karman TH. The impact on seaplane floats during landing. Washington, DC: National Advisory Committee for Aeronautics; 1929. Technical note No.: 321.
[2] Wagner VH. Über Stoß- und Gleitvorgange ¨ an der Oberflache ¨ von Flüssigkeiten. Z Angew Math Mech 1932;12(4):193–215.
[3] Chuang SL. Experiments on flat-bottom slamming. J Ship Res 1966;10:10–7.
[4] Chuang SL. Investigation of impact of rigid and elastic bodies with water. Report for Department of the Navy. Washington, DC: United States Department of the
Navy; 1970. Report No.: 3248.
[5] Mori K. Response of the bottom plate of high-speed crafts under impulsive water pressure. J Soc Nav Archit Jpn 1977;142:297–305 [Japanese].
[6] Cheon JS, Jang BS, Yim KH, Lee HSD, Koo BY, Ju HB. A study on slamming pressure on a flat stiffened plate considering fluid–structure interaction. J Mar Sci
Technol 2016;21:309–24.
[7] Truong DD, Jang BS, Ju HB, Han SW. Prediction of slamming pressure considering fluid-structure interaction. Part I: Numerical simulations. Ships Offshore
Struct. https://doi.org/10.1080/17445302.2020.1816732.
[8] Truong DD, Jang BS, Ju HB, Han SW. Prediction of slamming pressure considering fluid-structure interaction. Part II: Derivation of empirical formulations. Mar
Struct. https://doi.org/10.1016/j.marstruc.2019.102700.
[9] Greenhow M, Lin W. Numerical simulation of nonlinear free surface flows generated by wedge entry and wave maker motions. In: Proceedings of the 4th
international conference on numerical ship hydrodynamics, Washington, DC; 1985.
[10] Sun H, Faltinsen OM. Water impact of horizontal circular cylinders and cylindrical shells. Appl Ocean Res 2006;28(5):299–311.
[11] Gingold RA, Monaghan JJ. Smoothed particle hydrodynamics: theory and application to non-spherical stars. Royal Astronomical Society 1977;181:375–89.
[12] Shao S. Incompressible SPH simulation of water entry of a free-falling object. Int J Numer Methods Fluid 2009;59(1):91–115.
[13] Souli M, Ouahsine A, Lewin L. ALE formulation for fluid-structure interaction problems. Comput Methods Appl Mech Eng 2000;190(5):659–75.
[14] Livermore Software Technology Corporation (LSTC). ICFD theory manual incompressible fluid solver in LS-DYNA. Livermore Software Technology Corporation;

[15] Livermore Software Technology Corporation (LSTC). LS-DYNA theoretical manual. Livermore Software Technology Corporation; 2006.
[16] FLOW-3D user’s manual. 2018., version 12.0.
[17] Cd-adapco. STAR-CCM+ User’s manual. 2012., version 7.06.
[18] ANSYS fluent user’s guide. 2015.
[19] ANSYS CFX user’s guide. 2014.
[20] Abaqus user’s manual, version 6.13. SIMULIA; 2013.
[21] Luo HB, Hu J, Guedes Soares C. Numerical simulation of hydroelastic responses of flat stiffened panels under slamming loads. In: Proceedings of the 29th
international conference on ocean, offshore and arctic engineering (OMAE2010); 2010 [Shanghai, China].[22] Yamada Y, Takami T, Oka M. Numerical study on the slamming impact of wedge shaped obstacles considering fluid-structure interaction (FSI). In: Proceedings
of the 22nd international offshore and polar engineering conference (ISOPE2012); 2012 [Rhodes, Greece].
[23] Luo HB, Wang H, Guedes Soares C. Numerical and experimental study of hydrodynamic impact and elastic response of one free-drop wedge with stiffened
panels. Ocean Eng 2012;40:1–14.
[24] Sun H, Wang DY. Experimental and numerical analysis of hydrodynamic impact on stiffened side of three dimensional elastic stiffened plates. Adv Mech Eng
2018;10(4):1–23.
[25] Ma S, Mahfuz H. Finite element simulation of composite ship structures with fluid structure interaction. Ocean Eng 2012;52:52–9.
[26] LSTC. Turek & hron’s FSI benchmark problem. 2012.
[27] Califano A, Brinchmann K. Evaluation of loads during a free-fall lifeboat drop. In: Proceedings of the ASME 32nd international conference on ocean, offshore
and arctic engineering (OMAE2013); 2013 [Nantes, France].
[28] LSTC. 3D fluid elastic body interaction problem. 2014.
[29] Yamada Y, Takamoto K, Nakanishi T, Ma C, Komoriyama Y. Numerical study on the slamming impact of stiffened flat panel using ICFD method – effect of
structural rigidity on the slamming impact. In: Proceedings of the ASME 39th international conference on ocean, offshore and arctic engineering (OMAE2020);
2020 [Florida, USA].
[30] Nicolici S, Bilegan RM. Fluid structure interaction modeling of liquid sloshing phenomena in flexible tanks in flexible tanks. Nucl Eng Des 2013;258:51–6.
[31] DNV. DNV-RP-C205 environmental conditions and environmental loads. Det Norske Veritas; October 2010.
[32] Ahmed YM. Numerical simulation for the free surface flow around a complex ship hull form at different froude numbers. Alex Eng J 2011;50(3):229–35.
[33] Ghadimi P, Feizi Chekab MA, Dashtimanesh A. Numerical simulation of water entry of different arbitrary bow sections. J Nav Architect Mar Eng 2014;11:
117–29.
[34] Park BW, Cho S-R. Simple design formulae for predicting the residual damage of unstiffened and stiffened plates under explosive loadings. Int J Impact Eng
2006;32:1721–36.
[35] Truong DD, Shin HK, Cho S-R. Permanent set evolution of aluminium-alloy plates due to repeated impulsive pressure loadings induced by slamming. J Mar Sci
Technol 2018;23:580–95.
[36] Jones N. Structural impact. first ed. Cambridge, UK: Cambridge University Press; 1989.
[37] Zha Y, Moan T. Ultimate strength of stiffened aluminium panels with predominantly torsional failure modes. Thin-Walled Struct 2001;39:631–48.
[38] Sensharma P, Collette M, Harrington J. Effect of welded properties on aluminum structures. Ship Structure Committee SSC-4 2010.
[39] ABS. Guide for slamming loads and strength assessment for vessels. 2011.
[40] Villavicencio R, Sutherland L, Guedes Soares C. Numerical simulation of transversely impacted, clamped circular aluminium plates. Ships Offshore Struct 2012;7(1):31–45.
[41] Material properties database. https://www.varmintal.com/aengr.htm, Assessed date: 16 May 2020.
[42] Ringsberg JW, Andri´c J, Heggelund SE, Homma N, Huang YT, Jang BS, et al. Report of the ISSC technical committee II.1 on quasi-static response. In:
Kaminski ML, Rigo P, editors. Proceedings of the 20th international ship and offshore structures congress (ISSC 2018), vol. 1. IOS Press BV; 2018. p. 226–31.
[43] Shin HK, Kim S-C, Cho S-R. Experimental investigations on slamming impacts by drop tests. J Soc Nav Archit Korea 2010;47(3):410–20 [Korean].
[44] Huera-Huarte FJ, Jeon D, Gharib M. Experimental investigation of water slamming loads on panels. Ocean Eng 2011;38:1347–55.

Fig. 8. Pressure distribution during the infiltration of preform with the 50 ¯m particles and 20 % starches: (a) 25 % filled, (b) 57 % filled, and (c) 99 % filled.

Experimental study and numerical simulation of infiltration of AlSi12 alloys into Si porous preforms with micro-computed tomography inspection characteristics

마이크로 컴퓨터 단층 촬영 검사 특성을 가진 Si 다공성 프리폼에 AlSi12 합금의 침투에 대한 실험적 연구 및 수치 시뮬레이션

Ruizhe LIU1 and Haidong ZHAO1
1National Engineering Research Center of Near-Net-Shape Forming for Metallic Materials, South China University of Technology,
Guangzhou 510640, China

Abstract

전분 함량(10, 20 및 30%)과 입자 크기(20, 50 및 90 m)가 다른 실리콘 입자 예비 성형체는 압축 성형 및 열처리를 통해 제작되었습니다. 프리폼의 기공 특성은 고해상도(³1 m) 3차원(3D) X선 마이크로 컴퓨터 단층 촬영(V-CT)으로 검사되었습니다. AlSi12 합금의 프리폼으로의 침투는 진공 보조 압력 침투 장치에서 800 °C 및 400 kPa의 조건에서 서로 다른 압력 적용 시간(3, 8 및 15초)으로 수행되었습니다. 고해상도(³500 nm) 수직 주사 백색광 간섭 프로파일로미터를 사용하여 복합 재료의 전면을 감지했습니다. Navier-Stokes 방정식을 기반으로 하는 ¯-CT 검사에서 실제 기공 형상을 고려하여 침투를 미시적으로 시뮬레이션했습니다. 그 결과 전분 함량과 입자크기가 증가할수록 복합재료의 표면적이 증가하는 것으로 나타났다. 전분 함량과 비교하여 입자 크기는 전면 표면적에 더 많은 영향을 미칩니다. 시뮬레이션에서 침투가 진행됨에 따라 액체 AlSi12의 압력이 감소했습니다. 복합재의 잔류 기공은 침투와 함께 증가했습니다. 실험 및 시뮬레이션 결과에 따르면 침투 방향을 따라 더 큰 압력 강하가 복합 재료의 더 많은 잔류 기공을 유도합니다.

Silicon particle preforms with different starch contents (10, 20 and 30%) and particle sizes (20, 50 and 90 ¯m) were fabricated by compression mold forming and heat treatment. The pore characteristics of preforms were inspected with a high-resolution (³1 ¯m) three-dimensional (3D) X-ray micro-computed tomography (¯-CT). The infiltration of AlSi12 alloys into the preforms were carried out under the condition of 800 °C and 400 kPa with different pressure-applied times (3, 8 and 15 s) in a vacuum-assisted pressure infiltration apparatus. A highresolution (³500 nm) vertical scanning white light interfering profilometer was used to detect the front surfaces of composites. The infiltration was simulated at micro-scale by considering the actual pore geometry from the ¯- CT inspection based on the Navier-Stokes equation. The results demonstrated that as the starch content and particle size increased, the front surface area of composite increased. Compared with the starch content, the particle size has more influence on the front surface area. In the simulation, as the infiltration progressed, the pressure of liquid AlSi12 decreased. The residual pores of composites increased with infiltration. According to the experiment and simulation results, a larger pressure drop along the infiltration direction leads to more residual pores of composites.

Fig. 1. Size distributions of Si particles.
Fig. 1. Size distributions of Si particles.
Fig. 2. Schematic of different locations of composites.
Fig. 2. Schematic of different locations of composites.
Fig. 3. Three-dimensional geometry with the reconstruction technology, enmeshment and infiltration parameters of Si preforms: (a) geometry, and (b) meshes and flow direction.
Fig. 3. Three-dimensional geometry with the reconstruction technology, enmeshment and infiltration parameters of Si preforms: (a) geometry, and (b) meshes and flow direction.
Fig. 4. Number-based frequencies of effective pore radius and throat radius: (a) effective pore radius of preforms with the 50 ¯m particles, (b) effective throat radius of preforms with the 50 ¯m particles, (c) effective pore radius of preforms with the 20 % starches, and (d) effective throat radius of preforms with the 20 % starches.
Fig. 4. Number-based frequencies of effective pore radius and throat radius: (a) effective pore radius of preforms with the 50 ¯m particles, (b) effective throat radius of preforms with the 50 ¯m particles, (c) effective pore radius of preforms with the 20 % starches, and (d) effective throat radius of preforms with the 20 % starches.
Fig. 5. 3D topological morphologies of front surfaces of composites: (a) 50 ¯m-10 %, (b) 50 ¯m-20 %, (c) 50 ¯m-30 %, (d) 20 ¯m-20 %, and (e) 90 ¯m-20 %.
Fig. 5. 3D topological morphologies of front surfaces of composites: (a) 50 ¯m-10 %, (b) 50 ¯m-20 %, (c) 50 ¯m-30 %, (d) 20 ¯m-20 %, and (e) 90 ¯m-20 %.
Fig. 6. Schematic of capillary tube.
Fig. 6. Schematic of capillary tube.
Fig. 8. Pressure distribution during the infiltration of preform with the 50 ¯m particles and 20 % starches: (a) 25 % filled, (b) 57 % filled, and (c) 99 % filled.
Fig. 8. Pressure distribution during the infiltration of preform with the 50 ¯m particles and 20 % starches: (a) 25 % filled, (b) 57 % filled, and (c) 99 % filled.
Fig. 9. Pressure distributions of liquid AlSi12 during the infiltration of preforms: (a) different fill fractions, (b) different starch contents, and (c) different particle sizes.
Fig. 9. Pressure distributions of liquid AlSi12 during the infiltration of preforms: (a) different fill fractions, (b) different starch contents, and (c) different particle sizes.
Fig. 10. Metallographs of composites: (a) different locations of composite with the 20 ¯m particles and 20 % starches, and (b) different locations of composite with the 90 ¯m particles and 20 % starches.
Fig. 10. Metallographs of composites: (a) different locations of composite with the 20 ¯m particles and 20 % starches, and (b) different locations of composite with the 90 ¯m particles and 20 % starches.
Fig. 11. Area fractions of residual pores of composites: (a) 50 ¯m (different starch contents), and (b) 20 % (different particle sizes).
Fig. 11. Area fractions of residual pores of composites: (a) 50 ¯m (different starch contents), and (b) 20 % (different particle sizes).

References

1) V. G. Resmi, K. M. Sree Manu, V. Lakshmi, M.
Brahmakumar, T. P. D. Rajan, C. Pavithran and B. C.
Pai, J. Porous Mat., 22, 1445­1454 (2015).
2) C. García-Cordovilla, E. Louis and J. Narciso, Acta
Mater., 47, 4461­4479 (1999).
3) D. B. Miracle, Compos. Sci. Technol., 65, 2526­2540
(2005).
4) J. M. Chiou and D. D. L. Chung, J. Mater. Sci., 28,
1447­1470 (1993).
5) Q. G. Zhang and M. Y. Gu, J. Compos. Mater., 40, 471­
478 (2006).
6) C. M. Lawrence Wu and G. W. Han, Compos. Part AAppl. S., 37, 1858­1862 (2006).
7) X. Y. Cai, X. W. Yin, X. K. Ma, X. M. Fan, Y. Z. Cai,
J. P. Li, L. F. Cheng and L. T. Zhang, Ceram. Int., 42,
10144­10150 (2016).
8) J. M. Molina, E. Piñero, J. Narciso, C. GarcíaCordovilla and E. Louis, Curr. Opin. Solid St. M., 9,
202­210 (2005).
9) A. Léger, L. Weber and A. Mortensen, Acta Mater., 91,
57­69 (2015).
10) Y. Q. Ma, L. H. Qi, W. G. Zheng, J. M. Zhou and L. Y.
Ju, T. Nonferr. Metal. Soc., 23, 1915­1921 (2013).
11) J. T. Tian, E. Piñero, J. Narciso and E. Louis, Scripta
Mater., 53, 1483­1488 (2005).
12) J. Narciso, A. Alonso, A. Pamies, C. García-Cordovilla
and E. Louis, Metall. Mater. Trans. A, 26A, 983­990
(1995).
13) J. Roger, M. Avenel and L. Lapuyade, J. Eur. Ceram.
Soc., 40, 1859­1868 (2020).
14) J. Roger, M. Avenel and L. Lapuyade, J. Eur. Ceram.
Soc., 40, 1869­1876 (2020).
15) R. Scardovelli and S. Zaleski, Annu. Rev. Fluid Mech.,
31, 567­603 (1999).
16) H. D. Zhao, I. Ohnaka and J. D. Zhu, Appl. Math.
Model., 32, 185­194 (2008).
17) Y. He, A. E. Bayly, A. Hassanpour, F. Muller, K. Wu
and D. M. Yang, Powder Technol., 338, 548­562
(2018).
18) K. D. Nikitin, K. M. Terekhov and Y. V. Vassilevski,
Appl. Math. Lett., 86, 236­242 (2018).
19) J. F. Xiao, X. Liu, Y. M. Luo, J. C. Cai and J. F. Xu,
Colloid. Surface. A, 591, 124572 (2020).
20) N. Birgle, R. Masson and L. Trenty, J. Comput. Phys.,
368, 210­235 (2018).
21) M. Chaaban, Y. Heider and B. Markert, Int. J. Heat
Fluid Fl., 83, 108566 (2020).
22) S. Zhang, M. J. Zhu, X. Zhao, D. G. Xiong, H. Wan,
S. X. Bai and X. D. Wang, Compos. Part A-Appl. S., 90,
71­81 (2016).
23) J. Roger, L. Guesnet, A. Marchais and Y. Le Petitcorps,
J. Alloy. Compd., 747, 484­494 (2018).
24) Q. Wan, H. D. Zhao and C. Zou, ISIJ Int., 54, 511­515
(2014).
25) F. Liu, H. D. Zhao, R. S. Yang and F. Z. Sun, Mater.
Today Commun., 19, 114­123 (2019).
26) D. Roussel, A. Lichtner, D. Jauffrès, J. Villanova, R. K.
Bordia and C. L. Martin, Scripta Mater., 113, 250­253
(2016).
27) M. Fukushima, T. Ohji, H. Hyuga, C. Matsunaga and Y.
Yoshizawa, J. Mater. Res., 32, 3286­3293 (2017).
28) M. Fukushima, H. Hyuga, C. Matsunaga and Y.
Yoshizawa, J. Am. Ceram. Soc., 101, 3266­3270
(2018).
29) R. Z. Liu, H. D. Zhao, H. Long and B. Xie, Mater.
Charact., 137, 370­378 (2017).
30) B. Xie, H. D. Zhao, H. Long, J. L. Peng and R. Z. Liu,
Ceram. Int., 45, 23924­23933 (2019).
31) R. Z. Liu, H. D. Zhao and B. Xie, Transport Porous
Med., 131, 1053­1063 (2020).
32) Y. Li, H. W. Chen, F. Q. Wang, X. L. Xia and H. P. Tan,
Infrared Phys. Techn., 113, 103646 (2021).
33) P. Tahmasebi, M. Sahimi, A. H. Kohanpur and A.
Valocchi, J. Petrol. Sci. Eng., 155, 21­33 (2017).
34) B. Gharedaghloo, S. J. Berg and E. A. Sudicky, Adv.
Water Resour., 143, 103681 (2020).
35) A. Viswanath, M. V. Manu, S. Savithri and U. T. S.
Pillai, J. Mater. Process. Tech., 244, 320­330 (2017).
36) D. Silin and T. Patzek, Physica A, 371, 336­360 (2006).
37) W. Hui, Y. S. Wang, D. Z. Ren and H. Jin, J. Petrol. Sci.
Eng., 192, 107295 (2020).
38) H. Nakae and H. Katoh, J. Jpn. I. Met. Mater., 63,
1356­1362 (1999).

Liquid-solid co-printing of multi-material 3D fluidic devices via material jetting

재료 분사를 통한 다중 재료 3D 유체 장치의 액체-고체 공동 인쇄

Liquid-solid co-printing of multi-material 3D fluidic devices via material jetting

BrandonHayes,Travis Hainsworth, Robert MacCurdy
University of Colorado Boulder, Department of Mechanical Engineering, Boulder, 80309, CO, USA

Abstract

다중 재료 재료 분사 적층 제조 공정은 3차원(3D) 부품을 레이어별로 구축하기 위해 다양한 모델 및 지지 재료의 미세 액적을 증착합니다.

최근의 노력은 액체가 마이크로/밀리 채널에서 쉽게 퍼지할 수 있는 지지 재료로 작용할 수 있고 구조에 영구적으로 남아 있는 작동 유체로 작용할 수 있음을 보여주었지만 인쇄 프로세스 및 메커니즘에 대한 자세한 이해가 부족합니다.

액체 인쇄의 제한된 광범위한 적용. 이 연구에서 광경화성 및 광경화성 액체 방울이 동시에 증착되는 액체-고체 공동 인쇄라고 하는 “한 번에 모두 가능한” 다중 재료 인쇄 프로세스가 광범위하게 특성화됩니다. 액체-고체 공동 인쇄의 메커니즘은 실험적인 고속 이미징 및 CFD(전산 유체 역학) 연구를 통해 설명됩니다.

이 연구는 액체의 표면 장력이 액체 표면에서 광중합하여 재료의 단단한 층을 형성하는 분사된 광중합체 미세 방울을 지지할 수 있음을 보여줍니다.

마이크로/밀리 유체 소자의 액체-고체 공동 인쇄를 위한 설계 규칙은 믹서, 액적 발생기, 고도로 분기되는 구조 및 통합된 단방향 플랩 밸브와 같은 평면, 3D 및 복합 재료 마이크로/메조 유체 구조에 대한 사례 연구뿐만 아니라 제시됩니다.

우리는 액체-고체 공동 인쇄 과정을 마이크로/메조플루이딕 회로, 전기화학 트랜지스터, 칩 장치 및 로봇을 포함한 응용 프로그램을 사용하여 3D, 통합된 복합 재료 유체 회로 및 유압 구조의 단순하고 빠른 제작을 가능하게 하는 적층 제조의 핵심 새로운 기능으로 구상합니다.

Multi-material material jetting additive manufacturing processes deposit micro-scale droplets of different model and support materials to build three-dimensional (3D) parts layer by layer. Recent efforts have demonstrated that liquids can act as support materials, which can be easily purged from micro/milli-channels, and as working fluids, which permanently remain in a structure, yet the lack of a detailed understanding of the print process and mechanism has limited widespread applications of liquid printing. In this study, an “all in one go” multi-material print process, herein termed liquid–solid co-printing in which non photo-curable and photo-curable liquid droplets are simultaneous deposited, is extensively characterized. The mechanism of liquid–solid co-printing is explained via experimental high speed imaging and computational fluid dynamic (CFD) studies. This work shows that a liquid’s surface tension can support jetted photopolymer micro-droplets which photo-polymerize on the liquid surface to form a solid layer of material. Design rules for liquid–solid co-printing of micro/milli-fluidic devices are presented as well as case studies of planar, 3D, and multi-material micro/mesofluidic structures such as mixers, droplet generators, highly branching structures, and an integrated one-way flap valve. We envision the liquid–solid co-printing process as a key new capability in additive manufacturing to enable simple and rapid fabrication of 3D, integrated print-in-place multi-material fluidic circuits and hydraulic structures with applications including micro/mesofluidic circuits, electrochemical transistors, lab-on-a-chip devices, and robotics.

Liquid-solid co-printing of multi-material 3D fluidic devices via material jetting
Liquid-solid co-printing of multi-material 3D fluidic devices via material jetting

Keywords

Additive manufacturing; Mesofluidics; Modeling and simulation; Multi-material; Material jetting

Fig. 5. The predicted shapes of initial breach (a) Rectangular (b) V-notch. Fig. 6. Dam breaching stages.

Investigating the peak outflow through a spatial embankment dam breach

공간적 제방댐 붕괴를 통한 최대 유출량 조사

Mahmoud T.GhonimMagdy H.MowafyMohamed N.SalemAshrafJatwaryFaculty of Engineering, Zagazig University, Zagazig 44519, Egypt

Abstract

Investigating the breach outflow hydrograph is an essential task to conduct mitigation plans and flood warnings. In the present study, the spatial dam breach is simulated by using a three-dimensional computational fluid dynamics model, FLOW-3D. The model parameters were adjusted by making a comparison with a previous experimental model. The different parameters (initial breach shape, dimensions, location, and dam slopes) are studied to investigate their effects on dam breaching. The results indicate that these parameters have a significant impact. The maximum erosion rate and peak outflow for the rectangular shape are higher than those for the V-notch by 8.85% and 5%, respectively. Increasing breach width or decreasing depth by 5% leads to increasing maximum erosion rate by 11% and 15%, respectively. Increasing the downstream slope angle by 4° leads to an increase in both peak outflow and maximum erosion rate by 2.0% and 6.0%, respectively.

유출 유출 수문곡선을 조사하는 것은 완화 계획 및 홍수 경보를 수행하는 데 필수적인 작업입니다. 본 연구에서는 3차원 전산유체역학 모델인 FLOW-3D를 사용하여 공간 댐 붕괴를 시뮬레이션합니다. 이전 실험 모델과 비교하여 모델 매개변수를 조정했습니다.

다양한 매개변수(초기 붕괴 형태, 치수, 위치 및 댐 경사)가 댐 붕괴에 미치는 영향을 조사하기 위해 연구됩니다. 결과는 이러한 매개변수가 상당한 영향을 미친다는 것을 나타냅니다. 직사각형 형태의 최대 침식율과 최대 유출량은 V-notch보다 각각 8.85%, 5% 높게 나타났습니다.

위반 폭을 늘리거나 깊이를 5% 줄이면 최대 침식률이 각각 11% 및 15% 증가합니다. 하류 경사각을 4° 증가시키면 최대 유출량과 최대 침식률이 각각 2.0% 및 6.0% 증가합니다.

Keywords

Spatial dam breach; FLOW-3D; Overtopping erosion; Computational fluid dynamics (CFD)

1. Introduction

There are many purposes for dam construction, such as protection from flood disasters, water storage, and power generationEmbankment failures may have a catastrophic impact on lives and infrastructure in the downstream regions. One of the most common causes of embankment dam failure is overtopping. Once the overtopping of the dam begins, the breach formation will start in the dam body then end with the dam failure. This failure occurs within a very short time, which threatens to be very dangerous. Therefore, understanding and modeling the embankment breaching processes is essential for conducting mitigation plans, flood warnings, and forecasting flood damage.

The analysis of the dam breaching process is implemented by different techniques: comparative methods, empirical models with dimensional and dimensionless solutions, physical-based models, and parametric models. These models were described in detail [1]Parametric modeling is commonly used to simulate breach growth as a time-dependent linear process and calculate outflow discharge from the breach using hydraulics principles [2]. Alhasan et al. [3] presented a simple one-dimensional mathematical model and a computer code to simulate the dam breaching process. These models were validated by small dams breaching during the floods in 2002 in the Czech Republic. Fread [4] developed an erosion model (BREACH) based on hydraulics principles, sediment transport, and soil mechanics to estimate breach size, time of formation, and outflow discharge. Říha et al. [5] investigated the dam break process for a cascade of small dams using a simple parametric model for piping and overtopping erosion, as well as a 2D shallow-water flow model for the flood in downstream areas. Goodarzi et al. [6] implemented mathematical and statistical methods to assess the effect of inflows and wind speeds on the dam’s overtopping failure.

Dam breaching studies can be divided into two main modes of erosion. The first mode is called “planar dam breach” where the flow overtops the whole dam width. While the second mode is called “spatial dam breach” where the flow overtops through the initial pilot channel (i.e., a channel created in the dam body). Therefore, the erosion will be in both vertical and horizontal directions [7].

The erosion process through the embankment dams occurs due to the shear stress applied by water flows. The dam breaching evolution can be divided into three stages [8][9], but Y. Yang et al. [10] divided the breach development into five stages: Stage I, the seepage erosion; Stage II, the initial breach formation; Stage III, the head erosion; Stage IV, the breach expansion; and Stage V, the re-equilibrium of the river channel through the breach. Many experimental tests have been carried out on non-cohesive embankment dams with an initial breach to examine the effect of upstream inflow discharges on the longitudinal profile evolution and the time to inflection point [11].

Zhang et al. [12] studied the effect of changing downstream slope angle, sediment grain size, and dam crest length on erosion rates. They noticed that increasing dam crest length and decreasing downstream slope angle lead to decreasing sediment transport rate. While the increase in sediment grain size leads to an increased sediment transport rate at the initial stages. Höeg et al. [13] presented a series of field tests to investigate the stability of embankment dams made of various materials. Overtopping and piping were among the failure tests carried out for the dams composed of homogeneous rock-fill, clay, or gravel with a height of up to 6.0 m. Hakimzadeh et al. [14] constructed 40 homogeneous cohesive and non-cohesive embankment dams to study the effect of changing sediment diameter and dam height on the breaching process. They also used genetic programming (GP) to estimate the breach outflow. Refaiy et al. [15] studied different scenarios for the downstream drain geometry, such as length, height, and angle, to minimize the effect of piping phenomena and therefore increase dam safety.

Zhu et al. [16] examined the effect of headcut erosion on dam breach growth, especially in the case of cohesive dams. They found that the breach growth in non-cohesive embankments is slower than cohesive embankments due to the little effect of headcut. Schmocker and Hager [7] proposed a relationship for estimating peak outflow from the dam breach process.(1)QpQin-1=1.7exp-20hc23d5013H0

where: Qp = peak outflow discharge.

Qin = inflow discharge.

hc = critical flow depth.

d50 = mean sediment diameter.

Ho = initial dam height.

Yu et al. [17] carried out an experimental study for homogeneous non-cohesive embankment dams in a 180° bending rectangular flume to determine the effect of overtopping flows on breaching formation. They found that the main factors influencing breach formation are water level, river discharge, and embankment material diameter.

Wu et al. [18] carried out a series of experiments to investigate the effect of breaching geometry on both non-cohesive and cohesive embankment dams in a U-bend flume due to overtopping flows. In the case of non-cohesive embankments, the non-symmetrical lateral expansion was noticed during the breach formation. This expansion was described by a coefficient ranging from 2.7 to 3.3.

The numerical models of the dam breach can be categorized according to different parameters, such as flow dimensions (1D, 2D, or 3D), flow governing equations, and solution methods. The 1D models are mainly used to predict the outflow hydrograph from the dam breach. Saberi et al. [19] applied the 1D Saint-Venant equation, which is solved by the finite difference method to investigate the outflow hydrograph during dam overtopping failure. Because of the ability to study dam profile evolution and breach formation, 2D models are more applicable than 1D models. Guan et al. [20] and Wu et al. [21] employed both 2D shallow water equations (SWEs) and sediment erosion equations, which are solved by the finite volume method to study the effect of the dam’s geometry parameters on outflow hydrograph and dam profile evolution. Wang et al. [22] also proposed a second-order hybrid-type of total variation diminishing (TVD) finite-difference to estimate the breach outflow by solving the 2D (SWEs). The accuracy of (SWEs) for both vertical flow contraction and surface roughness has been assessed [23]. They noted that the accuracy of (SWEs) is acceptable for milder slopes, but in the case of steeper slopes, modelers should be more careful. Generally, the accuracy of 2D models is still low, especially with velocity distribution over the flow depth, lateral momentum exchange, density-driven flows, and bottom friction [24]. Therefore, 3D models are preferred. Larocque et al. [25] and Yang et al. [26] started to use three-dimensional (3D) models that depend on the Reynolds-averaged Navier-Stokes (RANS) equations.

Previous experimental studies concluded that there is no clear relationship between the peak outflow from the dam breach and the initial breach characteristics. Some of these studies depend on the sharp-crested weir fixed at the end of the flume to determine the peak outflow from the breach, which leads to a decrease in the accuracy of outflow calculations at the microscale. The main goals of this study are to carry out a numerical simulation for a spatial dam breach due to overtopping flows by using (FLOW-3D) software to find an empirical equation for the peak outflow discharge from the breach and determine the worst-case that leads to accelerating the dam breaching process.

2. Numerical simulation

The current study for spatial dam breach is simulated by using (FLOW-3D) software [27], which is a powerful computational fluid dynamics (CFD) program.

2.1. Geometric presentations

A stereolithographic (STL) file is prepared for each change in the initial breach geometry and dimensions. The CAD program is useful for creating solid objects and converting them to STL format, as shown in Fig. 1.

2.2. Governing equations

The governing equations for water flow are three-dimensional Reynolds Averaged Navier-Stokes equations (RANS).

The continuity equation:(2)∂ui∂xi=0

The momentum equation:(3)∂ui∂t+1VFuj∂ui∂xj=1ρ∂∂xj-pδij+ν∂ui∂xj+∂uj∂xi-ρu`iu`j¯

where u is time-averaged velocity,ν is kinematic viscosity, VF is fractional volume open to flow, p is averaged pressure and -u`iu`j¯ are components of Reynold’s stress. The Volume of Fluid (VOF) technique is used to simulate the free surface profile. Hirt et al. [28] presented the VOF algorithm, which employs the function (F) to express the occupancy of each grid cell with fluid. The value of (F) varies from zero to unity. Zero value refers to no fluid in the grid cell, while the unity value refers to the grid cell being fully occupied with fluid. The free surface is formed in the grid cells having (F) values between zero and unity.(4)∂F∂t+1VF∂∂xFAxu+∂∂yFAyv+∂∂zFAzw=0

where (u, v, w) are the velocity components in (x, y, z) coordinates, respectively, and (AxAyAz) are the area fractions.

2.3. Boundary and initial conditions

To improve the accuracy of the results, the boundary conditions should be carefully determined. In this study, two mesh blocks are used to minimize the time consumed in the simulation. The boundary conditions for mesh block 1 are as follows: The inlet and sides boundaries are defined as a wall boundary condition (wall boundary condition is usually used for bound fluid by solid regions. In the case of viscous flows, no-slip means that the tangential velocity is equal to the wall velocity and the normal velocity is zero), the outlet is defined as a symmetry boundary condition (symmetry boundary condition is usually used to reduce computational effort during CFD simulation. This condition allows the flow to be transferred from one mesh block to another. No inputs are required for this boundary condition except that its location should be defined accurately), the bottom boundary is defined as a uniform flow rate boundary condition, and the top boundary is defined as a specific pressure boundary condition with assigned atmospheric pressure. The boundary conditions for mesh block 2 are as follows: The inlet is defined as a symmetry boundary condition, the outlet is defined as a free flow boundary condition, the bottom and sides boundaries are defined as a wall boundary condition, and the top boundary is defined as a specific pressure boundary condition with assigned atmospheric pressure as shown in Fig. 2. The initial conditions required to be set for the fluid (i.e., water) inside of the domain include configuration, temperature, velocities, and pressure distribution. The configuration of water depends on the dimensions and shape of the dam reservoir. While the other conditions have been assigned as follows: temperature is normal water temperature (25 °c) and pressure distribution is hydrostatic with no initial velocity.

2.4. Numerical method

FLOW-3D uses the finite volume method (FVM) to solve the governing equation (Reynolds-averaged Navier-Stokes) over the computational domain. A finite-volume method is an Eulerian approach for representing and evaluating partial differential equations in algebraic equations form [29]. At discrete points on the mesh geometry, values are determined. Finite volume expresses a small volume surrounding each node point on a mesh. In this method, the divergence theorem is used to convert volume integrals with a divergence term to surface integrals. After that, these terms are evaluated as fluxes at each finite volume’s surfaces.

2.5. Turbulent models

Turbulence is the chaotic, unstable motion of fluids that occurs when there are insufficient stabilizing viscous forces. In FLOW-3D, there are six turbulence models available: the Prandtl mixing length model, the one-equation turbulent energy model, the two-equation (k – ε) model, the Renormalization-Group (RNG) model, the two-equation (k – ω) models, and a large eddy simulation (LES) model. For simulating flow motion, the RNG model is adopted to simulate the motion behavior better than the k – ε and k – ω.

models [30]. The RNG model consists of two main equations for the turbulent kinetic energy KT and its dissipation.εT(5)∂kT∂t+1VFuAx∂kT∂x+vAy∂kT∂y+wAz∂kT∂z=PT+GT+DiffKT-εT(6)∂εT∂t+1VFuAx∂εT∂x+vAy∂εT∂y+wAz∂εT∂z=C1.εTKTPT+c3.GT+Diffε-c2εT2kT

where KT is the turbulent kinetic energy, PT is the turbulent kinetic energy production, GT is the buoyancy turbulence energy, εT is the turbulent energy dissipation rate, DiffKT and Diffε are terms of diffusion, c1, c2 and c3 are dimensionless parameters, in which c1 and c3 have a constant value of 1.42 and 0.2, respectively, c2 is computed from the turbulent kinetic energy (KT) and turbulent production (PT) terms.

2.6. Sediment scour model

The sediment scour model available in FLOW-3D can calculate all the sediment transport processes including Entrainment transport, Bedload transport, Suspended transport, and Deposition. The erosion process starts once the water flows remove the grains from the packed bed and carry them into suspension. It happens when the applied shear stress by water flows exceeds critical shear stress. This process is represented by entrainment transport in the numerical model. After entrained, the grains carried by water flow are represented by suspended load transport. After that, some suspended grains resort to settling because of the combined effect of gravity, buoyancy, and friction. This process is described through a deposition. Finally, the grains sliding motions are represented by bedload transport in the model. For the entrainment process, the shear stress applied by the fluid motion on the packed bed surface is calculated using the standard wall function as shown in Eq.7.(7)ks,i=Cs,i∗d50

where ks,i is the Nikuradse roughness and Cs,i is a user-defined coefficient. The critical bed shear stress is defined by a dimensionless parameter called the critical shields number as expressed in Eq.8.(8)θcr,i=τcr,i‖g‖diρi-ρf

where θcr,i is the critical shields number, τcr,i is the critical bed shear stress, g is the absolute value of gravity acceleration, di is the diameter of the sediment grain, ρi is the density of the sediment species (i) and ρf is the density of the fluid. The value of the critical shields number is determined according to the Soulsby-Whitehouse equation.(9)θcr,i=0.31+1.2d∗,i+0.0551-exp-0.02d∗,i

where d∗,i is the dimensionless diameter of the sediment, given by Eq.10.(10)d∗,i=diρfρi-ρf‖g‖μf213

where μf is the fluid dynamic viscosity. For the sloping bed interface, the value of the critical shields number is modified according to Eq.11.(11)θ`cr,i=θcr,icosψsinβ+cos2βtan2φi-sin2ψsin2βtanφi

where θ`cr,i is the modified critical shields number, φi is the angle of repose for the sediment, β is the angle of bed slope and ψ is the angle between the flow and the upslope direction. The effects of the rolling, hopping, and sliding motions of grains along the packed bed surface are taken by the bedload transport process. The volumetric bedload transport rate (qb,i) per width of the bed is expressed in Eq.12.(12)qb,i=Φi‖g‖ρi-ρfρfdi312

where Φi is the dimensionless bedload transport rate is calculated by using Meyer Peter and Müller equation.(13)Φi=βMPM,iθi-θ`cr,i1.5cb,i

where βMPM,i is the Meyer Peter and Müller user-defined coefficient and cb,i is the volume fraction of species i in the bed material. The suspended load transport is calculated as shown in Eq.14.(14)∂Cs,i∂t+∇∙Cs,ius,i=∇∙∇DCs,i

where Cs,i is the suspended sediment mass concentration, D is the diffusivity, and us,i is the grain velocity of species i. Entrainment and deposition are two opposing processes that take place at the same time. The lifting and settling velocities for both entrainment and deposition processes are calculated according to Eq.15 and Eq.16, respectively.(15)ulifting,i=αid∗,i0.3θi-θ`cr,igdiρiρf-1(16)usettling,i=υfdi10.362+1.049d∗,i3-10.36

where αi is the entrainment coefficient of species i and υf is the kinematic viscosity of the fluid.

2.7. Grid type

Using simple rectangular orthogonal elements in planes and hexahedral in volumes in the (FLOW-3D) program makes the mesh generation process easier, decreases the required memory, and improves numerical accuracy. Two mesh blocks were used in a joined form with a size ratio of 2:1. The first mesh block is coarser, which contains the reservoir water, and the second mesh block is finer, which contains the dam. For achieving accuracy and efficiency in results, the mesh size is determined by using a grid convergence test. The optimum uniform cell size for the first mesh block is 0.012 m and for the second mesh block is 0.006 m.

2.8. Time step

The maximum time step size is determined by using a Courant number, which controls the distance that the flow will travel during the simulation time step. In this study, the Courant number was taken equal to 0.25 to prevent the flow from traveling through more than one cell in the time step. Based on the Courant number, a maximum time step value of 0.00075 s was determined.

2.9. Numerical model validation

The numerical model accuracy was achieved by comparing the numerical model results with previous experimental results. The experimental study of Schmocker and Hager [7] was based on 31 tests with changes in six parameters (d50, Ho, Bo, Lk, XD, and Qin). All experimental tests were conducted in a straight open glass-sided flume. The horizontal flume has a rectangular cross-section with a width of 0.4 m and a height of 0.7 m. The flume was provided with a flow straightener and an intake with a length of 0.66 m. All tested dams were inserted at various distances (XD) from the intake. Test No.1 from this experimental program was chosen to validate the numerical model. The different parameters used in test No.1 are as follows:

(1) uniform sediment with a mean diameter (d50 = 0.31 mm), (2) Ho = 0.2 m, (3) Bo = 0.2 m, (4) Lk = 0.1 m,

(5) XD = 1.0 m, (6) Qin = 6.0 lit/s, (7) Su and Sd = 2:1, (8) mass density (ρs = 2650 kg/m3(9) Homogenous and non-cohesive embankment dam. As shown in Fig. 2, the simulation is contained within a rectangular grid with dimensions: 3.56 m in the x-direction (where 0.66 m is used as inlet, 0.9 m as dam base width, and 1.0 m as outlet), in y-direction 0.2 m (dam length), and in the z-direction 0.3 m, which represents the dam height (0.2 m) with a free distance (0.1 m) above the dam. There are two main reasons that this experimental program is preferred for the validation process. The first reason is that this program deals with homogenous, non-cohesive soil, which is available in FLOW-3D. The second reason is that this program deals with small-scale models which saves time for numerical simulation. Finally, some important assumptions were considered during the validation process. The flow is assumed to be incompressible, viscous, turbulent, and three-dimensional.

By comparing dam profiles at different time instants for the experimental test with the current numerical model, it appears that the numerical model gives good agreement as shown in Fig. 3 and Fig. 4, with an average error percentage of 9% between the experimental results and the numerical model.

3. Analysis and discussions

The current model is used to study the effects of different parameters such as (initial breach shapes, dimensions, locations, upstream and downstream dam slopes) on the peak outflow discharge, QP, time of peak outflow, tP, and rate of erosion, E.

This study consists of a group of scenarios. The first scenario is changing the shapes of the initial breach according to Singh [1], the most predicted shapes are rectangular and V-notch as shown in Fig. 5. The second scenario is changing the initial breach dimensions (i.e., width and depth). While the third scenario is changing the location of the initial breach. Eventually, the last scenario is changing the upstream and downstream dam slopes.

All scenarios of this study were carried out under the same conditions such as inflow discharge value (Qin=1.0lit/s), dimensions of the tested dam, where dam height (Ho=0.20m), crest width.

(Lk=0.1m), dam length (Bo=0.20m), and homogenous & non-cohesive soil with a mean diameter (d50=0.31mm).

3.1. Dam breaching process evolution

The dam breaching process is a very complex process due to the quick changes in hydrodynamic conditions during dam failure. The dam breaching process starts once water flows reach the downstream face of the dam. During the initial stage of dam breaching, the erosion process is relatively quiet due to low velocities of flow. As water flows continuously, erosion rates increase, especially in two main zones: the crest and the downstream face. As soon as the dam crest is totally eroded, the water levels in the dam reservoir decrease rapidly, accompanied by excessive erosion in the dam body. The erosion process continues until the water levels in the dam reservoir equal the remaining height of the dam.

According to Zhou et al. [11], the breaching process consists of three main stages. The first stage starts with beginning overtopping flow, then ends when the erosion point directed upstream and reached the inflection point at the inflection time (ti). The second stage starts from the end of the stage1 until the occurrence of peak outflow discharge at the peak outflow time (tP). The third stage starts from the end of the stage2 until the value of outflow discharge becomes the same as the value of inflow discharge at the final time (tf). The outflow discharge from the dam breach increases rapidly during stage1 and stage2 because of the large dam storage capacity (i.e., the dam reservoir is totally full of water) and excessive erosion. While at stage3, the outflow values start to decrease slowly because most of the dam’s storage capacity was run out. The end of stage3 indicates that the dam storage capacity was totally run out, so the outflow equalized with the inflow discharge as shown in Fig. 6 and Fig. 7.

3.2. The effect of initial breach shape

To identify the effect of the initial breach shape on the evolution of the dam breaching process. Three tests were carried out with different cross-section areas for each shape. The initial breach is created at the center of the dam crest. Each test had an ID to make the process of arranging data easier. The rectangular shape had an ID (Rec5h & 5b), which means that its depth and width are equal to 5% of the dam height, and the V-notch shape had an ID (V-noch5h & 1:1) which means that its depth is equal to 5% of the dam height and its side slope is equal to 1:1. The comparison between rectangular and V-notch shapes is done by calculating the ratio between maximum dam height at different times (ZMax) to the initial dam height (Ho), rate of erosion, and hydrograph of outflow discharge for each test. The rectangular shape achieves maximum erosion rate and minimum inflection time, in addition to a rapid decrease in the dam reservoir levels. Therefore, the dam breaching is faster in the case of a rectangular shape than in a V-notch shape, which has the same cross-section area as shown in Fig. 8.

Also, by comparing the hydrograph for each test, the peak outflow discharge value in the case of a rectangular shape is higher than the V-notch shape by 5% and the time of peak outflow for the rectangular shape is shorter than the V-notch shape by 9% as shown in Fig. 9.

3.3. The effect of initial breach dimensions

The results of the comparison between the different initial breach shapes indicate that the worst initial breach shape is rectangular, so the second scenario from this study concentrated on studying the effect of a change in the initial rectangular breach dimensions. Groups of tests were carried out with different depths and widths for the rectangular initial breach. The first group had a depth of 5% from the dam height and with three different widths of 5,10, and 15% from the dam height, the second group had a depth of 10% with three different widths of 5,10, and 15%, the third group had a depth of 15% with three different widths of 5,10, and 15% and the final group had a width of 15% with three different heights of 5, 10, and 15% for a rectangular breach shape. The comparison was made as in the previous section to determine the worst case that leads to the quick dam failure as shown in Fig. 10.

The results show that the (Rec 5 h&15b) test achieves a maximum erosion rate for a shorter period of time and a minimum ratio for (Zmax / Ho) as shown in Fig. 10, which leads to accelerating the dam failure process. The dam breaching process is faster with the minimum initial breach depth and maximum initial breach width. In the case of a minimum initial breach depth, the retained head of water in the dam reservoir is high and the crest width at the bottom of the initial breach (L`K) is small, so the erosion point reaches the inflection point rapidly. While in the case of the maximum initial breach width, the erosion perimeter is large.

3.4. The effect of initial breach location

The results of the comparison between the different initial rectangular breach dimensions indicate that the worst initial breach dimension is (Rec 5 h&15b), so the third scenario from this study concentrated on studying the effect of a change in the initial breach location. Three locations were checked to determine the worst case for the dam failure process. The first location is at the center of the dam crest, which was named “Center”, the second location is at mid-distance between the dam center and dam edge, which was named “Mid”, and the third location is at the dam edge, which was named “Edge” as shown in Fig. 11. According to this scenario, the results indicate that the time of peak outflow discharge (tP) is the same in the three cases, but the maximum value of the peak outflow discharge occurs at the center location. The difference in the peak outflow values between the three cases is relatively small as shown in Fig. 12.

The rates of erosion were also studied for the three cases. The results show that the maximum erosion rate occurs at the center location as shown in Fig. 13. By making a comparison between the three cases for the dam storage volume. The results show that the center location had the minimum values for the dam storage volume, which means that a large amount of water has passed to the downstream area as shown in Fig. 14. According to these results, the center location leads to increased erosion rate and accelerated dam failure process compared with the two other cases. Because the erosion occurs on both sides, but in the case of edge location, the erosion occurs on one side.

3.5. The effect of upstream and downstream dam slopes

The results of the comparison between the different initial rectangular breach locations indicate that the worst initial breach location is the center location, so the fourth scenario from this study concentrated on studying the effect of a change in the upstream (Su) and downstream (Sd) dam slopes. Three slopes were checked individually for both upstream and downstream slopes to determine the worst case for the dam failure process. The first slope value is (2H:1V), the second slope value is (2.5H:1V), and the third slope value is (3H:1V). According to this scenario, the results show that the decreasing downstream slope angle leads to increasing time of peak outflow discharge (tP) and decreasing value of peak outflow discharge. The difference in the peak outflow values between the three cases for the downstream slope is 2%, as shown in Fig. 15, but changing the upstream slope has a negligible impact on the peak outflow discharge and its time as shown in Fig. 16.

The rates of erosion were also studied in the three cases for both upstream and downstream slopes. The results show that the maximum erosion rate increases by 6.0% with an increasing downstream slope angle by 4°, as shown in Fig. 17. The results also indicate that the erosion rates aren’t affected by increasing or decreasing the upstream slope angle, as shown in Fig. 18. According to these results, increasing the downstream slope angle leads to increased erosion rate and accelerated dam failure process compared with the upstream slope angle. Because of increasing shear stress applied by water flows in case of increasing downstream slope.

According to all previous scenarios, the dimensionless peak outflow discharge QPQin is presented for a fixed dam height (Ho) and inflow discharge (Qin). Fig. 19 illustrates the relationship between QP∗=QPQin and.

Lr=ho2/3∗bo2/3Ho. The deduced relationship achieves R2=0.96.(17)QP∗=2.2807exp-2.804∗Lr

4. Conclusions

A spatial dam breaching process was simulated by using FLOW-3D Software. The validation process was performed by making a comparison between the simulated results of dam profiles and the dam profiles obtained by Schmocker and Hager [7] in their experimental study. And also, the peak outflow value recorded an error percentage of 12% between the numerical model and the experimental study. This model was used to study the effect of initial breach shape, dimensions, location, and dam slopes on peak outflow discharge, time of peak outflow, and the erosion process. By using the parameters obtained from the validation process, the results of this study can be summarized in eight points as follows.1.

The rectangular initial breach shape leads to an accelerating dam failure process compared with the V-notch.2.

The value of peak outflow discharge in the case of a rectangular initial breach is higher than the V-notch shape by 5%.3.

The time of peak outflow discharge for a rectangular initial breach is shorter than the V-notch shape by 9%.4.

The minimum depth and maximum width for the initial breach achieve maximum erosion rates (increasing breach width, b0, or decreasing breach depth, h0, by 5% from the dam height leads to an increase in the maximum rate of erosion by 11% and 15%, respectively), so the dam failure is rapid.5.

The center location of the initial breach leads to an accelerating dam failure compared with the edge location.6.

The initial breach location has a negligible effect on the peak outflow discharge value and its time.7.

Increasing the downstream slope angle by 4° leads to an increase in both peak outflow discharge and maximum rate of erosion by 2.0% and 6.0%, respectively.8.

The upstream slope has a negligible effect on the dam breaching process.

References

[1]V. SinghDam breach modeling technologySpringer Science & Business Media (1996)Google Scholar[2]Wahl TL. Prediction of embankment dam breach parameters: a literature review and needs assessment. 1998.Google Scholar[3]Z. Alhasan, J. Jandora, J. ŘíhaStudy of dam-break due to overtopping of four small dams in the Czech RepublicActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63 (3) (2015), pp. 717-729 View PDFCrossRefView Record in ScopusGoogle Scholar[4]D. FreadBREACH, an erosion model for earthen dam failures: Hydrologic Research LaboratoryNOAA, National Weather Service (1988)Google Scholar[5]J. Říha, S. Kotaška, L. PetrulaDam Break Modeling in a Cascade of Small Earthen Dams: Case Study of the Čižina River in the Czech RepublicWater, 12 (8) (2020), p. 2309, 10.3390/w12082309 View PDFView Record in ScopusGoogle Scholar[6]E. Goodarzi, L. Teang Shui, M. ZiaeiDam overtopping risk using probabilistic concepts–Case study: The Meijaran DamIran Ain Shams Eng J, 4 (2) (2013), pp. 185-197ArticleDownload PDFView Record in ScopusGoogle Scholar[7]L. Schmocker, W.H. HagerPlane dike-breach due to overtopping: effects of sediment, dike height and dischargeJ Hydraul Res, 50 (6) (2012), pp. 576-586 View PDFCrossRefView Record in ScopusGoogle Scholar[8]J.S. Walder, R.M. Iverson, J.W. Godt, M. Logan, S.A. SolovitzControls on the breach geometry and flood hydrograph during overtopping of noncohesive earthen damsWater Resour Res, 51 (8) (2015), pp. 6701-6724View Record in ScopusGoogle Scholar[9]H. Wei, M. Yu, D. Wang, Y. LiOvertopping breaching of river levees constructed with cohesive sedimentsNat Hazards Earth Syst Sci, 16 (7) (2016), pp. 1541-1551 View PDFCrossRefView Record in ScopusGoogle Scholar[10]Y. Yang, S.-Y. Cao, K.-J. Yang, W.-P. LiYang K-j, Li W-p. Experimental study of breach process of landslide dams by overtopping and its initiation mechanismsJ Hydrodynamics, 27 (6) (2015), pp. 872-883ArticleDownload PDFCrossRefView Record in ScopusGoogle Scholar[11]G.G.D. Zhou, M. Zhou, M.S. Shrestha, D. Song, C.E. Choi, K.F.E. Cui, et al.Experimental investigation on the longitudinal evolution of landslide dam breaching and outburst floodsGeomorphology, 334 (2019), pp. 29-43ArticleDownload PDFView Record in ScopusGoogle Scholar[12]J. Zhang, Z.-x. Guo, S.-y. CaoYang F-g. Experimental study on scour and erosion of blocked damWater Sci Eng, 5 (2012), pp. 219-229ArticleDownload PDFView Record in ScopusGoogle Scholar[13]K. Höeg, A. Løvoll, K. VaskinnStability and breaching of embankment dams: Field tests on 6 m high damsInt J Hydropower Dams, 11 (2004), pp. 88-92View Record in ScopusGoogle Scholar[14]H. Hakimzadeh, V. Nourani, A.B. AminiGenetic programming simulation of dam breach hydrograph and peak outflow dischargeJ Hydrol Eng, 19 (4) (2014), pp. 757-768View Record in ScopusGoogle Scholar[15]A.R. Refaiy, N.M. AboulAtta, N.Y. Saad, D.A. El-MollaModeling the effect of downstream drain geometry on seepage through earth damsAin Shams Eng J, 12 (3) (2021), pp. 2511-2531ArticleDownload PDFView Record in ScopusGoogle Scholar[16]Y. Zhu, P.J. Visser, J.K. Vrijling, G. WangExperimental investigation on breaching of embankmentsScience China Technological Sci, 54 (1) (2011), pp. 148-155 View PDFCrossRefView Record in ScopusGoogle Scholar[17]M.-H. Yu, H.-Y. Wei, Y.-J. Liang, Y. ZhaoInvestigation of non-cohesive levee breach by overtopping flowJ Hydrodyn, 25 (4) (2013), pp. 572-579ArticleDownload PDFCrossRefView Record in ScopusGoogle Scholar[18]S. Wu, M. Yu, H. Wei, Y. Liang, J. ZengNon-symmetrical levee breaching processes in a channel bend due to overtoppingInt J Sedim Res, 33 (2) (2018), pp. 208-215ArticleDownload PDFView Record in ScopusGoogle Scholar[19]O. Saberi, G. ZenzNumerical investigation on 1D and 2D embankment dams failure due to overtopping flowInt J Hydraulic Engineering, 5 (2016), pp. 9-18View Record in ScopusGoogle Scholar[20]M. Guan, N.G. Wright, P.A. Sleigh2D Process-Based Morphodynamic Model for Flooding by Noncohesive Dyke BreachJ Hydraul Eng, 140 (7) (2014), p. 04014022, 10.1061/(ASCE)HY.1943-7900.0000861 View PDFView Record in ScopusGoogle Scholar[21]W. Wu, R. Marsooli, Z. HeDepth-Averaged Two-Dimensional Model of Unsteady Flow and Sediment Transport due to Noncohesive Embankment Break/BreachingJ Hydraul Eng, 138 (6) (2012), pp. 503-516View Record in ScopusGoogle Scholar[22]Z. Wang, D.S. BowlesThree-dimensional non-cohesive earthen dam breach model. Part 1: Theory and methodologyAdv Water Resour, 29 (10) (2006), pp. 1528-1545ArticleDownload PDFView Record in ScopusGoogle Scholar[23]Říha J, Duchan D, Zachoval Z, Erpicum S, Archambeau P, Pirotton M, et al. Performance of a shallow-water model for simulating flow over trapezoidal broad-crested weirs. J Hydrology Hydromechanics. 2019;67:322-8.Google Scholar[24]C.B. VreugdenhilNumerical methods for shallow-water flowSpringer Science & Business Media (1994)Google Scholar[25]L.A. Larocque, J. Imran, M.H. Chaudhry3D numerical simulation of partial breach dam-break flow using the LES and k–∊ turbulence modelsJ Hydraul Res, 51 (2) (2013), pp. 145-157 View PDFCrossRefView Record in ScopusGoogle Scholar[26]C. Yang, B. Lin, C. Jiang, Y. LiuPredicting near-field dam-break flow and impact force using a 3D modelJ Hydraul Res, 48 (6) (2010), pp. 784-792 View PDFCrossRefView Record in ScopusGoogle Scholar[27]FLOW-3D. Version 11.1.1 Flow Science, Inc., Santa Fe, NM. https://wwwflow3dcom.Google Scholar[28]C.W. Hirt, B.D. NicholsVolume of fluid (VOF) method for the dynamics of free boundariesJ Comput Phys, 39 (1) (1981), pp. 201-225ArticleDownload PDFGoogle Scholar[29]S.V. PatankarNumerical heat transfer and fluid flow, Hemisphere PublCorp, New York, 58 (1980), p. 288View Record in ScopusGoogle Scholar[30]M. Alemi, R. MaiaNumerical simulation of the flow and local scour process around single and complex bridge piersInt J Civil Eng, 16 (5) (2018), pp. 475-487 View PDFCrossRefView Record in ScopusGoogle Scholar

Figure 3: 3D temperature contours and 2D melt pool cross-sections where the melt pool is stabilized at x=500 µm from the start of the laser initial location for cases where (a) absorptivity = 0.1, Recoil pressure coefficient B = 1 and laser beam radius = 12 µm, (b) absorptivity = 0.1, Recoil pressure coefficient B = 20 and laser beam radius = 12 µm, (c) absorptivity = 0.1, Recoil pressure coefficient B = 1 and laser beam radius = 18 µm, (d) absorptivity = 0.45, Recoil pressure coefficient B = 1 and laser beam radius = 18 µm, (e) absorptivity = 0.45, Recoil pressure coefficient B = 20 and laser beam radius = 12 µm, (f) absorptivity = 0.45, Recoil pressure coefficient B = 20 and laser beam radius = 18 µm.

MULTI-PHYSICS NUMERICAL MODELLING OF 316L AUSTENITIC STAINLESS STEEL IN LASER POWDER BED FUSION PROCESS AT MESO-SCALE

W.E. Alphonso1, M.Bayat1,*, M. Baier 2, S. Carmignato2, J.H. Hattel1
1Department of Mechanical Engineering, Technical University of Denmark (DTU), Lyngby, Denmark
2Department of Management and Engineering – University of Padova, Padova, Italy

ABSTRACT

L-PBF(Laser Powder Bed Fusion)는 레이저 열원을 사용하여 선택적으로 통합되는 분말 층으로 복잡한 3D 금속 부품을 만드는 금속 적층 제조(MAM) 기술입니다. 처리 영역은 수십 마이크로미터 정도이므로 L-PBF를 다중 규모 제조 공정으로 만듭니다.

기체 기공의 형성 및 성장 및 용융되지 않은 분말 영역의 생성은 다중물리 모델에 의해 예측할 수 있습니다. 또한 이러한 모델을 사용하여 용융 풀 모양 및 크기, 온도 분포, 용융 풀 유체 흐름 및 입자 크기 및 형태와 같은 미세 구조 특성을 계산할 수 있습니다.

이 작업에서는 용융, 응고, 유체 흐름, 표면 장력, 열 모세관, 증발 및 광선 추적을 통한 다중 반사를 포함하는 스테인리스 스틸 316-L에 대한 충실도 다중 물리학 중간 규모 수치 모델이 개발되었습니다. 완전한 실험 설계(DoE) 방법을 사용하는 통계 연구가 수행되었으며, 여기서 불확실한 재료 특성 및 공정 매개변수, 즉 흡수율, 반동 압력(기화) 및 레이저 빔 크기가 용융수지 모양 및 크기에 미치는 영향을 분석했습니다.

또한 용융 풀 역학에 대한 위에서 언급한 불확실한 입력 매개변수의 중요성을 강조하기 위해 흡수율이 가장 큰 영향을 미치고 레이저 빔 크기가 그 뒤를 잇는 주요 효과 플롯이 생성되었습니다. 용융 풀 크기에 대한 반동 압력의 중요성은 흡수율에 따라 달라지는 용융 풀 부피와 함께 증가합니다.

모델의 예측 정확도는 유사한 공정 매개변수로 생성된 단일 트랙 실험과 시뮬레이션의 용융 풀 모양 및 크기를 비교하여 검증됩니다.

더욱이, 열 렌즈 효과는 레이저 빔 크기를 증가시켜 수치 모델에서 고려되었으며 나중에 결과적인 용융 풀 프로파일은 모델의 견고성을 보여주기 위한 실험과 비교되었습니다.

Laser Powder Bed Fusion (L-PBF) is a Metal Additive Manufacturing (MAM) technology where a complex 3D metal part is built from powder layers, which are selectively consolidated using a laser heat source. The processing zone is in the order of a few tenths of micrometer, making L-PBF a multi-scale manufacturing process. The formation and growth of gas pores and the creation of un-melted powder zones can be predicted by multiphysics models. Also, with these models, the melt pool shape and size, temperature distribution, melt pool fluid flow and its microstructural features like grain size and morphology can be calculated. In this work, a high fidelity multi-physics meso-scale numerical model is developed for stainless steel 316-L which includes melting, solidification, fluid flow, surface tension, thermo-capillarity, evaporation and multiple reflection with ray-tracing. A statistical study using a full Design of Experiments (DoE) method was conducted, wherein the impact of uncertain material properties and process parameters namely absorptivity, recoil pressure (vaporization) and laser beam size on the melt pool shape and size was analysed. Furthermore, to emphasize on the significance of the above mentioned uncertain input parameters on the melt pool dynamics, a main effects plot was created which showed that absorptivity had the highest impact followed by laser beam size. The significance of recoil pressure on the melt pool size increases with melt pool volume which is dependent on absorptivity. The prediction accuracy of the model is validated by comparing the melt pool shape and size from the simulation with single track experiments that were produced with similar process parameters. Moreover, the effect of thermal lensing was considered in the numerical model by increasing the laser beam size and later on the resultant melt pool profile was compared with experiments to show the robustness of the model.

Figure 1: a) Computational domain for single track L-PBF which includes a 200 μm thick substrate and 45 μm powder layer thickness b) 3D temperature contour plot after scanning a single track with melt pool contours at two locations along the scanning direction where the green region indicates the melted regions.
Figure 1: a) Computational domain for single track L-PBF which includes a 200 μm thick substrate and 45 μm powder layer thickness b) 3D temperature contour plot after scanning a single track with melt pool contours at two locations along the scanning direction where the green region indicates the melted regions.
Figure 2: Main effects plot of uncertain parameters: absorptivity, recoil pressure coefficient and laser beam radius on the melt pool dimensions (width and depth)
Figure 2: Main effects plot of uncertain parameters: absorptivity, recoil pressure coefficient and laser beam radius on the melt pool dimensions (width and depth)
Figure 3: 3D temperature contours and 2D melt pool cross-sections where the melt pool is stabilized at x=500 µm from the start of the laser initial location for cases where (a) absorptivity = 0.1, Recoil pressure coefficient B = 1 and laser beam radius = 12 µm, (b) absorptivity = 0.1, Recoil pressure coefficient B = 20 and laser beam radius = 12 µm, (c) absorptivity = 0.1, Recoil pressure coefficient B = 1 and laser beam radius = 18 µm, (d) absorptivity = 0.45, Recoil pressure coefficient B = 1 and laser beam radius = 18 µm, (e) absorptivity = 0.45, Recoil pressure coefficient B = 20 and laser beam radius = 12 µm, (f) absorptivity = 0.45, Recoil pressure coefficient B = 20 and laser beam radius = 18 µm.
Figure 3: 3D temperature contours and 2D melt pool cross-sections where the melt pool is stabilized at x=500 µm from the start of the laser initial location for cases where (a) absorptivity = 0.1, Recoil pressure coefficient B = 1 and laser beam radius = 12 µm, (b) absorptivity = 0.1, Recoil pressure coefficient B = 20 and laser beam radius = 12 µm, (c) absorptivity = 0.1, Recoil pressure coefficient B = 1 and laser beam radius = 18 µm, (d) absorptivity = 0.45, Recoil pressure coefficient B = 1 and laser beam radius = 18 µm, (e) absorptivity = 0.45, Recoil pressure coefficient B = 20 and laser beam radius = 12 µm, (f) absorptivity = 0.45, Recoil pressure coefficient B = 20 and laser beam radius = 18 µm.
Figure 4: Validation of Numerical model with Recoil pressure coefficient B= 20, absorptivity = 0.45 and a) laser beam radius = 15 µm b) laser beam radius = 20 µm
Figure 4: Validation of Numerical model with Recoil pressure coefficient B= 20, absorptivity = 0.45 and a) laser beam radius = 15 µm b) laser beam radius = 20 µm

CONCLUSION

In this work, a high-fidelity multi-physics numerical model was developed for L-PBF using the FVM method in Flow-3D. The impact of uncertainty in the input parameters including absorptivity, recoil pressure and laser beam size on the melt pool is addressed using a DoE method. The DoE analysis shows that absorptivity has the highest impact on the melt pool. The recoil pressure and laser beam size only become significant once absorptivity is 0.45. Furthermore, the numerical model is validated by comparing the predicted melt pool shape and size with experiments conducted with similar process parameters wherein a high prediction accuracy is achieved by the model. In addition, the impact of thermal lensing on the melt pool dimensions by increasing the laser beam spot size is considered in the validated numerical model and the resultant melt pool is compared with experiments.

REFERENCES

[1] T. Bonhoff, M. Schniedenharn, J. Stollenwerk, P. Loosen, Experimental and theoretical analysis of thermooptical effects in protective window for selective laser melting, Proc. Int. Conf. Lasers Manuf. LiM. (2017)
26–29. https://www.wlt.de/lim/Proceedings2017/Data/PDF/Contribution31_final.pdf.
[2] L.R. Goossens, Y. Kinds, J.P. Kruth, B. van Hooreweder, On the influence of thermal lensing during selective
laser melting, Solid Free. Fabr. 2018 Proc. 29th Annu. Int. Solid Free. Fabr. Symp. – An Addit. Manuf. Conf.
SFF 2018. (2020) 2267–2274.
[3] J. Shinjo, C. Panwisawas, Digital materials design by thermal-fluid science for multi-metal additive
manufacturing, Acta Mater. 210 (2021) 116825. https://doi.org/10.1016/j.actamat.2021.116825.
[4] Z. Zhang, Y. Huang, A. Rani Kasinathan, S. Imani Shahabad, U. Ali, Y. Mahmoodkhani, E. Toyserkani, 3-
Dimensional heat transfer modeling for laser powder-bed fusion additive manufacturing with volumetric heat
sources based on varied thermal conductivity and absorptivity, Opt. Laser Technol. 109 (2019) 297–312.
https://doi.org/10.1016/j.optlastec.2018.08.012.
[5] M. Bayat, A. Thanki, S. Mohanty, A. Witvrouw, S. Yang, J. Thorborg, N.S. Tiedje, J.H. Hattel, Keyholeinduced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and
experimental validation, Addit. Manuf. 30 (2019) 100835. https://doi.org/10.1016/j.addma.2019.100835.
[6] M. Bayat, S. Mohanty, J.H. Hattel, Multiphysics modelling of lack-of-fusion voids formation and evolution
in IN718 made by multi-track/multi-layer L-PBF, Int. J. Heat Mass Transf. 139 (2019) 95–114.
https://doi.org/10.1016/j.ijheatmasstransfer.2019.05.003.
[7] J. Metelkova, Y. Kinds, K. Kempen, C. de Formanoir, A. Witvrouw, B. Van Hooreweder, On the influence
of laser defocusing in Selective Laser Melting of 316L, Addit. Manuf. 23 (2018) 161–169.
https://doi.org/10.1016/j.addma.2018.08.006.

Figure 2: Temperature contours and melt pool border lines at different times for the 50 % duty cycle case: (a) - (c) Δtcycle = 400 μs, (d) – (f) Δtcycle = 1000 μs and (g) – (i) Δtcycle = 3000 μs.

MULTIPHYSICS SIMULATION OF THEMRAL AND FLUID DYNAMICS PHENOMENA DURING THE PULSED LASER POWDER BED FUSION PROCESS OF 316-L STEEL

M. Bayat* , V. K. Nadimpalli, J. H. Hattel
1Department of Mechanical Engineering, Technical University of Denmark (DTU), Produktionstorvet
425, Kgs. 2800, Lyngby, Denmark

ABSTRACT

L-PBF(Laser Powder Bed Fusion)는 다양한 산업 분야에서 많은 관심을 받았으며, 주로 기존 제조 기술을 사용하여 만들 수 없었던 복잡한 토폴로지 최적화 구성 요소를 구현하는 잘 알려진 능력 덕분입니다. . 펄스 L-PBF(PL-PBF)에서 레이저의 시간적 프로파일은 주기 지속 시간과 듀티 주기 중 하나 또는 둘 다를 수정하여 변조할 수 있습니다. 따라서 레이저의 시간적 프로파일은 향후 적용을 위해 이 프로세스를 더 잘 제어할 수 있는 길을 열어주는 새로운 프로세스 매개변수로 간주될 수 있습니다. 따라서 이 작업에서 우리는 레이저의 시간적 프로파일을 변경하는 것이 PL-PBF 공정에서 용융 풀 조건과 트랙의 최종 모양 및 형상에 어떻게 영향을 미칠 수 있는지 조사하는 것을 목표로 합니다. 이와 관련하여 본 논문에서는 CFD(Computational Fluid Dynamics) 소프트웨어 패키지인 Flow-3D를 기반으로 하는 316-L 스테인리스강 PL-PBF 공정의 다중물리 수치 모델을 개발하고 이 모델을 사용하여 열과 유체를 시뮬레이션합니다. 다양한 펄스 모드에서 공정 과정 중 용융 풀 내부에서 발생하는 유동 조건. 따라서 고정된 레이저 듀티 사이클(50%)이 있는 레이저 주기 지속 시간이 용융 풀의 모양과 크기 및 최종 트랙 형태에 미치는 영향을 연구하기 위해 매개변수 연구가 수행됩니다. 더 긴 주기 기간에서 더 많은 재료가 더 큰 용융 풀 내에서 변위됨에 따라 용융 풀의 후류에 더 눈에 띄는 혹이 형성되며, 동시에 더 심각한 반동 압력을 받습니다. 또한 시뮬레이션에서 50% 듀티 사이클에서 1000μs에서 형성된 보다 대칭적인 용융 풀과 비교하여 400μs 사이클 주기에서 더 긴 용융 풀이 형성된다는 것이 관찰되었습니다. 풀 볼륨은 1000μs의 경우 더 큽니다. 매개변수 연구는 연속 트랙과 파손된 트랙 PL-PBF 사이의 경계를 설명하며, 여기서 연속 트랙은 항상 소량의 용융 재료를 유지함으로써 유지됩니다.

English Abstract

Laser Powder Bed Fusion (L-PBF) has attracted a lot of attention from various industrial sectors and mainly thanks to its well-proven well-known capacity of realizing complex topology-optimized components that have so far been impossible to make using conventional manufacturing techniques. In Pulsed L-PBF (PL-PBF), the laser’s temporal profile can be modulated via modifying either or both the cycle duration and the duty cycle. Thus, the laser’s temporal profile could be considered as a new process parameter that paves the way for a better control of this process for future applications. Therefore, in this work we aim to investigate how changing the laser’s temporal profile can affect the melt pool conditions and the final shape and geometry of a track in the PL-PBF process. In this respect, in this paper a multiphysics numerical model of the PL-PBF process of 316-L stainless steel is developed based on the computational fluid dynamics (CFD) software package Flow-3D and the model is used to simulate the heat and fluid flow conditions occurring inside the melt pool during the course of the process at different pulsing modes. Thus, a parametric study is carried out to study the influence of the laser’s cycle duration with a fixed laser duty cycle (50 %) on the shape and size of the melt pool and the final track morphology. It is noticed that at longer cycle periods, more noticeable humps form at the wake of the melt pool as more material is displaced within bigger melt pools, which are at the same time subjected to more significant recoil pressures. It is also observed in the simulations that at 50 % duty cycle, longer melt pools form at 400 μs cycle period compared to the more symmetrical melt pools formed at 1000 μs, primarily because of shorter laser off-times in the former, even though melt pool volume is bigger for the 1000 μs case. The parameteric study illustrates the boundary between a continuous track and a broken track PL-PBF wherein the continuous track is retained by always maintaining a small volume of molten material.

Figure 1: Front and side views of the computational domain. Note that the region along z and from -100 μm to +50 μm is void.
Figure 1: Front and side views of the computational domain. Note that the region along z and from -100 μm to +50 μm is void.
Figure 2: Temperature contours and melt pool border lines at different times for the 50 % duty cycle case: (a) - (c) Δtcycle = 400 μs, (d) – (f) Δtcycle = 1000 μs and (g) – (i) Δtcycle = 3000 μs.
Figure 2: Temperature contours and melt pool border lines at different times for the 50 % duty cycle case: (a) – (c) Δtcycle = 400 μs, (d) – (f) Δtcycle = 1000 μs and (g) – (i) Δtcycle = 3000 μs.
Figure 3: Plot of melt pool volume versus time for four cases including continuous wave laser as well as 50 % duty cycle at 400 μs, 1000 μs and 3000 μs.
Figure 3: Plot of melt pool volume versus time for four cases including continuous wave laser as well as 50 % duty cycle at 400 μs, 1000 μs and 3000 μs.

CONCLUSIONS

In this work a CFD model of the modulated PL-PBF process of stainless steel 316-L is developed in the commercial software package Flow-3D. The model involves physics such as solidification, melting, evaporation, convection, laser-material interaction, capillarity, Marangoni effect and the recoil pressure effect. In the current study, a parametric study is carried out to understand how the change in the cycle period duration affects the melt pool’s thermo-fluid conditions during the modulated PL-PBF process. It is observed that at the pulse mode with 50 % duty cycle and 400 μs cycle period, an overlapped chain of humps form at the wake of the melt pool and at a spatial frequency of occurrence of about 78 μm. Furthermore and as expected, it is noted that the melt pool volume, the size of the hump as well as the crater size at the end of the track, increase with increase in the cycle period duration, as more material is re-deposited at the back of the melt pool and that itself is caused by more pronounced recoil pressures. Moreover, it is noticed that due to the short off-time period of the laser in the 400 μs cycle period case, there is always an amount of liquid metal left from the previous cycle, at the time the new cycle starts. This is found to be the main reason why longer and elongated melt pools form at 400 μs cycle period, compared to the bigger, shorter and more symmetrical-like melt pools forming at the 1000 μs case. In this study PL-PBF single tracks including the broken track and the continuous track examples were studied to illustrate the boundary of this transition at a given laser scan parameter setting. At higher scan speeds, it is expected that the Plateau–Rayleigh instability will compete with the pulsing behavior to change the transition boundary between a broken and continuous track, which is suggested as future work from this study.

REFERENCES

[1] T. Craeghs, L. Thijs, F. Verhaeghe, J.-P. Kruth, J. Van Humbeeck, A study of the microstructural
evolution during selective laser melting of Ti–6Al–4V, Acta Mater. 58 (2010) 3303–3312.
https://doi.org/10.1016/j.actamat.2010.02.004.
[2] J. Liu, A.T. Gaynor, S. Chen, Z. Kang, K. Suresh, A. Takezawa, L. Li, J. Kato, J. Tang, C.C.L. Wang, L.
Cheng, X. Liang, A.C. To, Current and future trends in topology optimization for additive manufacturing,
(2018) 2457–2483.
[3] M. Bayat, W. Dong, J. Thorborg, A.C. To, J.H. Hattel, A review of multi-scale and multi-physics
simulations of metal additive manufacturing processes with focus on modeling strategies, Addit. Manuf.
47 (2021). https://doi.org/10.1016/j.addma.2021.102278.
[4] A. Foroozmehr, M. Badrossamay, E. Foroozmehr, S. Golabi, Finite Element Simulation of Selective Laser
Melting process considering Optical Penetration Depth of laser in powder bed, Mater. Des. 89 (2016)
255–263. https://doi.org/10.1016/j.matdes.2015.10.002.
[5] Y.S. Lee, W. Zhang, Modeling of heat transfer, fluid flow and solidification microstructure of nickel-base
superalloy fabricated by laser powder bed fusion, Addit. Manuf. 12 (2016) 178–188.
https://doi.org/10.1016/j.addma.2016.05.003.
[6] S.A. Khairallah, A.T. Anderson, A. Rubenchik, W.E. King, Laser powder-bed fusion additive
manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation
zones, Acta Mater. 108 (2016) 36–45. https://doi.org/10.1016/j.actamat.2016.02.014.
[7] M. Bayat, A. Thanki, S. Mohanty, A. Witvrouw, S. Yang, J. Thorborg, N.S. Tiedje, J.H. Hattel, Keyholeinduced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and
experimental validation, Addit. Manuf. 30 (2019). https://doi.org/10.1016/j.addma.2019.100835.
[8] A. Charles, M. Bayat, A. Elkaseer, L. Thijs, J.H. Hattel, S. Scholz, Elucidation of dross formation in laser
powder bed fusion at down-facing surfaces: phenomenon-oriented multiphysics simulation and
experimental validation, Addit. Manuf. Under revi (2021).
[9] M. Bayat, V.K. Nadimpalli, D.B. Pedersen, J.H. Hattel, A fundamental investigation of thermo-capillarity
in laser powder bed fusion of metals and alloys, Int. J. Heat Mass Transf. 166 (2021) 120766.
https://doi.org/10.1016/j.ijheatmasstransfer.2020.120766.
[10] J.D. Roehling, S.A. Khairallah, Y. Shen, A. Bayramian, C.D. Boley, A.M. Rubenchik, J. Demuth, N.
Duanmu, M.J. Matthews, Physics of large-area pulsed laser powder bed fusion, Addit. Manuf. 46 (2021) https://doi.org/10.1016/j.addma.2021.102186.
[11] M. Zheng, L. Wei, J. Chen, Q. Zhang, J. Li, S. Sui, G. Wang, W. Huang, Surface morphology evolution
during pulsed selective laser melting: Numerical and experimental investigations, Appl. Surf. Sci. 496
(2019) 143649. https://doi.org/10.1016/j.apsusc.2019.143649.
[12] M. Bayat, V.K. Nadimpalli, D.B. Pedersen, J.H. Hattel, A fundamental investigation of thermo-capillarity
in laser powder bed fusion of metals and alloys, Int. J. Heat Mass Transf. 166 (2021).
https://doi.org/10.1016/j.ijheatmasstransfer.2020.120766.

Effect of roughness on separation zone dimensions.

Experimental and numerical study of flow at a 90 degree lateral turnout with enhanced roughness coefficient and invert level changes

조도 계수 및 역전 수준 변화가 개선된 90도 측면 분출구에서의 유동에 대한 실험적 및 수치적 연구

Maryam BagheriSeyed M. Ali ZomorodianMasih ZolghadrH. Md. AzamathullaC. Venkata Siva Rama Prasad

Abstract

측면 분기기(흡입구)의 상류 측에서 흐름 분리는 분기기 입구에서 와류를 일으키는 중요한 문제입니다. 이는 흐름의 유효 폭, 출력 용량 및 효율성을 감소시킵니다. 따라서 분리지대의 크기를 파악하고 크기를 줄이기 위한 방안을 제시하는 것이 필수적이다. 본 연구에서는 분리 구역의 치수를 줄이기 위한 방법으로 7가지 유형의 거칠기 요소를 분기구 입구에 설치하고 4가지 다른 배출(총 84번의 실험을 수행)과 함께 3개의 서로 다른 베드 반전 레벨을 조사했습니다. 또한 3D CFD(Computational Fluid Dynamics) 모델을 사용하여 분리 영역의 흐름 패턴과 치수를 평가했습니다. 결과는 거칠기 계수를 향상시키면 분리 영역 치수를 최대 38%까지 줄일 수 있는 반면, 드롭 구현 효과는 사용된 거칠기 계수를 기반으로 이 영역을 다르게 축소할 수 있음을 보여주었습니다. 두 가지 방법을 결합하면 분리 영역 치수를 최대 63%까지 줄일 수 있습니다.

Flow separation at the upstream side of lateral turnouts (intakes) is a critical issue causing eddy currents at the turnout entrance. It reduces the effective width of flow, turnout capacity and efficiency. Therefore, it is essential to identify the dimensions of the separation zone and propose remedies to reduce its dimensions. Installation of 7 types of roughening elements at the turnout entrance and 3 different bed invert levels, with 4 different discharges (making a total of 84 experiments) were examined in this study as a method to reduce the dimensions of the separation zone. Additionally, a 3-D Computational Fluid Dynamic (CFD) model was utilized to evaluate the flow pattern and dimensions of the separation zone. Results showed that enhancing the roughness coefficient can reduce the separation zone dimensions up to 38% while the drop implementation effect can scale down this area differently based on the roughness coefficient used. Combining both methods can reduce the separation zone dimensions up to 63%.

HIGHLIGHTS

Listen

  • Flow separation at the upstream side of lateral turnouts (intakes) is a critical issue causing eddy currents at the turnout entrance.
  • Installation of 7 types of roughening elements at the turnout entrance and 3 different bed level inverts were investigated.
  • Additionally, a 3-D Computational Fluid Dynamic (CFD) model was utilized to evaluate the flow.
  • Combining both methods can reduce the separation zone dimensions by up to 63%.
Experimental and numerical study of flow at a 90 degree lateral turnout with enhanced roughness coefficient and invert level changes
Experimental and numerical study of flow at a 90 degree lateral turnout with enhanced roughness coefficient and invert level changes

Keywords

discharge ratioflow separation zoneintakethree dimensional simulation

INTRODUCTION

Listen

Turnouts or intakes are amongst the oldest and most widely used hydraulic structures in irrigation networks. Turnouts are also used in water distribution, transmission networks, power generation facilities, and waste water treatment plants etc. The flows that enter a turnout have a strong momentum in the direction of the main waterway and that is why flow separation occurs inside the turnout. The horizontal vortex formed in the separation area is a suitable place for accumulation and deposition of sediments. The separation zone is a vulnerable area for sedimentation and for reduction of effective flow due to a contracted flow region in the lateral channel. Sedimentaion in the entrance of the intake can gradually be transfered into the lateral channel and decrease the capacity of the higher order channels over time (Jalili et al. 2011). On the other hand, the existence of coarse-grained materials causes erosion and destruction of the waterway side walls and bottom. In addition, sedimentation creates conditions for vegetation to take root and damage the waterway cover, which causes water to leak from its perimeter. Therefore, it is important to investigate the pattern of the flow separation area in turnouts and provide solutions to reduce the dimensions of this area.

The three-dimensional flow structure at turnouts is quite complex. In an experimental study by Neary & Odgaard (1993) in a 90-degree water turnout it was found that the secondary currents and separation zone varies from the bed to the water surface. They also found that at a 90-degree water turnout, the bed roughness and discharge ratio play a critical role in flow structure. They asserted that an explanation of sediment behavior at a diversion entrance requires a comprehensive understanding of 3D flow patterns around the lateral-channel entrance. In addition, they suggested that there is a strong similarity between flow in a channel bend and a diversion channel, and that this similarity can rationalize the use of bend flow models for estimation of 3D flow structures in diversion channels.

Some of the distinctive characteristics of dividing flow in a turnout include a zone of separation immediately near the entrance of the lateral turnout (separation zone), a contracted flow region in the branch channel (contracted flow), and a stagnation point near the downstream corner of the junction (stagnation zone). In the region downstream of the junction, along the continuous far wall, separation due to flow expansion may occur (Ramamurthy et al. 2007), that is, a separation zone. This can both reduce the turnout efficiency and the effective width of flow while increasing the sediment deposition in the turnout entrance (Jalili et al. 2011). Installation of submerged vanes in the turnout entrance is a method which is already applied to reduce the size of flow separation zones. The separation zone draws sediments and floating materials into themselves. This reduces effective cross-section area and reduces transmission capacity. These results have also been obtained in past studies, including by Ramamurthy et al. (2007) and in Jalili et al. (2011). Submerged vanes (Iowa vanes) are designed in order to modify the near-bed flow pattern and bed-sediment motion in the transverse direction of the river. The vanes are installed vertically on the channel bed, at an angle of attack which is usually oriented at 10–25 degrees to the local primary flow direction. Vane height is typically 0.2–0.5 times the local water depth during design flow conditions and vane length is 2–3 times its height (Odgaard & Wang 1991). They are vortex-generating devices that generate secondary circulation, thereby redistributing sediment within the channel cross section. Several factors affect the flow separation zone such as the ratio of lateral turnout discharge to main channel discharge, angle of lateral channel with respect to the main channel flow direction and size of applied submerged vanes. Nakato et al. (1990) found that sediment management using submerged vanes in the turnout entrance to Station 3 of the Council Bluffs plant, located on the Missouri River, is applicable and efficient. The results show submerged vanes are an appropriate solution for reduction of sediment deposition in a turnout entrance. The flow was treated as 3D and tests results were obtained for the flow characteristics of dividing flows in a 90-degree sharp-edged, junction. The main and lateral channel were rectangular with the same dimensions (Ramamurthy et al., 2007).

Keshavarzi & Habibi (2005) carried out experiments on intake with angles of 45, 67, 79 and 90 degrees in different discharge ratios and reported the optimum angle for inlet flow with the lowest flow separation area to be about 55 degrees. The predicted flow characteristics were validated using experimental data. The results indicated that the width and length of the separation zone increases with the increase in the discharge ratio Qr (ratio of outflow per unit width in the turnout to inflow per unit width in the main channel).

Abbasi et al. (2004) performed experiments to investigate the dimensions of the flow separation zone at a lateral turnout entrance. They demonstrated that the length and width of the separation zone decreases with the increasing ratio of lateral turn-out discharge. They also found that with a reducing angle of lateral turnout, the length of the separation zone scales up and width of separation zone reduces. Then they compared their observations with results of Kasthuri & Pundarikanthan (1987) who conducted some experiments in an open-channel junction formed by channels of equal width and an angle of lateral 90 degree turnout, which showed the dimensions of the separation zone in their experiments to be smaller than in previous studies. Kasthuri & Pundarikanthan (1987) studied vortex and flow separation dimensions at the entrance of a 90 degree channel. Results showed that increasing the diversion discharge ratio can reduce the length and width of the vortex area. They also showed that the length and width of the vortex area remain constant at diversion ratios greater than 0.7. Karami Moghaddam & Keshavarzi (2007) analyzed the flow characteristics in turnouts with angles of 55 and 90 degrees. They reported that the dimensions of the separation zone decrease by increasing the discharge ratio and reducing the turnout angle with respect to the main channel. Studies about flow separation zone can be found in Jalili et al. (2011)Nikbin & Borghei (2011)Seyedian et al. (2008).

Jamshidi et al. (2016) measured the dimensions of a flow separation zone in the presence of submerged vanes with five arrangements (parallel, stagger, compound, piney and butterflies). Results showed that the ratio of the width to the length of the separation zone (shape index) was between 0.2 and 0.28 for all arrangements.

Karami et al. (2017) developed a 3D computational fluid dynamic (CFD) code which was calibrated by measured data. They used the model to evaluate flow pattern, diversion ratio of discharge, strength of the secondary flow, and dimensions of the vortex inside the channel in various dikes and submerged vane installation scenarios. Results showed that the diversion ratio of discharge in the diversion channel is dependent on the width of the flow separation area in the main channel. A dike, perpendicular to the flow, doubles the ratio of diverted discharge and reduces the suspended sediment load compared with the base-line situation by creating outer arch conditions. In addition, increasing the longitudinal distance between vanes increases the velocity gradient between the vanes and leads to a more severe erosion of the bed near the vanes.Figure 1VIEW LARGEDOWNLOAD SLIDE

Laboratory channel dimensions.

Al-Zubaidy & Hilo (2021) used the Navier–Stokes equation to study the flow of incompressible fluids. Using the CFD software ANSYS Fluent 19.2, 3D flow patterns were simulated at a diversion channel. Their results showed good agreement using the comparison between the experimental and numerical results when the k-omega turbulence viscous model was employed. Simulation of the flow pattern was then done at the lateral channel junction using a variety of geometry designs. These improvements included changing the intake’s inclination angle and chamfering and rounding the inner corner of the intake mouth instead of the sharp edge. Flow parameters at the diversion including velocity streamlines, bed shear stress, and separation zone dimensions were computed in their study. The findings demonstrated that changing the 90° lateral intake geometry can improve the flow pattern and bed shear stress at the intake junction. Consequently, sedimentation and erosion problems are reduced. According to the conclusions of their study, a branching angle of 30° to 45° is the best configuration for increasing branching channel discharge, lowering branching channel sediment concentration.

The review of the literature shows that most of the studies deal with turnout angle, discharge ratio and implementation of vanes as techniques to reduce the area of the separation zone. This study examines the effect of roughness coefficient and drop implementation at the entrance of a 90-degree lateral turnout on the dimensions of the separation zone. As far as the authors are aware, these two variables have never been studied as a remedy to decrease the separation zone dimensions whilst enhancing turnout efficiency. Additionally, a three-dimensional numerical model is applied to simulate the flow pattern around the turnout. The numerical results are verified against experimental data.

METHOD

Experimental setup

Listen

The experiments were conducted in a 90 degree dividing flow laboratory channel. The main channel is 15 m long, 0.5 m wide and 0.4 m high and the branch channel is 3 m long, 0.35 m wide and 0.4 m high, as shown in Figure 1. The tests were carried out at 9.65 m from the beginning of the flume and were far enough from the inlet, so we were sure that the flow was fully developed. According to Kirkgöz & Ardiçlioğlu (1997) the length of the developing region would be approximantly 65 and 72 times the flow depth. In this study, the depth is 9 cm, which makes this condition.

Both the main and lateral channel had a slope of 0.0003 with side walls of concrete. A 100 hp pump discharged the water into a stilling basin at the entrance of the main flume. The discharge was measured using an ultrasonic discharge meter around the discharge pipe. Eighty-four experiments in total were carried out at range of 0.1<Fr<0.4 (Froude numbers in main channel and upstream of turnout). The depth of water in the main channel in the experiments was 9 cm, in which case the effect of surface tension can be considered; according to research by Zolghadr & Shafai Bejestan (2020) and Zolghadr et al. (2021), when the water depth is more than 6 cm, the effect of surface tension is reduced and can be ignored given that the separation phenomenon occurs in the boundary layer, the height of the roughness creates disturbances in growth and development of the boundary layer and, as a result, separation growth is also faced with disruption and its dimensions grow less compared to smooth surfaces. Similar conditions occur in case of drop implementation. A disturbance occurs in the growth of the boundary layer and as a result the separation zone dimensions decrease. In order to investigate the effect of roughness coefficient and drop implementation on the separation zone dimensions, four different discharges (16, 18, 21, 23 l/s) in subcritical conditions, seven Manning (Strickler) roughness coefficients (0.009, 0.011, 0.017, 0.023, 0.028, 0.030, 0.032) as shown in Figure 2 and three invert elevation differences between the main channel and lateral turnout invert (0, 5 and 10 cm) at the entrance of the turnout were considered. The Manning roughness coefficient values were selected based on available and feasible values for real conditions, so that 0.009 is equivalent to galvanized sheet roughness and selected for the baseline tests. 0.011 is for concrete with neat surface, 0.017 and 0.023 are for unfinished and gunite concrete respectively. 0.030 and 0.032 values are for concrete on irregular excavated rock (Chow 1959). The roughness coefficients were created by gluing sediment particles on a thin galvanized sheet which was installed at the upstream side of the lateral turnout. The values of roughness coefficients were calculated based on the Manning-Strickler formula. For this purpose, some uniformly graded sediment samples were prepared and the Manning roughness coefficient of each sample was determined with respect to the median size (D50) value pasted into the Manning-Strickler formula. Some KMnO4 was sifted in the main channel upstream to visualize and measure the dimensions of the separation zone. Consequently, when KMnO4 approached the lateral turnout a photo of the separation zone was taken from a top view. All the experiments were recorded and several photos were taken during the experiment after stablishment of steady flow conditions. The photos were then imported to AutoCAD to measure the separation zone dimensions. Because all the shooting was done with a high-definition camera and it was possible to zoom in, the results are very accurate.Figure 2VIEW LARGEDOWNLOAD SLIDE

Roughness plates.

The velocity values were also recorded by a one-dimensional velocity meter at 15 cm distance from the turnout entrance and in transverse direction (perpendicular to the flow direction).

The water level was also measured by depth gauges with a accuracy of 0.1 mm, and velocity in one direction with a single-dimensional KENEK LP 1100 with an accuracy of ±0.02 m/s (0–1 m/s), ± 0.04 m/s (1–2 m/s), ± 0.08 m/s (2–4 m/s), ±0.10 m/s (4–5 m/s).

Numerical simulation

ListenA FLOW-3D numerical model was utilized as a solver of the Navier-Stokes equation to simulate the three-dimensional flow field at the entrance of the turnout. The governing equations included continuity momentum equations. The continuity equation, regardless of the density of the fluid in the form of Cartesian coordinates x, y, and z, is as follows:

formula

(1)where uv, and w represent the velocity components in the x, y, and z directions, respectively; AxAy, and Az are the surface flow fractions in the xy, and z directions, respectively; VF denotes flow volume fraction; r is the density of the fluid; t is time; and Rsor refers to the source of the mass. Equations (2)–(4) show momentum equations in xy and z dimensions respectively :

formula

(2)

formula

(3)

formula

(4)where GxGy, and Gz are the accelerations caused by gravity in the xy, and z directions, respectively; and fxfy, and fz are the accelerations caused by viscosity in the xy, and z directions, respectively.

The turbulence models used in this study were the renormalized group (RNG) models. Evaluation of the concordance of the mentioned models with experimental studies showed that the RNG model provides more accurate results.

Two blocks of mesh were used to simulate the main channels and lateral turnout. The meshes were denser in the vicinity of the entrance of the turnout in order to increase the accuracy of computations. Boundary conditions for the main mesh block included inflow for the channel entrance (volumetric flow rate), outflow for the channel exit, ‘wall’ for the bed and the right boundary and ‘symmetry’ for the top (free surface) and left boundaries (turnout). The side wall roughness coefficient was given to the software as the Manning number in surface roughness of any component. Considering the restrictions in the available processor, a main mesh block with appropriate mesh size was defined to simulate the main flow field in the channel, while the nested mesh-block technique was utilized to create a very dense solution field near the roughness plate in order to provide accurate results around the plates and near the entrance of the lateral turnout. This technique reduced the number of required mesh elements by up to 60% in comparison with the method in which the mesh size of the main solution field was decreased to the required extent.

The numerical outputs are verified against experimental data. The hydraulic characteristics of the experiment are shown in Table 1.Table 1

Hydraulic conditions of the flow

Q(L/s)FrY1 (m)Q2/Q1
16 0.449 0.09 0.22 
18 0.335 0.09 0.61 
21 0.242 0.09 0.71 
23 0.180 0.09 1.04 

RESULTS AND DISCUSSION

Experimental results

Listen

During the experiments, the dimensions of the separation zone were recorded with an HD camera. Some photos were imported to AutoCad software. Then, the separation zones dimensions were measured and compared in different scenarios.

At the beginning, the flow pattern in the separation zone for four different hydraulic conditions was studied for seven different Manning roughness coefficients from 0.009 to 0.032. To compare the obtained results, roughness of 0.009 was considered as the base line. The percentage of reduction in separation zone area in different roughness coefficients is shown in Figure 3. According to this figure, by increasing the roughness of the turnout side wall, the separation zone area ratio reduces (ratio of separation zone area to turnout area). In other words, in any desired Froud number, the highest dimensions of the separation zone area are related to the lowest roughness coefficients. In Figure 3, ‘A’ is the area of the separation zone and ‘Ai’ represents the total area of the turnout.Figure 3VIEW LARGEDOWNLOAD SLIDE

Effect of roughness on separation zone dimensions.Figure 4VIEW LARGEDOWNLOAD SLIDE

Effect of roughness on separation zone dimensions.

It should be mentioned that the separation zone dimensions change with depth, so that the area is larger at the surface than near the bed. This study measured the dimensions of this area at the surface. Figure 4 show exactly where the roughness elements were located.Figure 5VIEW LARGEDOWNLOAD SLIDE

Comparison of separation zone for n=0.023 and n=0.032.

Figure 5 shows images of the separation zone at n=0.023 and n=0.032 as examples, and show that the separation area at n=0.032 is smaller than that of n=0.023.

The difference between the effect of the two 0.032 and 0.030 roughnesses is minor. In other words, the dimensions of the separation zone decreased by increasing roughness up to 0.030 and then remained with negligable changes.

In the next step, the effect of intake invert relative to the main stream (drop) on the dimensions of the separation zone was investigated. To do this, three different invert levels were considered: (1) without drop; (2) a 5 cm drop between the main canal and intake canal; and (3) a 10 cm drop between the main canal and intake canal. The without drop mode was considered as the control state. Figure 6 shows the effect of drop implementation on separation zone dimensions. Tables 2 and 3 show the reduced percentage of separation zone areas in 5 and 10 cm drop compared to no drop conditions as the base line. It was found that the best results were obtained when a 10 cm drop was implemented.Table 2

Decrease percentage of separation zone area in 5 cm drop

Frn=0.011n=0.017n=0.023n=0.028n=0.030n=0.032
0.08 10.56 11.06 25.27 33.03 35.57 36.5 
0.121 7.66 11.14 11.88 15.93 34.59 36.25 
0.353 1.38 2.63 8.17 14.39 31.20 31.29 
0.362 11.54 19.56 25.73 37.89 38.31 

Table 3

Decrease percentage of separation zone area in 10 cm drop

Frn=0.011n=0.017n=0.023n=0.028n=0.030n=0.032
0.047 4.30 8.75 23.47 31.22 34.96 35.13 
0.119 11.01 13.16 15.02 21.48 39.45 40.68 
0.348 3.89 5.71 9.82 16.09 29 30.96 
0.354 2.84 10.44 18.42 25.45 35.68 35.76 

Figure 6VIEW LARGEDOWNLOAD SLIDE

Effect of drop implementation on separation zone dimensions.

The combined effect of drop and roughness is shown in Figure 7. According to this figure, by installing a drop structure at the entrance of the intake, the dimensions of the separation zone scales down in any desired roughness coefficient. Results indicated that by increasing the roughness coefficient or drop implementation individually, the separation zone area decreases up to 38 and 25% respectively. However, employing both techniques simultaneously can reduce the separation zone area up to 63% (Table 4). The reason for the reduction of the dimensions of the separation zone area by drop implementation can be attributed to the increase of discharge ratio. This reduces the dimensions of the separation zone area.Table 4

Reduction in percentage of combined effect of roughness and 10 cm drop

Qin=0.011n=0.017n=0.023n=0.028n=0.030n=0.032
16 32.3 35.07 37.2 45.7 58.01 59.1 
18 44.5 34.15 36.18 48.13 54.2 56.18 
21 43.18 32.33 42.30 37.79 57.16 63.2 
23 40.56 34.5 34.09 46.25 50.12 57.2 

Figure 7VIEW LARGEDOWNLOAD SLIDE

Combined effect of roughness and drop on separation zone dimensions.

This method increases the discharge ratio (ratio of turnout to main channel discharge). The results are compatible with the literature. Some other researchers reported that increasing the discharge ratio can scale down the separation zone dimensions (Karami Moghaddam & Keshavarzi 2007Ramamurthy et al. 2007). However, these researchers employed other methods to enhance the discharge ratio. Drop implementation is simple and applicable in practice, since there is normally an elevation difference between the main and lateral canal in irrigation networks to ensure gravity flow occurance.

Table 4 depicts the decrease in percentage of the separation zone compared to base line conditions in different arrangements of the combined tests.Figure 8VIEW LARGEDOWNLOAD SLIDE

Velocity profiles for various roughness coefficients along turnout width.

A comparison between the proposed methods introduced in this paper and traditional methods such as installation of submerged vanes, and changing the inlet geometry (angle, radius) was performed. Figure 8 shows the comparison of the results. The comparison shows that the new techniques can be highly influential and still practical. In this research, with no change in structural geometry (enhancement of roughness coefficient) or minor changes with respect to drop implementation, the dimensions of the separation zone are decreased noticeably. The velocity values were also recorded by a one-dimensional velocity meter at 15 cm distance from the turnout entrance and in a transverse direction (perpendicular to the flow direction). The results are shown in Figure 9.Figure 9VIEW LARGEDOWNLOAD SLIDE

Effect of roughness on separation zone dimensions in numerical study.

Numerical results

Listen

This study examined the flow patterns around the entrance of a diversion channel due to various wall roughnesses in the diversion channel. Results indicated that increasing the discharge ratio in the main channel and diversion channel reduces the area of the separation zone in the diversion channel.Figure 10VIEW LARGEDOWNLOAD SLIDE

Comparision of the vortex area (software output) for three roughnesses (0.009, 0.023 and 0.032).A laboratory and numerical error rate of 0.2605 was calculated from the following formula,

formula

where Uexp is the experimental result, Unum is the numerical result, and N is the number of data.

Figure 9 shows the effect of roughness on separation zone dimensions in numerical study. Figure 10 compares the vortex area (software output) for three roughnesses, 0.009, 0.023 and 0.032 and Figure 11 shows the flow lines (tecplot output) that indicate the effect of roughness on flow in the separation zone. Numerical analysis shows that by increasing the roughness coefficient, the dimensions of the separation zone area decrease, as shown in Figure 10 where the separation zone area at n=0.032 is less than the separation zone area at n=0.009.Figure 11VIEW LARGEDOWNLOAD SLIDE

Comparison of vortex area in 3D mode (tecplot output) with two roughnesses (a) 0.009 and (b) 0.032.Figure 12VIEW LARGEDOWNLOAD SLIDE

Velocity vector for flow condition Q1/422 l/s, near surface.

The velocities intensified moving midway toward the turnout showing that the effective area is scaled down. The velocity values were almost equal to zero near the side walls as expected. As shown in Figure 12 the approach vortex area velocity decreases. Experimental and numerical measured velocity at x=0.15 m of the diversion channel compared in Figure 13 shows that away from the separation zone area, the velocity increases. All longitudinal velocity contours near the vortex area are distinctly different between different roughnesses. The separation zone is larger at less roughness both in length and width.Figure 13VIEW LARGEDOWNLOAD SLIDE

Exprimental and numerical measured velocity.

CONCLUSION

Listen

This study introduces practical and feasible methods for enhancing turnout efficiency by reducing the separation zone dimensions. Increasing the roughness coefficient and implementation of inlet drop were considered as remedies for reduction of separation zone dimensions. A data set has been compiled that fully describes the complex, 3D flow conditions present in a 90 degree turnout channel for selected flow conditions. The aim of this numerical model was to compare the results of a laboratory model in the area of the separation zone and velocity. Results showed that enhancing roughness coefficient reduce the separation zone dimensions up to 38% while the drop implementation effect can scale down this area differently based on roughness coefficient used. Combining both methods can reduce the separation zone dimensions up to 63%. Further research is proposed to investigate the effect of roughness and drop implementation on sedimentation pattern at lateral turnouts. The dimensions of the separation zone decreases with the increase of the non-dimensional parameter, due to the reduction ratio of turnout discharge increasing in all the experiments.

This method increases the discharge ratio (ratio of turnout to main channel discharge). The results are compatible with the literature. Other researchers have reported that intensifying the discharge ratio can scale down the separation zone dimensions (Karami Moghaddam & Keshavarzi 2007Ramamurthy et al. 2007). However, they employed other methods to enhance the discharge ratio. Employing both techniques simultaneously can decrease the separation zone dimensions up to 63%. A comparison between the new methods introduced in this paper and traditional methods such as installation of submerged vanes, and changing the inlet geometry (angle, radius) was performed. The comparison shows that the new techniques can be highly influential and still practical. The numerical and laboratory models are in good agreement and show that the method used in this study has been effective in reducing the separation area. This method is simple, economical and can prevent sediment deposition in the intake canal. Results show that CFD prediction of the fluid through the separation zone at the canal intake can be predicted reasonably well and the RNG model offers the best results in terms of predictability.

DATA AVAILABILITY STATEMENT

Listen

All relevant data are included in the paper or its Supplementary Information.

REFERENCES

Abbasi A., Ghodsian M., Habibi M. & Salehi Neishabouri S. A. 2004 Experimental investigation on dimensions of flow separation zone at lateral intakeentrance. Research & Construction; Pajouhesh va Sazandegi 62, 38–44. (In Persian).Google Scholar Al-Zubaidy R. & Hilo A. 2021 Numerical investigation of flow behavior at the lateral intake using Computational Fluid Dynamics (CFD). Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.11.172.Google Scholar Chow V. T. 1959 Open Channel Hydraulics. McGraw-Hill, New York.Jalili H., Hosseinzadeh Dalir A. & Farsadizadeh D. 2011 Effect of intake geometry on the sediment transport and lateral flow pattern. Iranian Water Research Journal 5 (9), 1–10. (In Persian).Google Scholar Jamshidi A., Farsadizadeh D. & Hosseinzadeh Dalir A. 2016 Variations of flow separation zone at lateral intake entrance using submerged vanes. Journal of Civil Engineering Urban 6 (3), 54–63. Journal homepage. Available from: www.ojceu.ir/main.Google Scholar Karami Moghaddam K. & Keshavarzi A. 2007 Investigation of flow structure in lateral intakes of 55° and 90° with rounded entrance edge. In: 03 National Congress on Civil Engineering University of Tabriz. Available from: https://civilica.com/doc/16317. (In Persian).Google Scholar Karami H., Farzin S., Sadrabadi M. T. & Moazeni H. 2017 Simulation of flow pattern at rectangular lateral intake with different dike and submerged vane scenarios. Journal of Water Science and Engineering 10 (3), 246–255. https://doi.org/10.1016/j.wse.2017.10.001.Google ScholarCrossref  Kasthuri B. & Pundarikanthan N. V. 1987 Discussion on separation zone at open- channel junction. Journal of Hydraulic Engineering 113 (4), 543–548.Google ScholarCrossref  Keshavarzi A. & Habibi L. 2005 Optimizing water intake angle by flow separation analysis. Journal of Irrigation and Drain 54, 543–552. https://doi.org/10.1002/ird.207.Google ScholarCrossref  Kirkgöz M. S. & Ardiçlioğlu M. 1997 Velocity profiles of developing and developed open channel flow. Journal of Hydraulic Engineering 1099–1105. 10.1061/(ASCE)0733-9429(1997)123:12(1099).Google Scholar Nakato T., Kennedy J. F. & Bauerly D. 1990 Pumpstation intake-shoaling control with submerge vanes. Journal of Hydraulic Engineering. https://doi.org/10.1061/(ASCE)0733-9429(1990)116:1(119).Google Scholar Neary V. S. & Odgaard J. A. 1993 Three-dimensional flow structure at open channel diversions. Journal of Hydraulic Engineering. ASCE 119 (11), 1224–1230. https://doi.org/10.1061/(ASCE)0733-9429(1993)119:11(1223).Google ScholarCrossref  Nikbin S. & Borghei S. M. 2011 Experimental investigation of submerged vanes effect on dimensions of flow separation zone at a 90° openchannel junction. In: 06rd National Congress on Civil Engineering University of Semnan. (In Persian). Available from: https://civilica.com/doc/120494.Google Scholar Odgaard J. A. & Wang Y. 1991 Sediment management with submerged vanes, I: theory. Journal of Hydraulic Engineering 117 (3), 267–283.Google ScholarCrossref  Ramamurthy A. S., Junying Q. & Diep V. 2007 Numerical and experimental study of dividing open-channel flows. Journal of Hydraulic Engineering. See: https://doi.org/10.1061/(ASCE)0733-9429(2007)133:10(1135).Google Scholar Seyedian S., Karami Moghaddam K. & Shafai Begestan M. 2008 Determining the optimal radius in lateral intakes of 55° and 90° using variation of flow velocity. In: 07th Iranian Hydraulic Conference. Power & Water University of Technology (PWUT). (In Persian). Available from: https://civilica.com/doc/56251.Google Scholar Zolghadr M. & Shafai Bejestan M. 2020 Six legged concrete (SLC) elements as scour countermeasures at wing wall bridge abutments. International Journal of River Basin Management. doi: 10.1080/15715124.2020.1726357.Google Scholar Zolghadr M., Zomorodian S. M. A., Shabani R. & Azamatulla H.Md. 2021 Migration of sand mining pit in rivers: an experimental, numerical and case study. Measurement. https://doi.org/10.1016/j.measurement.2020.108944.Google Scholar © 2022 The AuthorsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition

Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition

Xiang WangLin-Jie ZhangJie Ning, and Suck-Joo Na
Published Online:8 Apr 2022https://doi.org/10.1089/3dp.2021.0159

Abstract

A 3D numerical model of heat transfer and fluid flow of molten pool in the process of laser wire deposition was presented by computational fluid dynamics technique. The simulation results of the deposition morphology were also compared with the experimental results under the condition of liquid bridge transfer mode. Moreover, they showed a good agreement. Considering the effect of recoil pressure, the morphology of the deposit metal obtained by the simulation was similar to the experiment result. Molten metal at the wire tip was peeled off and flowed into the molten pool, and then spread to both sides of the deposition layer under the recoil pressure. In addition, the results of simulation and high-speed charge-coupled device presented that a wedge transition zone, with a length of ∼6 mm, was formed behind the keyhole in the liquid bridge transfer process, where the height of deposited metal decreased gradually. After solidification, metal in the transition zone retained the original melt morphology, resulting in a decrease in the height of the tail of the deposition layer.

Keywords

LWD, CFD, liquid bridge transfer, fluid dynamics, wedge transition zone

Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition
Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition
Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition
Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition

References

1. Matthews MJ, Guss G, Khairallah SA, et al. Denudation of metal powder layers in laser powder bed fusion processes. Acta Mater 2016;114:33–42. CrossrefGoogle Scholar

2. Ge WJ, Han SW, Fang YC, et al. Mechanism of surface morphology in electron beam melting of Ti6Al4V based on computational flow patterns. Appl Surf Sci 2017;419:150–158. CrossrefGoogle Scholar

3. Bai XW, Colegrove P, Ding JL, et al. Numerical analyswas of heat transfer and fluid flow in multilayer deposition of PAW-based wire and arc additive manufacturing. Int J Heat Mass Transf 2018;124:504–516. CrossrefGoogle Scholar

4. Torkamany MJ, Kaplan AFH, Ghaini FM. Wire deposition by a laser-induced boiling front. Opt Laser Technol 2015;69:104–112. CrossrefGoogle Scholar

5. Yu Y, Huang W, Wang G. Investigation of melting dynamics of filler wire during wire feed laser welding. J Mec Sci Technol 2013;27:1097–1108. CrossrefGoogle Scholar

6. Ma G, Li L, Chen Y. Effects of beam confgurations on wire melting and transfer behaviors in dual beam laser welding with fller wire. Opt Laser Technol 2017;91:138–148. CrossrefGoogle Scholar

7. Abioye TE, Folkes J, Clare AT. A parametric study of Inconel 625 wire laser deposition. J Mater Process Tech 2013;213:2145–2151. CrossrefGoogle Scholar

8. Wei S, Wang G, Shin YC, et al. Comprehensive modeling of transport phenomena in laser hot-wire deposition process. Int J Heat Mass Transf 2018;125:1356–1368. CrossrefGoogle Scholar

9. Gu H, Li L. Computational fluid dynamic simulation of gravity and pressure effects in laser metal deposition for potential additive manufacturing in space. Int J Heat Mass Transf 2019;140:51–65. CrossrefGoogle Scholar

10. Hu R, Luo M, Liu T, et al. Thermal fluid dynamics of liquid bridge transfer in laser wire deposition 3D printing. Sci Technolf Weld Join 2019;24:1–11. Google Scholar

11. Chatterjee D, Chakraborty S. A hybrid lattice Boltzmann model for solid–liquid phase transition in presence of fluid flow. Phys Lett A 2006;351:359–367. CrossrefGoogle Scholar

12. Wu L, Cheon J, Kiran DV, et al. CFD simulations of GMA welding of horizontal fillet joints based on coordinate rotation of arc models. J Mater Process Tech 2016;231:221–238. CrossrefGoogle Scholar

13. Gerhard W, Boyer RR, Collings EW. Materials Properties Handbook: Titanium Alloys. ASM International: Almere, The Netherlands, 1994. Google Scholar

14. Colegrove P, Simiand PE, Varughese A, et al. Evaluation of a drilling model approach to represent laser spot microwelding. In: ASM Proceedings of the international conference: trends in welding research; 2009. Google Scholar

15. Boivineau M, Cagran C, Doytier D, et al. Thermophysical properties of solid and liquid Ti-6Al-4V (TA6V) alloy. Int J Thermophys 2006;27:507–529. CrossrefGoogle Scholar

16. Shejndlin AE, Kenisarin MM, Chekhovskoj VY. Melting point of yttrium oxide. AN SSSR 1974;216:582–584. Google Scholar

17. Cho JH, Na SJ. Teflection and Fresnel absorption of laser beam in keyhole. J Phys D Appl Phys 2006;39:5372–5378. CrossrefGoogle Scholar

18. Han SW, Ahn J, Na SJ. A study on ray tracing method for CFD simulations of laser keyhole welding: Progressive search method. Weld World 2016;60:247–258. CrossrefGoogle Scholar

19. Allmen MV. Laser-Beam Interactions with Materials. Springer, Berlin-Heidelberg, 1995. Google Scholar

20. Dobson PJ. Absorption and scattering of light by small particles. Phys Bull 1984;35:104. CrossrefGoogle Scholar

21. Greses J, Hilton PA, Barlow CY. Plume attenuation under high power Nd:yttritium aluminum garnet laser welding. J Laser Appl 2004;16:9–15. CrossrefGoogle Scholar

22. Shcheglov PY, Uspenskiy SA, Gumenyuk AV, et al. Plume attenuation of laser radiation during high power fiber laser welding. Laser Phys Lett 2011;8:475–480. CrossrefGoogle Scholar

23. Yang P, Liou KN. Effective refractive index for determining ray propagation in an absorbing dielectric particle. J Quant Spectrosc Radiat Transf 2009;110:300–306. CrossrefGoogle Scholar

24. Barber PW. Absorption and scattering of light by small particles. J Colloid Interface Sci 1984;98:290–291. Google Scholar

25. Hu ZR, Chen X, Yang G, et al. Metal transfer in wire feeding-based electron beam 3D printing: Modes, dynamics, and transition criterion. Int J Heat Mass Transf 2018;126:877–887. CrossrefGoogle Scholar

26. David SA, Babu SS, Vitek JM. Welding: Solidification and microstructure. JOM 2013;55:14–20. CrossrefGoogle Scholar

27. Zhong ML, Liu W. Laser surface cladding: The state of the art and challenges. Proc Inst Mech Eng Part C J Mech Eng Sci 2010;224:1041–1060. CrossrefGoogle Scholar

28. Kobryn PA, Semiatin S. Microstructure and texture evolution during solidification processing of Ti-6Al-4V. J Mater Process Technol 2003;135:330–339. CrossrefGoogle Scholar

29. Debroy T, David S. Physical processes in fusion welding. Rev Mod Phys 1995;67:85–112. CrossrefGoogle Scholar

30. Lee YS, Nordin M, Babu SS, et al. Effect of fluid convection on dendrite arm spacing in laser deposition. Metall Trans B 2014;45:1520–1528. CrossrefGoogle Scholar

31. Rappaz M, David SA, Vitek JM, et al. Development of microstructures in Fe15Ni15Cr single crystal electron beam welds. Metall Trans A 1989;20:1125–1138. CrossrefGoogle Scholar

Figure 6. Circular section of the viscosity and shear-rate clouds.

Simulation and Visual Tester Verification of Solid Propellant Slurry Vacuum Plate Casting

Wu Yue,Li Zhuo,Lu RongFirst published: 26 February 2020 https://doi.org/10.1002/prep.201900411Citations: 3

Abstract

Using an improved Carreau constitutive model, a numerical simulation of the casting process of a type of solid propellant slurry vacuum plate casting was carried out using the Flow3D software. Through the flow process in the orifice flow channel and the combustion chamber, the flow velocity of the slurry passing through the plate flow channel was quantitatively analyzed, and the viscosity, shear rate, and leveling characteristics of the slurry in the combustion chamber were qualitatively analyzed and predicted. The pouring time, pouring quality, and flow state predicted by the numerical simulation were verified using a visual tester consisting of a vacuum plate casting system in which a pouring experiment was carried out. Studies have shown that HTPB three-component propellant slurry is a typical yielding pseudoplastic fluid. When the slurry flows through the flower plate and the airfoil, the fluid shear rate reaches its maximum value and the viscosity of the slurry decreases. The visual pouring platform was built and the experiment was controlled according to the numerically-calculated parameters, ensuring the same casting speed. The comparison between the predicted casting quality and the one obtained in the verification test resulted in an error less than 10 %. Moreover, the error between the simulated casting completion time and the process verification test result was also no more than 10 %. Last, the flow state of the slurry during the simulation was consistent with the one during the experimental test. The overall leveling of the slurry in the combustion chamber was adequate and no relatively large holes and flaws developed during the pouring process.

개선된 Carreau 구성 모델을 사용하여 FLOW-3D 소프트웨어를 사용하여 고체 추진제 슬러리 진공판 유형의 Casting Process에 대한 수치 시뮬레이션을 수행했습니다. 오리피스 유로와 연소실에서의 유동과정을 통해 판 유로를 통과하는 슬러리의 유속을 정량적으로 분석하고, 연소실에서 슬러리의 점도, 전단율, 레벨링 특성을 정성적으로 분석하하고, 예측하였습니다.

타설시간, 타설품질, 수치해석으로 예측된 ​​유동상태는 타설실험을 수행한 진공판주조시스템으로 구성된 비주얼 테스터를 이용하여 검증하였습니다.

연구에 따르면 HTPB 3성분 추진제 슬러리는 전형적인 생성 가소성 유체입니다. 슬러리가 플라워 플레이트와 에어포일을 통과할 때 유체 전단율이 최대값에 도달하고 슬러리의 점도가 감소합니다.

시각적 주입 플랫폼이 구축되었고 동일한 주조 속도를 보장하기 위해 수치적으로 계산된 매개변수에 따라 실험이 제어되었습니다. 예측된 casting 품질과 검증 테스트에서 얻은 품질을 비교한 결과 10 % 미만의 오류가 발생했습니다.

또한 모의 casting 완료시간과 공정검증시험 결과의 오차도 10 % 이하로 나타났습니다.

마지막으로 시뮬레이션 중 슬러리의 흐름 상태는 실험 테스트 시와 일치하였다. 연소실에서 슬러리의 전체 레벨링은 적절했으며 주입 과정에서 상대적으로 큰 구멍과 결함이 발생하지 않았습니다.

Figure 1. The equipment used in the vacuum flower-plate pouring process.
Figure 1. The equipment used in the vacuum flower-plate pouring process.
Figure 2. Calculation model.
Figure 2. Calculation model.
Figure 3. Grid block division unit.
Figure 3. Grid block division unit.
Figure 4. Circular section of the speed cloud.
Figure 4. Circular section of the speed cloud.
Figure 5. Viscosity and shear rate distribution cloud pattern flowing through the plate holes.
Figure 5. Viscosity and shear rate distribution cloud pattern flowing through the plate holes.
Figure 6. Circular section of the viscosity and shear-rate clouds.
Figure 6. Circular section of the viscosity and shear-rate clouds.
Figure 7. Volume fraction cloud chart at different time.
Figure 7. Volume fraction cloud chart at different time.
Figure 8. Experimental program.
Figure 8. Experimental program.
Figure 9. Emulation experimental device.
Figure 9. Emulation experimental device.
Figure 10. Visualization of the flow state of the pulp inside the tester.
Figure 10. Visualization of the flow state of the pulp inside the tester.

References

[1] B. M. Bandgar, V. N. Krishnamurthy, T. Mukundan, K. C. Sharma,
Mathematical Modeling of Rheological Properties of HydroxylTerminated Polybutadiene Binder and Dioctyl Adipate Plasticizer, J. Appl. Polym. Sci. 2002, 85, 1002–1007.
[2] B. Thiyyarkandy, M. Jain, G. S. Dombe, M. Mehilal, P. P. Singh, B.
Bhattacharya, Numerical Studies on Flow Behavior of Composite Propellant Slurry during Vacuum Casting, J.Aerosp.Technol.
Manage. 2012, 4, 197–203.
[3] T. Shimada, H. Habu, Y. Seike, S. Ooya, H. Miyachi, M. Ishikawa,
X-Ray Visualization Measurement of Slurry Flow in Solid Propellant Casting, Flow Meas. Instrum. 2007, 18, 235–240.
[4] Y. Damianou, G. C. Georgiou, On Poiseuille Flows of a Bingham
Plastic with Pressure-Dependent Rheological Parameters, J.
Non-Newtonian Fluid Mech. 2017, 250, 1–7.
[5] S. Sadasivan, S. K. Arumugam, M. Aggarwal, Numerical Simulation of Diffuser of a Gas Turbine using the Actuator Disc
Model, J.Appl. Fluid Mech. 2019, 12, 77–84.
[6] M. Acosta, V. L. Wiesner, C. J. Martinez, R. W. Trice, J. P. Youngblood, Effect of Polyvinylpyrrolidone Additions on the Rheology of Aqueous, Highly Loaded Alumina Suspensions, J. Am.
Ceram. Soc. 2013, 96, 1372–1382.
[7] Y. Wu, Numerical Simulation and Experiment Study of Flower
Plate Pouring System for Solid Propellant, Chin. J. Expl. Propell.
2017, 41, 506–511.
[8] T. M. G. Chu, J. W. Halloran, High-Temperature Flow Behavior
of Ceramic Suspensions, J. Am. Ceram. Soc. 2004, 83, 2189–
2195.
[9] T. Kaully, A. Siegmann, D. Shacham, Rheology of Highly Filled
Natural CaCO3 Composites. I. Effects of Solid Loading and Particle Size Distribution on Capillary Rheometry, Polym. Compos.
2007, 28, 512–523.
[10] M. M. Rueda, M.-C. Auscher, R. Fulchiron, T. Périé, G. Martin, P.
Sonntag, P. Cassagnau, Rheology and Applications of Highly
Filled Polymers: A Review of Current Understanding, Prog. Polym. Sci. 2017, 66, 22–53.
[11] F. Soltani, Ü. Yilmazer, Slip Velocity and Slip Layer Thickness in
Flow of Concentrated Suspensions, J. Appl. Polym. Sci. 1998,
70, 515–522.

[12] E. Landsem, T. L. Jensen, F. K. Hansen. E. Unneberg, T. E. Kristensen, Neutral Polymeric Bonding Agents (NPBA) and Their
Use in Smokeless Composite Rocket Propellants Based on
HMX-GAP-BuNENA. Propellants, Explos., Pyrotech.. 2012, 37,
581–589.
[13] J. Mewis, N. J. Wagner, Colloidal Suspension Rheology, Cambridge University Press, 2011.
[14] D. M. Kalyon, An Overview of the Rheological Behavior and
Characterization of Energetic Formulations: Ramifications on
Safety and Product Quality, J. Energ. Mater. 2006, 24, 213–245.
[15] H. Ohshima, Effective Viscosity of a Concentrated Suspension
of Uncharged Spherical Soft Particles, Langmuir 2010, 26,
6287–6294.

Figure 8: Instantaneous flow structures extracted using the Q-criterion (Qcriterion=1200) and colored by the magnitude of flow velocity.

Hybrid modeling on 3D hydraulic features of a step-pool unit

Chendi Zhang1
, Yuncheng Xu1,2, Marwan A Hassan3
, Mengzhen Xu1
, Pukang He1
1State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China. 2
College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100081, China.
5 3Department of Geography, University of British Columbia, 1984 West Mall, Vancouver BC, V6T1Z2, Canada.
Correspondence to: Chendi Zhang (chendinorthwest@163.com) and Mengzhen Xu (mzxu@mail.tsinghua.edu.cn)

Abstract

스텝 풀 시스템은 계류의 일반적인 기반이며 전 세계의 하천 복원 프로젝트에 활용되었습니다. 스텝 풀 장치는 스텝 풀 기능의 형태학적 진화 및 안정성과 밀접하게 상호 작용하는 것으로 보고된 매우 균일하지 않은 수력 특성을 나타냅니다.

그러나 스텝 풀 형태에 대한 3차원 수리학의 자세한 정보는 측정의 어려움으로 인해 부족했습니다. 이러한 지식 격차를 메우기 위해 SfM(Structure from Motion) 및 CFD(Computational Fluid Dynamics) 기술을 기반으로 하이브리드 모델을 구축했습니다. 이 모델은 CFD 시뮬레이션을 위한 입력으로 6가지 유속의 자연석으로 만든 인공 스텝 풀 장치가 있는 침대 표면의 3D 재구성을 사용했습니다.

하이브리드 모델은 스텝 풀 장치에 대한 3D 흐름 구조의 고해상도 시각화를 제공하는 데 성공했습니다. 결과는 계단 아래의 흐름 영역의 분할, 즉 수면에서의 통합 점프, 침대 근처의 줄무늬 후류 및 그 사이의 고속 제트를 보여줍니다.

수영장에서 난류 에너지의 매우 불균일한 분포가 밝혀졌으며 비슷한 용량을 가진 두 개의 에너지 소산기가 수영장에 공존하는 것으로 나타났습니다. 흐름 증가에 따른 풀 세굴 개발은 점프 및 후류 와류의 확장으로 이어지지만 이러한 증가는 스텝 풀 실패에 대한 임계 조건에 가까운 높은 흐름에서 점프에 대해 멈춥니다.

음의 경사면에서 발달된 곡물 20 클러스터와 같은 미세 지반은 국부 수력학에 상당한 영향을 주지만 이러한 영향은 수영장 바닥에서 억제됩니다. 스텝 스톤의 항력은 가장 높은 흐름이 사용되기 전에 배출과 함께 증가하는 반면 양력은 더 큰 크기와 더 넓은 범위를 갖습니다. 우리의 결과는 계단 풀 형태의 복잡한 흐름 특성을 조사할 때 물리적 및 수치적 모델링을 결합한 하이브리드 모델 접근 방식의 가능성과 큰 잠재력을 강조합니다.

Step-pool systems are common bedforms in mountain streams and have been utilized in river restoration projects around the world. Step-pool units exhibit highly non-uniform hydraulic characteristics which have been reported to closely 10 interact with the morphological evolution and stability of step-pool features. However, detailed information of the threedimensional hydraulics for step-pool morphology has been scarce due to the difficulty of measurement. To fill in this knowledge gap, we established a hybrid model based on the technologies of Structure from Motion (SfM) and computational fluid dynamics (CFD). The model used 3D reconstructions of bed surfaces with an artificial step-pool unit built by natural stones at six flow rates as inputs for CFD simulations. The hybrid model succeeded in providing high-resolution visualization 15 of 3D flow structures for the step-pool unit. The results illustrate the segmentation of flow regimes below the step, i.e., the integral jump at the water surface, streaky wake vortexes near the bed, and high-speed jets in between. The highly non-uniform distribution of turbulence energy in the pool has been revealed and two energy dissipaters with comparable capacity are found to co-exist in the pool. Pool scour development under flow increase leads to the expansion of the jump and wake vortexes but this increase stops for the jump at high flows close to the critical condition for step-pool failure. The micro-bedforms as grain 20 clusters developed on the negative slope affect the local hydraulics significantly but this influence is suppressed at pool bottom. The drag forces on the step stones increase with discharge before the highest flow is used while the lift force has a larger magnitude and wider varying range. Our results highlight the feasibility and great potential of the hybrid model approach combining physical and numerical modeling in investigating the complex flow characteristics of step-pool morphology.

Figure 1: Workflow of the hybrid modeling. SfM-MVS refers to the technology of Structure from Motion with Multi View Stereo. DSM is short for digital surface model. RNG-VOF is short for Renormalized Group (RNG) k-ε turbulence model coupled with Volume of Fluid method.
Figure 1: Workflow of the hybrid modeling. SfM-MVS refers to the technology of Structure from Motion with Multi View Stereo. DSM is short for digital surface model. RNG-VOF is short for Renormalized Group (RNG) k-ε turbulence model coupled with Volume of Fluid method.
Figure 2: Flume experiment settings in Zhang et al., (2020): (a) the artificially built-up step-pool model using natural stones, with stone number labelled; (b) the unsteady hydrograph of the run of CIFR (continually-increasing-flow-rate) T2 used in this study.
Figure 2: Flume experiment settings in Zhang et al., (2020): (a) the artificially built-up step-pool model using natural stones, with stone number labelled; (b) the unsteady hydrograph of the run of CIFR (continually-increasing-flow-rate) T2 used in this study.
Figure 3: Setup of the CFD model: (a) three-dimensional digital surface model (DSM) of the step-pool unit by structure from motion with multi view stereo (SfM-MVS) method as the input to the 3D computational fluid dynamics (CFD) modeling; (b) extruded bed 160 surface model connected to the extra downstream component (in purple blue) and rectangular columns to fill leaks (in green), with the boundary conditions shown on mesh planes; (c) recognized geometry with mesh grids of two mesh blocks shown where MS is short for mesh size; (d) sampling volumes to capture the flow forces acting on each step stone at X, Y, and Z directions; and (e) an example for the simulated 3D flow over the step-pool unit colored by velocity magnitude at the discharge of 49.9 L/s. The abbreviations for boundary conditions in (b) are: V for specified velocity; C for continuative; P for specific pressure; and W for wall 165 condition. The contraction section in Figure (e) refers to the edge between the jet and jump at water surface.
Figure 3: Setup of the CFD model: (a) three-dimensional digital surface model (DSM) of the step-pool unit by structure from motion with multi view stereo (SfM-MVS) method as the input to the 3D computational fluid dynamics (CFD) modeling; (b) extruded bed 160 surface model connected to the extra downstream component (in purple blue) and rectangular columns to fill leaks (in green), with the boundary conditions shown on mesh planes; (c) recognized geometry with mesh grids of two mesh blocks shown where MS is short for mesh size; (d) sampling volumes to capture the flow forces acting on each step stone at X, Y, and Z directions; and (e) an example for the simulated 3D flow over the step-pool unit colored by velocity magnitude at the discharge of 49.9 L/s. The abbreviations for boundary conditions in (b) are: V for specified velocity; C for continuative; P for specific pressure; and W for wall 165 condition. The contraction section in Figure (e) refers to the edge between the jet and jump at water surface.
Figure 4: Distribution of time-averaged velocity magnitude (VM_mean) and vectors in three longitudinal sections. The section at Y = 0 cm goes across the keystone while the other two (Y = -18 and 13.5 cm) are located at the step stones beside the keystone with 265 lower top elevations. Q refers to the discharge at the inlet of the computational domain. The spacing for X, Y, and Z axes are all 10 cm in the plots.
Figure 4: Distribution of time-averaged velocity magnitude (VM_mean) and vectors in three longitudinal sections. The section at Y = 0 cm goes across the keystone while the other two (Y = -18 and 13.5 cm) are located at the step stones beside the keystone with lower top elevations. Q refers to the discharge at the inlet of the computational domain. The spacing for X, Y, and Z axes are all 10 cm in the plots.
Figure 5: Distribution of time-averaged flow velocity at five cross sections which are set according to the reference section (x0). The reference cross section x0 is located at the downstream end of the keystone (KS). The five sections are located at 18 cm and 6 cm upstream of the reference section (x0-18 and x0-6), and 2 cm, 15 cm and 40 cm downstream of the reference section (x0+2, x0+15, x0+40). The spacing for X, Y, and Z axes are all 10 cm in the plots.
Figure 5: Distribution of time-averaged flow velocity at five cross sections which are set according to the reference section (x0). The reference cross section x0 is located at the downstream end of the keystone (KS). The five sections are located at 18 cm and 6 cm upstream of the reference section (x0-18 and x0-6), and 2 cm, 15 cm and 40 cm downstream of the reference section (x0+2, x0+15, x0+40). The spacing for X, Y, and Z axes are all 10 cm in the plots.
Figure 6: Distribution of the time-averaged turbulence kinetic energy (TKE) at the five cross sections same with Figure 3.
Figure 6: Distribution of the time-averaged turbulence kinetic energy (TKE) at the five cross sections same with Figure 3.
Figure 7: Boxplots for the distributions of the mass-averaged flow kinetic energy (KE, panels a-f), turbulence kinetic energy (TKE, panels g-l), and turbulent dissipation (εT, panels m-r) in the pool for all the six tested discharges (the plots at the same discharge are in the same row). The mass-averaged values were calculated every 2 cm in the streamwise direction. The flow direction is from left to right in all the plots. The general locations of the contraction section for all the flow rates are marked by the dashed lines, except for Q = 5 L/s when the jump is located too close to the step. The longitudinal distance taken up by negative slope in the pool for the inspected range is shown by shaded area in each plot.
Figure 7: Boxplots for the distributions of the mass-averaged flow kinetic energy (KE, panels a-f), turbulence kinetic energy (TKE, panels g-l), and turbulent dissipation (εT, panels m-r) in the pool for all the six tested discharges (the plots at the same discharge are in the same row). The mass-averaged values were calculated every 2 cm in the streamwise direction. The flow direction is from left to right in all the plots. The general locations of the contraction section for all the flow rates are marked by the dashed lines, except for Q = 5 L/s when the jump is located too close to the step. The longitudinal distance taken up by negative slope in the pool for the inspected range is shown by shaded area in each plot.
Figure 8: Instantaneous flow structures extracted using the Q-criterion (Qcriterion=1200) and colored by the magnitude of flow velocity.
Figure 8: Instantaneous flow structures extracted using the Q-criterion (Qcriterion=1200) and colored by the magnitude of flow velocity.
Figure 9: Time-averaged dynamic pressure (DP_mean) on the bed surface in the step-pool model under the two highest discharges, with the step numbers marked. The negative values in the plots result from the setting of standard atmospheric pressure = 0 Pa, whose absolute value is 1.013×105 Pa.
Figure 9: Time-averaged dynamic pressure (DP_mean) on the bed surface in the step-pool model under the two highest discharges, with the step numbers marked. The negative values in the plots result from the setting of standard atmospheric pressure = 0 Pa, whose absolute value is 1.013×105 Pa.
Figure 10: Time-averaged shear stress (SS_mean) on bed surface in the step-pool model, with the step numbers marked. The standard atmospheric pressure is set as 0 Pa.
Figure 10: Time-averaged shear stress (SS_mean) on bed surface in the step-pool model, with the step numbers marked. The standard atmospheric pressure is set as 0 Pa.
Figure 11: Variation of fluid force components and magnitude of resultant flow force acting on step stones with flow rate. The stone 4 is the keystone. Stone numbers are consistent with those in Fig. 9-10. The upper limit of the sampling volumes for flow force calculation is higher than water surface while the lower limit is set at 3 cm lower than the keystone crest.
Figure 11: Variation of fluid force components and magnitude of resultant flow force acting on step stones with flow rate. The stone 4 is the keystone. Stone numbers are consistent with those in Fig. 9-10. The upper limit of the sampling volumes for flow force calculation is higher than water surface while the lower limit is set at 3 cm lower than the keystone crest.
Figure 12: Variation of drag (CD) and lift (CL) coefficient of the step stones along with flow rate. Stone numbers are consistent with those in Fig. 8-9. KS is short for keystone. The negative values of CD correspond to the drag forces towards the upstream while the negative values of CL correspond to lift forces pointing downwards.
Figure 12: Variation of drag (CD) and lift (CL) coefficient of the step stones along with flow rate. Stone numbers are consistent with those in Fig. 8-9. KS is short for keystone. The negative values of CD correspond to the drag forces towards the upstream while the negative values of CL correspond to lift forces pointing downwards.
Figure 13: Longitudinal distributions of section-averaged and -integral turbulent kinetic energy (TKE) for the jump and wake vortexes at the largest three discharges. The flow direction is from left to right in all the plots. The general locations of the contraction sections under the three flow rates are marked by dashed lines in figures (d) to (f).
Figure 13: Longitudinal distributions of section-averaged and -integral turbulent kinetic energy (TKE) for the jump and wake vortexes at the largest three discharges. The flow direction is from left to right in all the plots. The general locations of the contraction sections under the three flow rates are marked by dashed lines in figures (d) to (f).
Figure A1: Water surface profiles of the simulations with different mesh sizes at the discharge of 43.6 L/s at the longitudinal sections at: (a) Y = 24.5 cm (left boundary); (b) Y = 0.3 cm (middle section); (c) Y = -24.5 cm (right boundary). MS is short for mesh size. The flow direction is from left to right in each plot.
Figure A1: Water surface profiles of the simulations with different mesh sizes at the discharge of 43.6 L/s at the longitudinal sections at: (a) Y = 24.5 cm (left boundary); (b) Y = 0.3 cm (middle section); (c) Y = -24.5 cm (right boundary). MS is short for mesh size. The flow direction is from left to right in each plot.
Figure A2: Contours of velocity magnitude in the longitudinal section at Y = 0 cm at different mesh sizes (MSs) under the flow condition with the discharge of 43.6 L/s: (a) 0.50 cm; (b) 0.375 cm; (c) 0.30 cm; (d) 0.27 cm; (e) 0.25 cm; (f) 0.24 cm. The flow direction is from left to right.
Figure A2: Contours of velocity magnitude in the longitudinal section at Y = 0 cm at different mesh sizes (MSs) under the flow condition with the discharge of 43.6 L/s: (a) 0.50 cm; (b) 0.375 cm; (c) 0.30 cm; (d) 0.27 cm; (e) 0.25 cm; (f) 0.24 cm. The flow direction is from left to right.
Figure A3: Measurements of water surfaces (orange lines) used in model verification: (a) water surface profiles from both sides of the flume; (b) upstream edge of the jump regime from top view. KS refers to keystone in figure (b).
Figure A3: Measurements of water surfaces (orange lines) used in model verification: (a) water surface profiles from both sides of the flume; (b) upstream edge of the jump regime from top view. KS refers to keystone in figure (b).
Figure A15. Figure (a) shows the locations of the cross sections and target coarse grains at Q = 49.9 L/s. Figures (b) to (e) show the distribution of velocity magnitude (VM_mean) in the four chosen cross sections: (a) x0+8.0; (b) x0+14.0; (c) x0+21.5; (d) x0+42.5. G1 to G6 refer to 6 protruding grains in the micro-bedforms in the pool.
Figure A15. Figure (a) shows the locations of the cross sections and target coarse grains at Q = 49.9 L/s. Figures (b) to (e) show the distribution of velocity magnitude (VM_mean) in the four chosen cross sections: (a) x0+8.0; (b) x0+14.0; (c) x0+21.5; (d) x0+42.5. G1 to G6 refer to 6 protruding grains in the micro-bedforms in the pool.
Figure A16. The distribution of turbulent kinetic energy (TKE) in the same cross sections as in figure S15: (a) x0+8.0; (b) x0+14.0; (c) x0+21.5; (d) x0+42.5.
Figure A16. The distribution of turbulent kinetic energy (TKE) in the same cross sections as in figure S15: (a) x0+8.0; (b) x0+14.0; (c) x0+21.5; (d) x0+42.5.

References

720 Aberle, J. and Smart, G. M: The influence of roughness structure on flow resistance on steep slopes, J. Hydraul. Res., 41(3),
259-269, https://doi.org/10.1080/00221680309499971, 2003.
Abrahams, A. D., Li, G., and Atkinson, J. F.: Step-pool streams: Adjustment to maximum flow resistance. Water Resour. Res.,
31(10), 2593-2602, https://doi.org/10.1029/95WR01957, 1995.
Adrian, R. J.: Twenty years of particle image velocimetry. Exp. Fluids, 39(2), 159-169, https://doi.org/10.1007/s00348-005-
725 0991-7 2005.
Chanson, H.: Hydraulic design of stepped spillways and downstream energy dissipators. Dam Eng., 11(4), 205-242, 2001.
Chartrand, S. M., Jellinek, M., Whiting, P. J., and Stamm, J.: Geometric scaling of step-pools in mountain streams:
Observations and implications, Geomorphology, 129(1-2), 141-151, https://doi.org/10.1016/j.geomorph.2011.01.020,
2011.
730 Chen, Y., DiBiase, R. A., McCarroll, N., and Liu, X.: Quantifying flow resistance in mountain streams using computational
fluid dynamics modeling over structure‐from‐motion photogrammetry‐derived microtopography, Earth Surf. Proc.
Land., 44(10), 1973-1987, https://doi.org/10.1002/esp.4624, 2019.
Church, M. and Zimmermann, A.: Form and stability of step‐pool channels: Research progress, Water Resour. Res., 43(3),
W03415, https://doi.org/10.1029/2006WR005037, 2007.
735 Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., and Ranzuglia, G.: Meshlab: an open-source mesh
processing tool, in: Eurographics Italian chapter conference, Salerno, Italy, 2-4 July 2008, 129-136, 2008.

Comiti, F., Andreoli, A., and Lenzi, M. A.: Morphological effects of local scouring in step-pool streams, Earth Surf. Proc.
Land., 30(12), 1567-1581, https://doi.org/10.1002/esp.1217, 2005.
Comiti, F., Cadol, D., and Wohl, E.: Flow regimes, bed morphology, and flow resistance in self‐formed step-pool
740 channels, Water Resour. Res., 45(4), 546-550, https://doi.org/10.1029/2008WR007259, 2009.
Dudunake, T., Tonina, D., Reeder, W. J., and Monsalve, A.: Local and reach‐scale hyporheic flow response from boulder ‐
induced geomorphic changes, Water Resour. Res., 56, e2020WR027719, https://doi.org/10.1029/2020WR027719, 2020.
Flow Science.: Flow-3D Version 11.2 User Manual, Flow Science, Inc., Los Alamos, 2016.
Gibson, S., Heath, R., Abraham, D., and Schoellhamer, D.: Visualization and analysis of temporal trends of sand infiltration
745 into a gravel bed, Water Resour. Res., 47(12), W12601, https://doi.org/10.1029/2011WR010486, 2011.
Hassan, M. A., Tonina, D., Beckie, R. D., and Kinnear, M.: The effects of discharge and slope on hyporheic flow in step‐pool
morphologies, Hydrol. Process., 29(3), 419-433, https://doi.org/10.1002/hyp.10155, 2015.
Hirt, C. W. and Nichols, B. D.: Volume of Fluid (VOF) method for the dynamics of free boundaries. J. Comput. Phys., 39,
201-225, https://doi.org/10.1016/0021-9991(81)90145-5, 1981.
750 Javernick L., Brasington J., and Caruso B.: Modeling the topography of shallow braided rivers using structure-from-motion
photogrammetry, Geomorphology, 213(4), 166-182, https://doi.org/10.1016/j.geomorph.2014.01.006, 2014.
Lai, Y. G., Smith, D. L., Bandrowski, D. J., Xu, Y., Woodley, C. M., and Schnell, K.: Development of a CFD model and
procedure for flows through in-stream structures, J. Appl. Water Eng. Res., 1-15,
https://doi.org/10.1080/23249676.2021.1964388, 2021.
755 Lenzi, M. A.: Step-pool evolution in the Rio Cordon, northeastern Italy, Earth Surf. Proc. Land., 26(9), 991-1008,
https://doi.org/10.1002/esp.239, 2001.
Lenzi, M. A.: Stream bed stabilization using boulder check dams that mimic step-pool morphology features in Northern
Italy, Geomorphology, 45(3-4), 243-260, https://doi.org/10.1016/S0169-555X(01)00157-X, 2002.
Lenzi, M. A., Marion, A., and Comiti, F.: Local scouring at grade‐control structures in alluvial mountain rivers, Water Resour.
760 Res., 39(7), 1176, https://doi:10.1029/2002WR001815, 2003.
Li, W., Wang Z., Li, Z., Zhang, C., and Lv, L.: Study on hydraulic characteristics of step-pool system, Adv. Water Sci., 25(3),
374-382, https://doi.org/10.14042/j.cnki.32.1309.2014.03.012, 2014. (In Chinese with English abstract)
Maas, H. G., Gruen, A., and Papantoniou, D.: Particle tracking velocimetry in three-dimensional flows, Exp. Fluids, 15(2),
133-146. https://doi.org/10.1007/BF00223406, 1993.

765 Montgomery, D. R. and Buffington, J. M.: Channel-reach morphology in mountain drainage basins, Geol. Soc. Am. Bul., 109(5), 596-611, https://doi.org/10.1130/0016-7606(1997)109<0596:CRMIMD>2.3.CO;2, 1997. Morgan J. A., Brogan D. J., and Nelson P. A.: Application of structure-from-motion photogrammetry in laboratory flumes, Geomorphology, 276(1), 125-143, https://doi.org/10.1016/j.geomorph.2016.10.021, 2017. Recking, A., Leduc, P., Liébault, F., and Church, M.: A field investigation of the influence of sediment supply on step-pool 770 morphology and stability. Geomorphology, 139, 53-66, https://doi.org/10.1016/j.geomorph.2011.09.024, 2012. Roth, M. S., Jähnel, C., Stamm, J., and Schneider, L. K.: Turbulent eddy identification of a meander and vertical-slot fishways in numerical models applying the IPOS-framework, J. Ecohydraulics, 1-20, https://doi.org/10.1080/24705357.2020.1869916, 2020. Saletti, M. and Hassan, M. A.: Width variations control the development of grain structuring in steep step‐pool dominated 775 streams: insight from flume experiments, Earth Surf. Proc. Land., 45(6), 1430-1440, https://doi.org/10.1002/esp.4815, 2020. Smith, D. P., Kortman, S. R., Caudillo, A. M., Kwan‐Davis, R. L., Wandke, J. J., Klein, J. W., Gennaro, M. C. S., Bogdan, M. A., and Vannerus, P. A.: Controls on large boulder mobility in an ‘auto-naturalized’ constructed step-pool river: San Clemente Reroute and Dam Removal Project, Carmel River, California, USA, Earth Surf. Proc. Land., 45(9), 1990-2003, 780 https://doi.org/10.1002/esp.4860, 2020. Thappeta, S. K., Bhallamudi, S. M., Fiener, P., and Narasimhan, B.: Resistance in Steep Open Channels due to Randomly Distributed Macroroughness Elements at Large Froude Numbers, J. Hydraul. Eng., 22(12), 04017052, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001587, 2017. Thappeta, S. K., Bhallamudi, S. M., Chandra, V., Fiener, P., and Baki, A. B. M.: Energy loss in steep open channels with step785 pools, Water, 13(1), 72, https://doi.org/10.3390/w13010072, 2021. Turowski, J. M., Yager, E. M., Badoux, A., Rickenmann, D., and Molnar, P.: The impact of exceptional events on erosion, bedload transport and channel stability in a step-pool channel, Earth Surf. Proc. Land., 34(12), 1661-1673, https://doi.org/10.1002/esp.1855, 2009. Vallé, B. L. and Pasternack, G. B.: Air concentrations of submerged and unsubmerged hydraulic jumps in a bedrock step‐pool 790 channel, J. Geophys. Res.-Earth, 111(F3), F03016. https://doi:10.1029/2004JF000140, 2006. Waldon, M. G.: Estimation of average stream velocity, J. Hydraul. Eng., 130(11), 1119-1122. https://doi.org/10.1061/(ASCE)0733-9429(2004)130:11(1119), 2004. Wang, Z., Melching, C., Duan, X., and Yu, G.: Ecological and hydraulic studies of step-pool systems, J. Hydraul. Eng., 135(9), 705-717, https://doi.org/10.1061/(ASCE)0733-9429(2009)135:9(705), 2009

795 Wang, Z., Qi, L., and Wang, X.: A prototype experiment of debris flow control with energy dissipation structures, Nat. Hazards, 60(3), 971-989, https://doi.org/10.1007/s11069-011-9878-5, 2012. Weichert, R. B.: Bed Morphology and Stability in Steep Open Channels, Ph.D. Dissertation, No. 16316. ETH Zurich, Switzerland, 247pp., 2005. Wilcox, A. C., Wohl, E. E., Comiti, F., and Mao, L.: Hydraulics, morphology, and energy dissipation in an alpine step‐pool 800 channel, Water Resour. Res., 47(7), W07514, https://doi.org/10.1029/2010WR010192, 2011. Wohl, E. E. and Thompson, D. M.: Velocity characteristics along a small step–pool channel. Earth Surf. Proc. Land., 25(4), 353-367, https://doi.org/10.1002/(SICI)1096-9837(200004)25:4<353::AID-ESP59>3.0.CO;2-5, 2000. Wu, S. and Rajaratnam, N.: Impinging jet and surface flow regimes at drop. J. Hydraul. Res., 36(1), 69-74, https://doi.org/10.1080/00221689809498378, 1998. 805 Xu, Y. and Liu, X.: 3D computational modeling of stream flow resistance due to large woody debris, in: Proceedings of the 8th International Conference on Fluvial Hydraulics, St. Louis, USA, 11-14, Jul, 2346-2353, 2016. Xu, Y. and Liu, X.: Effects of different in-stream structure representations in computational fluid dynamics models—Taking engineered log jams (ELJ) as an example, Water, 9(2), 110, https://doi.org/10.3390/w9020110, 2017. Zeng, Y. X., Ismail, H., and Liu, X.: Flow Decomposition Method Based on Computational Fluid Dynamics for Rock Weir 810 Head-Discharge Relationship. J. Irrig. Drain. Eng., 147(8), 04021030, https://doi.org/10.1061/(ASCE)IR.1943- 4774.0001584, 2021. Zhang, C., Wang, Z., and Li, Z.: A physically-based model of individual step-pool stability in mountain streams, in: Proceedings of the 13th International Symposium on River Sedimentation, Stuttgart, Germany, 801-809, 2016. Zhang, C., Xu, M., Hassan, M. A., Chartrand, S. M., and Wang, Z.: Experimental study on the stability and failure of individual 815 step-pool, Geomorphology, 311, 51-62, https://doi.org/10.1016/j.geomorph.2018.03.023, 2018. Zhang, C., Xu, M., Hassan, M. A., Chartrand, S. M., Wang, Z., and Ma, Z.: Experiment on morphological and hydraulic adjustments of step‐pool unit to flow increase, Earth Surf. Proc. Land., 45(2), 280-294, https://doi.org/10.1002/esp.4722, 2020. Zimmermann A., E.: Flow resistance in steep streams: An experimental study, Water Resour. Res., 46, W09536, 820 https://doi.org/10.1029/2009WR007913, 2010. Zimmermann A. E., Salleti M., Zhang C., Hassan M. A.: Step-pool Channel Features, in: Treatise on Geomorphology (2nd Edition), vol. 9, Fluvial Geomorphology, edited by: Shroder, J. (Editor in Chief), Wohl, E. (Ed.), Elsevier, Amsterdam, Netherlands, https://doi.org/10.1016/B978-0-12-818234-5.00004-3, 2020.

Numerical study of the effect of flow velocity and flood roughness components on hydraulic flow performance in composite sections with converging floodplains

Numerical study of the effect of flow velocity and flood roughness components on hydraulic flow performance in composite sections with converging floodplains

Authors

1 Civil Enigneering Department, Lahijan Branch.Islamic Azad University.Lahijan.Iran

2 Department of Civil Engnieering, University of Qom,Qom,Iran

3 Civil Engineering Department, Lahijan Branch,Islamic Azad Univeristy,Lahijan,Iran

Abstract

홍수와 그 위험을 통제해야 할 필요성은 누구에게도 숨겨져 있지 않습니다. 또한 이 현상으로 인해 다양한 경제, 사회 및 환경 문제가 영향을 받습니다. 홍수 제어 방법의 설계 및 최적 관리의 첫 번째 단계는 홍수 중 하천 거동을 올바르게 식별하는 것입니다.

홍수 경로 지정, 하상 및 하천 면적 결정 등과 같은 대부분의 하천 엔지니어링 프로젝트에서 하천 단면의 수리학적 매개변수의 평균값을 계산하는 것으로 충분합니다. 오늘날 유체 환경 연구에서 수치 및 분석 방법의 사용이 성장하고 발전했습니다.

신뢰할 수 있는 결과 생성으로 인해 물리적 모델에 대한 좋은 대안이 될 수 있었습니다. 오늘날 수치 모델의 급속한 발전과 컴퓨터 계산 속도의 증가로 인해 3D 수치 모델의 사용이 선호되며 또한 강의 속도 분포 및 전단 응력을 측정하는 데 시간이 많이 걸리고 비용이 많이 들기 때문에 결과 3D 수치 모델의 가치가 있을 것입니다.

한편, 본 연구에서는 복합단면에 대해 FLOW-3D 모델을 이용한 종합적인 수치연구가 이루어지지 않았음을 보여주고 있어 적절한 연구기반을 제공하고 있습니다.

따라서 본 연구의 혁신은 발산 및 수렴 범람원을 동반하는 비 각형 복합 단면에서 흐름의 상태 및 수리 성능에 대한 거칠기와 같은 매개 변수의 영향에 대한 수치 연구입니다.

수치해석 결과를 검증하기 위해 Younesi(2013) 연구를 이용하였습니다. 이 실험에서는 먼저 고정층이 있는 복합 프리즘 및 비 프리즘 단면의 수리 흐름을 조사한 다음 조건을 유지하면서 프리즘 및 비 프리즘 모드에서 퇴적물 이동 실험을 수행했습니다.

실험은 15미터 길이의 연구 채널에서 수행되었습니다. 이 운하는 초당 250리터의 시스템에서 재순환을 위해 제공될 수 있는 유속과 0.0088 000의 종경사를 가진 폭 400mm의 두 개의 대칭 범람원이 있는 합성 운하입니다. 범람원의 가장자리는 0.18미터와 같고 주요 운하의 너비는 0.4미터와 같습니다(그림 1).

본수로의 바닥과 벽을 거칠게 하기 위해 평균직경 0.65mm의 퇴적물을 사용하였으며, 각 단계에서 범람원의 벽과 바닥은 평균직경 0.65, 1.3, 1.78의 퇴적물로 거칠게 하였습다. (mm). 삼각형 오버플로는 운하 상류에서 운하로의 유입량을 측정하는 데 사용됩니다.

상대깊이 0.15와 0.25, 직경 14mm의 마이크로몰리나 실험과 상대깊이 0.35의 실험에서는 유속을 측정하기 위해 3차원 속도계(ADV)를 사용하였습니다. 수위는 0.1mm의 정확도로 깊이 게이지로 측정 되었습니다.

본 연구에서는 수면 프로파일의 수치적 모델을 검증하기 위해 실험 0.25-2에서 발산대의 시작, 중간 및 끝에서 세 단면의 평균 깊이 속도 분포 및 경계 전단 응력) -11.3-NP 및 0.25-2-5.7-NP 및 또한 각형 복합 단면의 0.25-2-2 P 테스트가 평가되었습니다.

각형 합성 단면의 P.20-2-2-P 테스트와 관련된 RMSE 및 NRMSE 지수 값 및 표 (2) 실험 11.3에서 RMSE 및 NRMSE 지수 값 -2-0.25-NP 및 -0.25. 2-5.7-NP가 제공됩니다. 실험 0.25-2-5.7-NP-11.3-2-0.25, NP 및 P.2.0-2-2-P의 평균 깊이 속도의 검증과 관련된 결과가 표시됩니다. 0.25-2-5.7-NP 실험에서 초, 중, 기말 NRMSE의 양은 각각 5.7, 11.8, 10.3%로 계산되었으며, 이는 초급이 우수, 중급이 양호, 최종 성적. 배치. 보시다시피, RMSE 값은 각각 0.026, 0.037 및 0.026으로 계산됩니다.

실험 11.3-2-0.25, NP에서 초급, 중급 및 최종 수준의 NRMSE 값은 각각 7, 11.2 및 15.4%로 계산되었으며, 이는 초급에서 우수 범주 및 우수 범주에서 중간 및 최종 수준. 가져 가다. 보시다시피, RMSE 값은 각각 0.032, 0.038, 0.04로 계산됩니다. 0.25-2-P 실험에서 NRMSE 값은 1.7%로 계산되어 우수 범주에 속한다. 보시다시피 RMSE 값도 0.004로 계산됩니다. 중간 깊이의 속도 분포와 관련하여 수치 모델은 실험실 결과에 적합하며 접합 영역에 작은 오류만 입력되었다고 말할 수 있습니다. 이는 2차 전지의 이동 결과로 간주될 수 있습니다. 모서리를 향해.
결론: 본 연구에서는 3차원 유동 해석이 가능한 Flow 3D 소프트웨어를 사용하여 각형 및 비각형 단면이 복합된 수로의 유동 패턴을 조사했습니다. 3개의 다른 상대 거칠기(1, 2 및 2.74)와 3개의 상대 깊이(0.15, 0.25 및 0.35) 및 5.7 및 11.3도의 발산 각도에 대해 속도의 세로 성분 변화, 평균 깊이 속도 분포, 경계 범람원에 의해 전달되는 유속뿐만 아니라 전단 응력 분포를 조사했습니다.

결과는 수로를 따라 범람원의 폭이 증가함에 따라 유속량이 감소함을 보여주었다. 또한 조도가 유동패턴에 미치는 영향에 대한 연구는 일반적으로 벽의 거칠기에 따라 모든 구간에서 유속량이 감소하는 것으로 나타났으며, 또한 본관과 범람원의 교차점에서의 유동패턴은 벽의 거칠기 영향을 더 많이 받는 것으로 나타났습니다. 결과는 또한 상대 깊이가 증가하거나 상대 거칠기가 감소함에 따라 주 수로와 범람원 사이의 속도 구배가 감소함을 보여주었습니다.

Intrpduction: The need to control floods and their dangers is not hidden from anyone. In addition, a wide range of economic, social and environmental issues are affected by this phenomenon. The first step in the design and optimal management of flood control methods is the correct identification of river behavior during floods. In most river engineering projects such as flood routing, determining the bed and river area, etc., calculating the average values of hydraulic parameters of the river section is sufficient. Today, the use of numerical and analytical methods in the study of fluid environment have grown and developed. Due to the production of reliable results, they have been able to be a good alternative to physical models. Today, with the rapid development of numerical models and increasing the speed of computer calculations, the use of 3D numerical models is preferred and also due to the fact that measuring the velocity distribution and shear stress in rivers is very time consuming and expensive, the results of 3D numerical models It will be valuable. On the other hand, the present studies show that comprehensive numerical research using FLOW-3D model has not been performed on composite sections, so a suitable ground for research is provided. Therefore, the innovation of the present study is the numerical study of the effects of parameters such as roughness on the status and hydraulic performance of the flow in non-prismatic composite sections, which are accompanied by divergent and convergent floodplains, which have received less attention numerically.

Methodology: Younesi (2013) research has been used to validate the results of numerical simulation. In these experiments, first the hydraulic flow in composite prismatic and non-prismatic sections with fixed bed was examined and then, while maintaining the conditions, sediment transfer experiments were performed in prismatic and non-prismatic mode. The experiments were performed in a research channel 15 meters long. This canal is a composite canal with two symmetrical floodplains with a width of 400 mm with a flow rate that can be provided for recirculation in the system of 250 liters per second and a longitudinal slope of 0.0088 000. The depth of the main canal to the edge of the floodplain is equal to 0.18 meters and the width of the main canal is equal to 0.4 meters (Figure 1). In order to roughen the bed and walls of the main canal, sediments with an average diameter of 0.65 mm have been used and at each stage, the walls and bed of floodplains have been roughened by sediments with an average diameter of 0.65, 1.3 and 1.78 (mm). A triangular overflow is used to measure the inflow to the canal, upstream of the canal. In order to measure the flow velocity in experiments with relative depth of 0.15 and 0.25, a micromolina with a diameter of 14 mm and in experiments with relative depth of 0.35, a three-dimensional speedometer (ADV) was used. The water level was also taken by depth gauges with an accuracy of 0.1 mm.
Result and Diccussion: In the present study, in order to validate the numerical model of water surface profile, average depth velocity distribution and boundary shear stress in the three sections at the beginning, middle and end of the divergence zone) in experiments 0.25-2-11.3-NP and 0.25-2-5.7-NP and Also, the 0.25-2-2 P test of the prismatic composite section has been evaluated. In Table (1) the values of RMSE and NRMSE indices related to the P.20-2-2-P test of the prismatic composite section, and also in Table (2) the values of the RMSE and NRMSE indices in the experiments 11.3-2-0.25-NP and -0.25. 2-5.7-NP is provided. The results related to the validation of the average depth velocity of the experiments 0.25-2-5.7- NP-11.3-2-0.25, NP and P.2.0-2-2-P are shown. In 0.25-2-5.7-NP experiment, the amount of NRMSE in elementary, middle and final grades was calculated to be 5.7, 11.8 and 10.3%, respectively, which is in the excellent grade in the elementary grade and good in the middle and final grades. Placed. As can be seen, the RMSE values are calculated as 0.026, 0.037 and 0.026, respectively. In the experiment 11.3-2-0.25, NP, the NRMSE values in the primary, middle and final levels were calculated as 7, 11.2 and 15.4%, respectively, which are in the excellent category in the primary level and in the good category in the middle and final levels. Take. As can be seen, the RMSE values are calculated as 0.032, 0.038 and 0.04, respectively. In the 0.25-2-P experiment, the NRMSE value was calculated to be 1.7%, which is in the excellent category. As can be seen, the RMSE value is also calculated to be 0.004. Regarding the medium-depth velocity distribution, it can be said that the numerical model has an acceptable compliance with the laboratory results and only a small error has been entered in the junction area, which can be considered as a result of the movement of secondary cells towards the corners.
Conclusion: in this research The flow pattern in waterways with composite prismatic and non-prismatic sections was investigated using Flow 3D software that is capable of three-dimensional flow analysis. For three different relative roughnesses (1, 2 and 2.74) as well as three relative depths (0.15, 0.25 and 0.35) and divergence angles of 5.7 and 11.3 degrees, changes in the longitudinal component of velocity, The average depth velocity distribution, the boundary shear stress distribution as well as the flow rate transmitted by the floodplains were investigated. The results showed that with increasing the width of floodplains along the canal, the amount of velocity decreases. Also, the study of the effect of roughness on the flow pattern showed that in general, with wall roughness, the amount of velocity has decreased in all sections and also the flow pattern at the junction of the main canal and floodplain is more affected by wall roughness. The results also showed that with increasing relative depth or decreasing relative roughness, the velocity gradient between the main channel and floodplains decreases

Keywords

그림 1 하천횡단구조물 하류부 횡단구조물 파괴

유입조건에 따른압력변이로 인한하천횡단구조물 하류물받이공 및 바닥보호공설계인자 도출최종보고서

주관연구기관 / 홍익대학교 산학협력단
공동연구기관 / 한국건설기술연구원
공동연구기관 / 주식회사 지티이

연구의 목적 및 내용

하천횡단구조물이 하천설계기준(2009)대로 설계되었음에도 불구하고, 하류부에서 물받이공 및 바닥보호공의 피해가 발생하여, 구조물 본체에 대한 안전성이 현저하 게 낮아지고 있는 실정이다. 하천설계기준이 상류부의 수리특성을 반영하였다고 하나 하류부의 수리특성인 유속의 변동 성분 또는 압력의 변동성분까지 고려하고 있지는 않다. 현재 많은 선행연구에서 이러한 난류적 특성이 구조물에 미치는 영 향에 대해 제시하고 있는 실정이며, 국내 하천에서의 피해 또한 이와 관련이 있다 고 판단된다. 이에 본 연구에서는 난류성분 특히 압력의 변동성분이 물받이공과 바닥보호공에 미치는 영향을 정량적으로 분석하여, 하천 횡단구조물의 치수 안전 성 증대에 기여하고자 한다. 물받이공과 바닥보호공에 미치는 압력의 변동성분 (pressure fluctuation) 영향을 분석하기 위해 크게 3가지로 연구내용을 분류하였 다. 첫 번째는 압력의 변동으로 순간적인 음압구배(adversed pressure gradient) 가 발생할 경우 바닥보호공의 사석 및 블록이 이탈하는 것이다. 이를 확인하기 위 해 정밀한 압력 측정장치를 통해 압력변이를 측정하여, 사석의 이탈 가능성을 검 토할 것이며, 최종적으로 이탈에 대한 한계조건을 도출할 것이다. 두 번째는 압력 의 변동이 물받이공의 진동을 유발시켜 이를 지지하고 있는 지반에 다짐효과를 가 져와 물받이공과 지반사이에 공극이 발생하는 경우이다. 이러한 공극으로 물받이 공은 자중 및 물의 압력을 받게 되어, 결국 휨에 의한 파괴가 발생할 가능성이 있 게 된다. 본 연구에서는 실험을 통하여 압력의 변동과 물받이공의 진동을 동시에 측정하여, 진동이 발생하지 않을 최소 두께를 제시할 것이다. 세 번째는 압력변이 로 인한 물받이공의 진동이 피로파괴로 연결되는 경우이다. 이 현상 또한 수리실 험을 통해 압력변이-피로파괴의 관계를 정량적으로 분석하여, 한계 조건을 제시할 것이다. 본 연구는 국내 보 및 낙차공에서 발생하는 다양한 Jet의 특성을 수리실 험으로 재현해야 하며, 이를 위해 평면 Jet 분사기(plane Jet injector)를 고안/ 제작하여, 효율적인 수리실험을 수행할 것이다. 또한 3차원 수치해석을 통해 실제 스케일에 적용함으로써 연구결과의 활용도 및 적용성을 높이고자 한다.

Keywords

압력변이, 물받이공, 바닥보호공, 난류, 진동

 그림 1 하천횡단구조물 하류부 횡단구조물 파괴
그림 1 하천횡단구조물 하류부 횡단구조물 파괴
그림 2. 시간에 따른 압력의 변동 양상 및 정의
그림 2. 시간에 따른 압력의 변동 양상 및 정의
 그림 3. 하천횡단구조물 하류부 도수현상시 발생하는 압력변이 분포도, Fr=8.0 상태이며, 바닥(slab)에 양압과 음압이 지속적으로 작용한다. (Fiorotto & Rinaldo, 2010)
그림 3. 하천횡단구조물 하류부 도수현상시 발생하는 압력변이 분포도, Fr=8.0 상태이며, 바닥(slab)에 양압과 음압이 지속적으로 작용한다. (Fiorotto & Rinaldo, 2010)
 그림 4. 파괴 개념
그림 4. 파괴 개념
그림 6. PIV 측정 원리(www.photonics.com)
그림 6. PIV 측정 원리(www.photonics.com)
그림 7. LED회로판 및 BIV기법 기본개념
그림 7. LED회로판 및 BIV기법 기본개념
그림 8. BIV측정기법을 적용한 순간이미지 (Lin et al., 2012)
그림 8. BIV측정기법을 적용한 순간이미지 (Lin et al., 2012)
그림 9. 감세공의 분류
그림 9. 감세공의 분류
그림 17 수리실헐 수로시설: (a) 전체수로전경, (b) Weir 보를 포함한 측면도, (c) 도수조건 실험전경
그림 17 수리실헐 수로시설: (a) 전체수로전경, (b) Weir 보를 포함한 측면도, (c) 도수조건 실험전경
그림 18 수리실험 개요도
그림 18 수리실험 개요도
그림 127 난류모형별 압력 Data (측정위치는 그림 125 참조)
그림 127 난류모형별 압력 Data (측정위치는 그림 125 참조)
그림 128 RNG 모형을 이용한 수치모의 결과
그림 128 RNG 모형을 이용한 수치모의 결과
그림 129 LES 모형을 이용한 수치모의 결과
그림 129 LES 모형을 이용한 수치모의 결과
그림 130 압력 Data의 필터링
그림 130 압력 Data의 필터링
그림 134 Case 1의 흐름특성 분포도 및 그래프
그림 134 Case 1의 흐름특성 분포도 및 그래프

참고문헌

국토기술연구센터 (1998) 하상유지공의 구조설계 지침.

감사원 (2013) 감사원 결과보고서- 4대강살리기 사업 주요시설물 품질 밑 수질관리 실태.

국토해양부 (2009) 전국 하천횡단 구조물 설치현황 및 어도 실태조사 보고서. 국토해양부 (2010). 낙동강 살리기 사업 24공구(성주칠곡지구) 실시설계보고서.

국토해양부 (2012) 보도자료-준공대비 점검결과, 4대강 보 안전 재확인.

국토해양부 (2012) 국가 및 지방하천 종합정비 마스터플랜.

국토교통성 (2008) 하천사방기술기준.

농림부 (1996). 농업생산기반정비사업계획 설계기준. 류권규(역자) (2009). 난류의 수치모의(원저자 : 梶島岳夫, 1999).

류권규, 마리안 머스테, 로버트 에테마, 윤병만 (2006). “난류 중 부유사의 속도 지체 측정.” 한국수자원학회논문집, 제39권, 제2호, pp.99-108.

배재현, 이경훈, 신종근, 양용수, 이주희 (2011). “입자영상유속계를 이용한 은어의 유영능력 측정.” 제47권, 제4호, pp.411-418.

우효섭 (2001). 하천수리학. 청문각.

한국수자원학회 (2009). 하천설계기준해설.

한국건설기술연구원 (2014) 입자영상유속계(PIV)를 이용한 하천구조물 주변 유동해석 기법 개발

한국건설기술연구원 (2017) 보와 하상유지공의 안전성 확보를 위한 물받이와 바닥보호공의 성능평가
기법에 대한 원천기술개발

국토기술연구센터 (1998) 하상유지공의 구조설계 지침.

감사원 (2013) 감사원 결과보고서- 4대강살리기 사업 주요시설물 품질 밑 수질관리 실태. 국토해양부 (2009) 전국 하천횡단 구조물 설치현황 및 어도 실태조사 보고서.

국토해양부 (2012) 보도자료-준공대비 점검결과, 4대강 보 안전 재확인. 국토해양부 (2012) 국가 및 지방하천 종합정비 마스터플랜.

국토교통성 (2008) 하천사방기술기준.

농림부 (1996). 농업생산기반정비사업계획 설계기

류권규(역자) (2009). 난류의 수치모의(원저자 : 梶島岳夫, 1999).
류권규, 마리안 머스테, 로버트 에테마, 윤병만 (2006). “난류 중 부유사의 속도 지체 측정.” 한국수자원학회논문집, 제39권, 제2호, pp.99-108.
배재현, 이경훈, 신종근, 양용수, 이주희 (2011). “입자영상유속계를 이용한 은어의 유영능력 측정.” 제47권, 제4호, pp.411-418.
우효섭 (2001). 하천수리학. 청문각. 한국수자원학회 (2009). 하천설계기준해설. 한국건설기술연구원 (2014) 입자영상유속계(PIV)를 이용한 하천구조물 주변 유동해석 기법 개발
한국건설기술연구원 (2017) 보와 하상유지공의 안전성 확보를 위한 물받이와 바닥보호공의 성능평가
기법에 대한 원천기술개발

Adrian, R. J., Meinhart, C. D., & Tomkins, C. D. (2000). Vortex organization in the outer
region of the turbulent boundary layer. Journal of Fluid Mechanics, 422, 1-54.
Anderson, T. W., & Darling, D. A. (1954). A test of goodness of fit. Journal of the American
statistical association, 49(268), 765-769.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy. The journal of finance, 23(4), 589-609.
Barjastehmaleki, S., Fiorotto, V., & Caroni, E. (2016). Spillway stilling basins lining design
via Taylor hypothesis. Journal of Hydraulic Engineering, 142(6), 04016010.
Beheshti, M. R., Khosrojerdi, A., & Borghei, S. M. (2013). Experimental study of air-water
turbulent flow structures on stepped spillways. International Journal of Physical Sciences,
8(25), 1362-1370.
Bligh, W. G. (1910). Dams, barrages and weirs on porous foundations. Engineering News, 64(26),
708-710.
Bowers, C. E., &Tsai, F. Y. (1969). Fluctuating pressure in spillway stilling basins. Journal
of the Hydraulics Division, 95(6), 2071-2080.
Brater, E. F., King, H. W., Lindell, J. E., & Wei, C. Y. (1976). Handbook of hydraulics for
the solution of hydraulic engineering problems (Vol. 7). New York: McGraw-Hill.
Castillo, L. G., Carrillo, J. M., & Sordo-Ward, Á. (2014). Simulation of overflow nappe
impingement jets. Journal of Hydroinformatics, 16(4), 922-940

Lin, C., Hsieh, S. C., Lin, I. J., Chang, K. A., & Raikar, R. V. (2012). Flow property and
self-similarity in steady hydraulic jumps. Experiments in Fluids, 53(5), 1591-1616

Chanson, H. (1999). The Hydraulics of Open Channel Flow: An Introduction. Physical Modelling
of Hydraulics.
Chow, V. T. (1959). Open-Channel Hydraulics, McGraw Hill Book Company, Inc., New York.
Christensen, B. A. (1984). “Analysis of Partially Filled Circular Storm Sewers.” J. of
Hydraulic Engineering, ASCE, Vol. 110, No. 8.
El-Ragaby, A., El-Salakawy, E., and Benmokrane, B., “Fatigue Life Evaluation of Concrete
Bridge Deck Slabs Reinforced with Glass FRP Composite Bars,” Journal of Composites for
Construction, ASCE, Vol. 11, No. 3, 2007, pp. 258-268. (doi: http://dx.doi.org/10.1061/(ASCE)
1090-0268(2007)11:3(258),
Fiorotto, V., & Rinaldo, A. (1992). Turbulent pressure fluctuations under hydraulic jumps.
Journal of Hydraulic Research, 30(4), 499-520.
Flow Science (2015). FLOW-3D User Manual(Release 11.1.0), Los Alamos, New Mexico.
González-Betancourt, M. (2016). Uplift force and momenta on a slab subjected to hydraulic
jump. Dyna, 83(199), 124-133.
Grinstein, L., & Lipsey, S. I. (2001). Encyclopedia of mathematics education. Routledge.
Grubbs, F. E., & Beck, G. (1972). Extension of sample sizes and percentage points for
significance tests of outlying observations. Technometrics, 14(4), 847-854.
Gylltoft K. (1983): Fracture mechanics models for fatigue in concrete structures. Doctoral
thesis / Tekniska högskolan i Luleå, 25D, Luleå, 210 pp.
Herlina, H. and Jirka, G. H. (2008). “Experiments on gas transfer near the air-water
interface in a grid-stirred tank.” Journal of Fluid Mechanics, 594, pp.183-208.
IACWD (Interagency Advisory Committee on Water Data). (1982). Guidelines for determining flood
flow frequency. Bulletin 17B.
JIRKA, G. H. (2008). Experiments on gas transfer at the air–water interface induced by
oscillating grid turbulence. Journal of Fluid Mechanics, 594, 183-208.
Kadota, A., Suzuki, K., Rummel, A. C., Weitbrecht, V., & Jirka, G. H. (2007). Shallow flow
visualization around a single groyne. In Proc. of 7th International Symposium of Particle
Image Velocimetry (CD-ROM).
Kazemi, F., Khodashenas, S. R., & Sarkardeh, H. (2016). Experimental study of pressure
fluctuation in stilling basins. International Journal of Civil Engineering, 14(1), 13-21.
Klowak, C., Memon, A., and Mufti, A., “Static and fatigue investigation of second generation
steel-free bridge decks,” Cement & Concrete Composites, ScienceDirect, Elsevier, Vol. 28, No.

10, 2006, pp. 890-897. (doi: http://dx.doi.org/10.1016/j.cemconcomp.2006.07.019),
Koca, K., Noss, C., Anlanger, C., Brand, A., & Lorke, A. (2017). Performance of the Vectrino
Profiler at the sediment–water interface. Journal of Hydraulic Research, 55(4), 573-581.
Kolmogorov, A. (1933). Sulla determinazione empirica di una lgge di distribuzione. Inst. Ital.
Attuari, Giorn., 4, 83-91.
Leon, A., & Alnahit, A. (2016). A Remotely Controlled Siphon System for Dynamic Water Storage
Management.
Lin, C., Hsieh, S., Chang, K. and Raikar, R. (2012). “Flow property and self-similarity in
steady hydraulic jumps.” Experiments in Fluid, 53, pp. 1591-1616.
Lopardo, R., Fattor, C. A., Casado, J. M. and Lopardo, M. C. (2004). “Aspects of vibration
and fatigue of materials related to coherent structures of macroturbulent flows”
International Conference on Hydraulic of Dams and River Structures.
Lopardo, R. A., & Romagnoli, M. (2009). Pressure and velocity fluctuations in stilling basins.
In Advances in Water Resources and Hydraulic Engineering (pp. 2093-2098). Springer, Berlin,
Heidelberg.
Sanchez, P. A., Ramirez, G. E., Vergara, R., & Minguillo, F. (1973). Performance of
Sulfur-Coated Urea Under Intermittently Flooded Rice Culture in Peru 1. Soil Science Society
of America Journal, 37(5), 789-792.
Matsui, S., Tokai, D., Higashiyama, H., and Mizukoshi, M., “Fatigue Durability of Fiber
Reinforced Concrete Decks Under Running Wheel Load,” Proceedings 3rd International Conference
on Concrete Under Severe Conditions, Ed. N. Banthia, Vancouver, Canada, 2001, pp. 982-991.,
Mohammadi, S. F., Galgoul, N. S., Starossek, U., & Videiro, P. M. (2016). An efficient time
domain fatigue analysis and its comparison to spectral fatigue assessment for an offshore
jacket structure. Marine Structures, 49, 97-115.
Pothof, I. (2011). Co-current air-water flow in downward sloping pipes. Stichting Deltares
Pothof, I. W. M., & Clemens, F. H. L. R. (2011). Experimental study of air–water flow in
downward sloping pipes. International journal of multiphase flow, 37(3), 278-292.
Ryu, Y., Chang, K. A., & Lim, H. J. (2005). Use of bubble image velocimetry for measurement of
plunging wave impinging on structure and associated greenwater. Measurement Science and
Technology, 16(10), 1945.
Sanjou, M., & Nezu, I. (2009). Turbulence structure and coherent motion in meandering compound
open-channel flows. Journal of Hydraulic Research, 47(5), 598-610.
Sargison, J. E., & Percy, A. (2009). Hydraulics of broad-crested weirs with varying side
slopes. Journal of irrigation and drainage engineering, 135(1), 115-118.

Sobani, A. (2014). Pressure fluctuations on the slabs of stilling basins under hydraulic jump.
Song, Y., Chang, K, Ryu, Y. and Kwon, S. (2013). “ Experimental study on flow kinematics and
impact pressure in liquid sloshing.”, Experiments in Fluid, 54, pp. 1592.
Stagonas, D., Lara, J. L., Losada, I. J., Higuera, P., Jaime, F. F., & Muller, G. (2014).
Large scale measurements of wave loads and mapping of impact pressure distribution at the
underside of wave recurves. In Proceedings of the HYDRALAB IV Joint User Meeting.
Toso, J. W., & Bowers, C. E. (1988). Extreme pressures in hydraulic-jump stilling basins.
Journal of Hydraulic Engineering, 114(8), 829-843.
Youn, S. G. and Chang, S. P., “Behavior of Composite Bridge Decks Subjected to Static and
Fatigue Loading,” Structural Journal, ACI Technical paper, Title No. 95-S23, 1998, pp.
249-258. (doi: http://dx.doi.org/10.14359/543),

Fig. 1. Double-deck TBM tunnel example

Flow 3D를 이용한 다목적 수로 터널의 마찰 손실 산정

Friction loss of multi-purpose stormwater tunnel simulated by Flow 3D

이두한1
, 김정환2
, 정건희2*
1한국건설기술연구원 수자원하천연구소, 2호서대학교 토목공학과

Abstract

본 최근 지구의 온난화로 인하여 극한 홍수가 자주 발생하고 있으며, 기존 도시 유역의 우수배제시설 용량부족 등으로 도시홍수가 빈번하게 발생하고 있으므로, 주요 범람지역의 홍수량을 우회하거나 저류하여 홍수를 방지하기 위한 수로터널의 개발이 요구된다.

본 연구에서는 교통기능과 수로기능을 동시에 갖춘 다기능 수로 터널의 설계 기준을 개발하기 위한 수리 실험 및 Flow3D를 이용한 수치모의을 수행하였다. 수치모의결과 동일한 수로 터널 구간 내 발생하는 마찰손실의 크기는 수치모의로 도출된 마찰손실이 이론적으로 계산한 마찰손실보다 더 크게 발생함을 관측하였으며, 이는 수로의 형상이 비원형인 경우에는 관의 기하학적 형상에 의한 흐름구조의 변화로 추가적인 마찰손실이 발생하는 것이 원인으로 판단된다.

마찰손실의 증가는 난류보다 층류에서 두드러졌다. 따라서 터널의 홍수량 흐름 시 마찰손실계수가 터널의 형상에 좌우되며, 실무에서 정확한 설계를 위해 방수로 터널의 형상을 주의 깊게 고려해야 한다는 결론을 내렸다. 이는 실제 방수로 터널 설계에 활용될 수 있는 기본 정보를 제공할 수 있을 것으로 보인다.

The extreme floods recently are have been attributed global warming, The development of a canal tunnel to prevent floods by making a bypass or undercurrent to flood discharge in a major flooding area is required because urban flooding in heavy rainfall occurs frequently, increasing the impermeability according to lack of capacity in sewage to urbanization by the existing urban basin. In this study, a numerical simulation was performed to support design standards for a multi-purpose waterway tunnel combined road tunnel of canal tunnel. The numerical simulation showed that the size of the friction loss occurring in the tunnel section of the same channel occurred more than the theoretically calculated frictional loss derived from the numerical simulations. This is probably due to the additional frictional loss caused by the change in the flow structure due to the geometry of the pipe when the shape of the channel is non-circular. The increase in friction loss was more pronounced in the laminar flow than in the turbulent flow. Depending on the shape of the conduit, the friction loss should be adjusted for accurate flow calculations. This result can provide the basin information about the design of flood by a pass conduit

Fig. 1. Double-deck TBM tunnel example
Fig. 1. Double-deck TBM tunnel example
Table 1. Discharge cases
Table 1. Discharge cases
Fig. 3. Setup of geometry
Fig. 3. Setup of geometry
Table 2. Boundary applied model
Table 2. Boundary applied model
Fig. 4. Pressure value according to the 6 different discharges
Fig. 4. Pressure value according to the 6 different discharges
Fig. 5. Hydraulic grade line along the stormwater tunnel using FLOW-3D
Fig. 5. Hydraulic grade line along the stormwater tunnel using FLOW-3D
Fig. 6. Head loss compared hydraulic experiment with Flow 3D and assumed circular pipe
Fig. 6. Head loss compared hydraulic experiment with Flow 3D and assumed circular pipe
Table 3. Measured and calculated frictional loss coefficient in the discharge cases
Table 3. Measured and calculated frictional loss coefficient in the discharge cases
Fig. 7. Comparison of frictional loss coefficient according to the Reynolds number
Fig. 7. Comparison of frictional loss coefficient according to the Reynolds number

References

[1] Kim, J.-H., Kwon, S.-H., Yoon, K.-S., Lee, L.-H., Chung, G.-H. Hydraulic Experiment for Friction Loss
Coefficient in Non-circular Pipe, Procedia Engineering, Vol. 154, pp. 773-778, 2016.
DOI: https://doi.org/10.1016/j.proeng.2017.02.354
[2] Colebrook, C. F. and White, C. M. Experiments with fluid- friction in roughened pipes, Proc. Royal Soc.
London, 161, 367-381, 1937.
DOI: https://doi.org/10.1098/rspa.1937.0150
[3] Flow Science Inc. FLOW-3D User’s Manual, 2015
[4] Jeong, C.-S. Discharge coefficient of side weir for various curvatures simulated by FLOW-3D, Korea Water Resources Association, 2011.
[5] Kang, S.-H. A comparison of hydraulic phenomenon in inlet and outlet point in retention reservoir using FLOW-3D model and hydraulic experiment, Donga University, 2012.
[6] Seoul General planning for reducing traffic of surface road, 2015.
[7] Willams, G. S. and Hazen A. Hydraulic Tables, 3rd ed., rev. John Wiley, New York, 1933.
[8] Yen, Ben Chie. Channel flow resistance: centennial of Manning’s formula, Water Resources Publications, 1992.

Figure 7. The simulated velocity (a) and simulated pressure pattern (b) across the Parshall flume. The patterns match the physical behavior of actual Parshall flumes [7].

Application of Numerical and Experimental Modeling to Improve the Efficiency of Parshall Flumes: A Review of the State-of-the-Art

Parshall Flumes의 효율성 향상을 위한 수치 및 실험 모델링의 적용: 최신 기술 검토

Mehdi Heyrani 1,* , Abdolmajid Mohammadian 1, Ioan Nistor 1 and Omerul Faruk Dursun 2

Abstract

열린 채널에서 흐름을 관리하는 기본 단계 중 하나는 속성을 결정하는 것입니다. 개방 수로의 흐름에 관한 추가 정보를 제공하기 위해 경험적 방정식이 개발되었습니다. 이러한 실험 방정식을 얻는 것은 비용과 시간이 많이 소요됩니다. 따라서 대체 솔루션이 모색되었습니다.

지난 세기 동안 움직이는 부분이 없는 정적 측정 장치인 Parshall 수로가 개방 수로의 흐름을 측정하는 데 중요한 역할을 했습니다. 많은 연구자들이 관개 및 폐수 관리와 같은 다양한 분야에서 Parshall 수로의 적용을 연구하는 데 관심을 집중해 왔습니다.

여러 학자들이 실험 결과를 사용하여 Parshall 수로의 등급 방정식을 향상시켰지만 다른 학자들은 수치 시뮬레이션을 사용하여 높이-방전 관계 방정식을 재보정하기 위해 대체 데이터 소스를 사용했습니다. 컴퓨팅 하드웨어가 지난 수십 년 동안 크게 발전하여 과거에 경험했던 제한된 해상도를 뛰어넘는 것이 가능해짐에 따라 CFD(Computational Fluid Dynamic) 소프트웨어가 오늘날 대중화되고 있습니다.

여러 CFD 모델은 가용성에 따라 오픈 소스 또는 상업적으로 허가되어 수위 결과를 생성하기 위해 다양한 구성의 수로, 특히 Parshall 수로에 대한 수치 시뮬레이션을 수행하는 데 사용되었습니다.

FLOW-3D, Ansys Fluent, OpenFOAM 등 지금까지 사용되어 온 다양한 CFD 도구에 대해 실험 데이터로 정밀 교정한 결과, 출력이 안정적이고 실제 시나리오에 구현할 수 있음이 확인되었습니다.

결과를 생성하기 위해 이 기술을 사용하는 이점은 필요한 경우 유속 또는 구조적 형상과 같은 초기 조건을 조정하는 CFD 접근 방식의 능력입니다. 수로 크기와 수로가 위치한 부지의 조건과 관련하여 상황에 적합한 특정 Parshall 수로로 선택이 좁혀집니다.

표준 Parshall 수로를 선택하는 것이 항상 가능한 것은 아닙니다. 따라서 엔지니어는 가장 가까운 수로 크기에 약간의 수정을 제공하고 정확한 유량을 생성하기 위해 새로운 등급 곡선을 제공합니다.

이 검토는 기존 등급 방정식을 향상시키거나 구조의 기하학에 대한 추가 수정을 제안하기 위해 Parshall 수로에서 수치 시뮬레이션 및 물리적 실험 데이터의 적용을 목표로 하는 여러 학자의 작업에 대해 수행되었습니다.

One of the primary steps in managing the flow in an open channel is determining its properties. Empirical equations are developed to provide further information regarding the flow in open channels. Obtaining such experimental equations is expensive and time consuming; therefore, alternative solutions have been sought. Over the last century, the Parshall flume, a static measuring device with no moving parts, has played a significant role in measuring the flow in open channels. Many researchers have focused their interest on studying the application of Parshall flumes in various fields like irrigation and wastewater management. Although various scholars used experimental results to enhance the rating equation of the Parshall flume, others used an alternative source of data to recalibrate the height–discharge relation equation using numerical simulation. Computational Fluid Dynamic (CFD) software is becoming popular nowadays as computing hardware has advanced significantly within the last few decades, making it possible to go beyond the limited resolution that was experienced in the past. Multiple CFD models, depending on their availability, either open-source or commercially licensed, have been used to perform numerical simulations on different configurations of flumes, especially Parshall flumes, to produce water level results. Regarding various CFD tools that have been used, i.e., FLOW-3D, Ansys Fluent, or OpenFOAM, after precise calibration with experimental data, it has been determined that the output is reliable and can be implemented to the actual scenarios. The benefit of using this technique to produce results is the ability of the CFD approach to adjust the initial conditions, like flow velocity or structural geometry, where necessary. With respect to channel size and the condition of the site where the flume is located, the choices are narrowed to the specific Parshall flume suitable to the situation. It is not always possible to select the standard Parshall flume; therefore, engineers provide some modification to the closest flume size and provide a new rating curve to produce accurate flowrates. This review has been performed on the works of a number of scholars who targeted the application of numerical simulation and physical experimental data in Parshall flumes to either enhance the existing rating equation or propose further modification to the structure’s geometry.

Keywords

Parshall flume; CFD; OpenFOAM; FLOW-3D; numerical simulation; turbulence model

Figure 1. Parshall flume measuring structure, installed [2].
Figure 1. Parshall flume measuring structure, installed [2].
Figure 2. Parshall flume measuring structure, uninstalled [3]
Figure 2. Parshall flume measuring structure, uninstalled [3]
Figure 4. Mesh sensitivity analysis: top view and side view of the Parshall flume: (a) contains 27,000 cells; (b) 52,000 cells; (c) 75,000 cells; (d) 270,000 cells. The C setup was used in their simulation [7].
Figure 4. Mesh sensitivity analysis: top view and side view of the Parshall flume: (a) contains 27,000 cells; (b) 52,000 cells; (c) 75,000 cells; (d) 270,000 cells. The C setup was used in their simulation [7].
Figure 7. The simulated velocity (a) and simulated pressure pattern (b) across the Parshall flume. The patterns match the physical behavior of actual Parshall flumes [7].
Figure 7. The simulated velocity (a) and simulated pressure pattern (b) across the Parshall flume. The patterns match the physical behavior of actual Parshall flumes [7].
Figure 8. Computational grid system in the Side A flume. (a) contains a triangular grid system (b) demonstrates the rectangular grid system. (c) and (d) are three-dimensional schematics showing the superimposed grid system. (e) magnifies the dashed section in (b). (Reprinted with permission from Ref. [11]. 2020 ELSEVIER). ).
Figure 8. Computational grid system in the Side A flume. (a) contains a triangular grid system (b) demonstrates the rectangular grid system. (c) and (d) are three-dimensional schematics showing the superimposed grid system. (e) magnifies the dashed section in (b). (Reprinted with permission from Ref. [11]. 2020 ELSEVIER). ).
Figure 10. The results of flow patterns in different flumes; (a) Cutthroat flume, (b) airfoil-shaped flume, (c) airfoil pillar-shaped flume, (d) optimized airfoil-shaped flume [23]
Figure 10. The results of flow patterns in different flumes; (a) Cutthroat flume, (b) airfoil-shaped flume, (c) airfoil pillar-shaped flume, (d) optimized airfoil-shaped flume [23]
Figure 11. Experimental setup: contraction ratio used on each flume [23].
Figure 11. Experimental setup: contraction ratio used on each flume [23].
Figure 12. Entire flume geometry [25]
Figure 12. Entire flume geometry [25]

References

  1. Cone, V.M. The Venturi Flume; U.S. Government Printing Office: Washington, DC, USA, 1917.
  2. 20-Foot Concrete Parshall Flume with Radius Wing Walls. Available online: https://www.openchannelflow.com/assets/uploads/
    media/_large/20-foot-parshall-flume-curved-wing-walls.jpg (accessed on 12 January 2021).
  3. Fiberglass 6-Inch Parshall Flume with Gauge. Available online: https://www.openchannelflow.com/assets/uploads/media/
    _large/flume-parshall-6-inch-fiberglass.png (accessed on 12 January 2021).
  4. Parshall, R.L. The Parshall Measuring Flume; Colorado State College, Colorado Experiment Station: Fort Collins, CO, USA, 1936.
  5. Selecting Between a Weir and a Flume. 2022. Available online: https://www.openchannelflow.com/blog/selecting-a-primarydevice-part-1-choosing-between-a-weir-and-a-flume (accessed on 29 December 2021).
  6. Parshall, R.L. The Improved Venturi Flume. Trans. Am. Soc. Civ. Eng. 1928, 89, 841–851. [CrossRef]
  7. Heyrani, M.; Mohammadian, A.; Nistor, I. Numerical Simulation of Flow in Parshall Flume Using Selected Nonlinear Turbulence
    Models. Hydrology 2021, 8, 151. [CrossRef]
  8. Heyrani, M.; Mohammadian, A.; Nistor, I.; Dursun, O.F. Numerical Modeling of Venturi Flume. Hydrology 2021, 8, 27. [CrossRef]
  9. Alfonsi, G. Reynolds-Averaged Navier–Stokes Equations for Turbulence Modeling. Appl. Mech. Rev. 2009, 62, 040802. [CrossRef]
  10. Imanian, H.; Mohammadian, A. Numerical Simulation of Flow over Ogee Crested Spillways under High Hydraulic Head Ratio.
    Eng. Appl. Comput. Fluid Mech. 2019, 13, 983–1000. [CrossRef]
  11. Khosronejad, A.; Herb, W.; Sotiropoulos, F.; Kang, S.; Yang, X. Assessment of Parshall Flumes for Discharge Measurement of
    Open-Channel Flows: A Comparative Numerical and Field Case Study. Measurement 2020, 167, 108292. [CrossRef]
  12. Dursun, O.F. An Experimental Investigation of the Aeration Performance of Parshall Flume and Venturi Flumes. KSCE J. Civ. Eng.
    2016, 20, 943–950. [CrossRef]
  13. Shih, T.-H.; Liu, N.-S.; Chen, K.-H. A Non-Linear k-Epsilon Model for Turbulent Shear Flows. In Proceedings of the 34th
    AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Cleveland, OH, USA, 13 July 1998; p. 3983.
  14. Lien, F.S. Low-Reynolds-Number Eddy-Viscosity Modelling Based on Non-Linear Stress-Strain/Vorticity Relations. In Proceedings of the 3rd Symposium on Engineering Turbulence Modelling and Measurement, Heraklion, Greece, 27 May 1996.
  15. Davis, R.W.; Deutsch, S. A Numerical-Experimental Study of Parhall Flumes. J. Hydraul. Res. 1980, 18, 135–152. [CrossRef]
  16. Xiao, Y.; Wang, W.; Hu, X.; Zhou, Y. Experimental and Numerical Research on Portable Short-Throat Flume in the Field. Flow
    Meas. Instrum. 2016, 47, 54–61. [CrossRef]
  17. Wright, S.J.; Tullis, B.P.; Long, T.M. Recalibration of Parshall Flumes at Low Discharges. J. Irrig. Drain. Eng. 1994, 120, 348–362.
    [CrossRef]
  18. Heiner, B.; Barfuss, S.L. Parshall Flume Discharge Corrections: Wall Staff Gauge and Centerline Measurements. J. Irrig. Drain.
    Eng. 2011, 137, 779–792. [CrossRef]
  19. Savage, B.M.; Heiner, B.; Barfuss, S. Parshall Flume Discharge Correction Coefficients through Modelling. Proc. ICE Water Manag.
    2013, 167, 279–287. [CrossRef]
  20. Zerihun, Y.T. A Numerical Study on Curvilinear Free Surface Flows in Venturi Flumes. Fluids 2016, 1, 21. [CrossRef]
  21. Sun, B.; Zhu, S.; Yang, L.; Liu, Q.; Zhang, C.; Zhang, J. ping Experimental and Numerical Investigation of Flow Measurement
    Mechanism and Hydraulic Performance on Curved Flume in Rectangular Channel. Arab. J. Sci. Eng. 2020. [CrossRef]
  22. Hu, H.; Huang, J.; Qian, Z.; Huai, W.; Yu, G. Hydraulic Analysis of Parabolic Flume for Flow Measurement. Flow Meas. Instrum.
    2014, 37, 54–64. [CrossRef]
  23. Sun, B.; Yang, L.; Zhu, S.; Liu, Q.; Wang, C.; Zhang, C. Study on the Applicability of Four Flumes in Small Rectangular Channels.
    Flow Meas. Instrum. 2021, 80, 101967. [CrossRef]
  24. Willeitner, R.P.; Barfuss, S.L.; Johnson, M.C. Using Numerical Modeling to Correct Flow Rates for Submerged Montana Flumes. J.
    Irrig. Drain. Eng. 2013, 139, 586–592. [CrossRef]
  25. Ran, D.; Wang, W.; Hu, X. Three-Dimensional Numerical Simulation of Flow in Trapezoidal Cutthroat Flumes Based on FLOW-3D.
    Front. Agric. Sci. Eng. 2018, 5, 168–176. [CrossRef]
  26. Kim, S.-Y.; Lee, J.-H.; Hong, N.-K.; Lee, S.-O. Numerical Simulation for Determining Scale of Parshall Flume. Proc. Korea Water
    Resour. Assoc. Conf. 2010, 719–723.
  27. Tekade, S.A.; Vasudeo, A.D.; Ghare, A.D.; Ingle, R.N. Measurement of Flow in Supercritical Flow Regime Using Cutthroat Flumes.
    Sadhana 2016, 41, 265–272. [CrossRef]
  28. Wahl, T.L.; Replogle, J.A.; Wahlin, B.T.; Higgs, J.A. New Developments in Design and Application of Long-Throated Flumes. In
    Proceedings of the Joint Conference on Water Resource Engineering and Water Resources Planning and Management, Minneapolis,
    MN, USA, 30 July–2 August 2000.
  29. Howes, D.J.; Burt, C.M.; Sanders, B.F. Subcritical Contraction for Improved Open-Channel Flow Measurement Accuracy with an
    Upward-Looking ADVM. J. Irrig. Drain. Eng. 2010, 136, 617–626. [CrossRef]
  30. Tiwari, N.K.; Sihag, P. Prediction of Oxygen Transfer at Modified Parshall Flumes Using Regression Models. ISH J. Hydraul. Eng.
    2020, 26, 209–220. [CrossRef]
  31. Thornton, C.I.; Smith, B.A.; Abt, S.R.; Robeson, M.D. Supercritical Flow Measurement Using a Small Parshall Flume. J. Irrig.
    Drain. Eng. 2009, 135, 683–692. [CrossRef]
  32. Cox, A.L.; Thornton, C.I.; Abt, S.R. Supercritical Flow Measurement Using a Large Parshall Flume. J. Irrig. Drain. Eng. 2013, 139,
    655–662. [CrossRef]
  1. Ribeiro, Á.S.; Sousa, J.A.; Simões, C.; Martins, L.L.; Dias, L.; Mendes, R.; Martins, C. Parshall Flumes Flow Rate Uncertainty
    Including Contributions of the Model Parameters and Correlation Effects. Meas. Sens. 2021, 18, 100108. [CrossRef]
  2. Singh, J.; Mittal, S.K.; Tiwari, H.L. Discharge Relation for Small Parshall Flume in Free Flow Condition. Int. J. Res. Eng. Technol.
    2014, 3, 317–321.
  3. Kim, S.-D.; Lee, H.-J.; Oh, B.-D. Investigation on Application of Parshall Flume for Flow Measurement of Low-Flow Season in
    Korea. Meas. Sci. Rev. 2010, 10, 111. [CrossRef]
  4. Willeitner, R.P.; Barfuss, S.L.; Johnson, M.C. Montana Flume Flow Corrections under Submerged Flow. J. Irrig. Drain. Eng. 2012,
    138, 685–689. [CrossRef]
  5. Dufresne, M.; Vazquez, J. Head–Discharge Relationship of Venturi Flumes: From Long to Short Throats. J. Hydraul. Res. 2013, 51,
    465–468. [CrossRef]
Figure 13 | Velocity vector for flow condition Q¼22 l/s, Near surface.

Experimental and numerical study of flow at a 90 degree lateral turn-out with enhanced roughness coefficient and invert elevation changes

조도 계수 및 역 고도 변화가 향상된 90도 측면 회전에서 유동의 실험 및 수치 연구

Maryam Bagheria, Seyed M. Ali Zomorodianb, Masih Zolghadrc, H. MD. Azamathulla d,*
and C. Venkata Siva Rama Prasade
a Hydraulic Structures, Department of Water Engineering, Shiraz University, Shiraz, Iran
b Department of Water Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
c Department of Water Sciences Engineering, College of Agriculture, Jahrom University, Jahrom, Iran
d Civil & Environmental Engineering, The University of the West Indies, St. Augustine Campus, Port of Spain, Trinidad
e Department of Civil Engineering, St. Peters Engineering College, Hyderabad, India
*Corresponding author. E-mail: azmatheditor@gmail.com

ABSTRACT

Flow separation at the upstream side of the lateral turnouts (intakes) is a critical issue causing eddy currents at the turn-out entrance. It reduces the effective width of flow, turn-out capacity and efficiency.

Therefore, it is essential to identify the dimensions of the separation zone and propose remedies to reduce its dimensions. Installation of 7 types of roughening elements at the turn-out entrance and 3 different bed level inverts, with 4 different discharges (total of 84 experiments) were examined in this study as a method to reduce the dimensions of
the separation zone.

Additionally, a 3-D Computational Fluid Dynamic (CFD) model was utilized to evaluate the flow pattern and dimensions of the separation zone. Results showed that enhancing the roughness coefficient can reduce the separation zone dimensions up to 38% while the drop implementation effect can scale down this area differently based on the roughness coefficient used. Combining both methods can reduce the separation zone dimensions up to 63%.

측면 분기기(흡입구)의 상류 측에서 흐름 분리는 분기기 입구에서 와류를 일으키는 중요한 문제입니다. 이는 흐름의 유효 폭, 턴아웃 용량 및 효율성을 감소시킵니다. 따라서 분리지대의 크기를 파악하고 크기를 줄이기 위한 방안을 제시하는 것이 필수적이다.

이 연구에서는 분리 구역의 치수를 줄이기 위한 방법으로 4가지 다른 배출(총 84개 실험)과 함께 7가지 유형의 조면화 요소를 출구 입구에 설치하고 3가지 서로 다른 베드 레벨 반전 장치를 조사했습니다.

또한 3D CFD(Computational Fluid Dynamics) 모델을 사용하여 분리 영역의 흐름 패턴과 치수를 평가했습니다. 결과는 거칠기 계수를 향상시키면 분리 영역 치수를 최대 38%까지 줄일 수 있는 반면 드롭 구현 효과는 사용된 거칠기 계수를 기반으로 이 영역을 다르게 축소할 수 있음을 보여주었습니다.

두 가지 방법을 결합하면 분리 영역 치수를 최대 63%까지 줄일 수 있습니다.

Key words

discharge ratio, flow separation zone, intake, three dimensional simulation

Experimental and numerical study of flow at a 90 degree lateral turn-out with enhanced roughness coefficient and invert elevation changes
Experimental and numerical study of flow at a 90 degree lateral turn-out with enhanced roughness coefficient and invert elevation changes
Figure 2 | Roughness plates.
Figure 2 | Roughness plates.
Figure 3 | Effect of roughness on separation zone dimensions
Figure 3 | Effect of roughness on separation zone dimensions
Figure 4 | Effect of roughness on separation zone dimensions.
Figure 4 | Effect of roughness on separation zone dimensions.
Figure 5 | Comparison of separation zone for n¼0.023 and n¼0.032.
Figure 5 | Comparison of separation zone for n¼0.023 and n¼0.032.
Figure 6 | Effect of drop implementation on separation zone dimensions
Figure 6 | Effect of drop implementation on separation zone dimensions
Figure 7 | Combined effect of roughness and drop on separation zone dimensions
Figure 7 | Combined effect of roughness and drop on separation zone dimensions
Figure 8 | Non- dimensional Length of separation zone (Lr) variations against relative unit discharge per width (qr) in present study compared with other methods.
Figure 8 | Non- dimensional Length of separation zone (Lr) variations against relative unit discharge per width (qr) in present study compared with other methods.
Figure 9 | Velocity profiles for various roughness coefficients along turn-out width.
Figure 9 | Velocity profiles for various roughness coefficients along turn-out width.
Figure 10 | Effect of roughness on sepration zone dimensions in numerical study
Figure 10 | Effect of roughness on sepration zone dimensions in numerical study
Figure 11 | Comparision of the vortex area (software output) with three roughness (0.009, 0.023 and 0.032).
Figure 11 | Comparision of the vortex area (software output) with three roughness (0.009, 0.023 and 0.032).
Figure 12 | Comparison of vortex area in 3D mode (tecplot output) with two roughness (a) 0.009 and (b) 0.032
Figure 12 | Comparison of vortex area in 3D mode (tecplot output) with two roughness (a) 0.009 and (b) 0.032
Figure 13 | Velocity vector for flow condition Q¼22 l/s, Near surface.
Figure 13 | Velocity vector for flow condition Q¼22 l/s, Near surface.
Figure 14 | Exprimental and numerical measured velocity.
Figure 14 | Exprimental and numerical measured velocity.

REFERENCES

Abbasi, A., Ghodsian, M., Habibi, M. & Salehi Neishabouri, S. A. 2004 Experimental investigation on dimensions of flow separation zone at
lateral intakeentrance. Research & Construction; Pajouhesh va Sazandegi (in Persian) 62, 38–44.
Al-Zubaidy, R. & Hilo, A. 2021 Numerical investigation of flow behavior at the lateral intake using Computational Fluid Dynamics (CFD).
Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.11.172.
Jalili, H., Hosseinzadeh Dalir, A. & Farsadizadeh, D. 2011 Effect of intake geometry on the sediment transport and flow pattern at lateral.
Iranian Water Research Journal(InPersian) 5 (9), 1–10.
Jamshidi, A., Farsadizadeh, D. & Hosseinzadeh Dalir, A. 2016 Variations of flow separation zone at lateral intakes entrance using submerged
vanes. Journal of Civil Engineering Urban 6 (3), 54–63. Journal homepage. Available from: www.ojceu.ir/main.
Karami Moghaddam, K. & Keshavarzi, A. 2007 Investigation of flow structure in lateral intakes 55° and 900
° with rounded entrance edge.
In: 03 National Congress on Civil Engineering University of Tabriz. (In Persian). Available from: https://civilica.com/doc/16317.
Karami, H., Farzin, S., Sadrabadi, M. T. & Moazeni, H. 2017 Simulation of flow pattern at rectangular lateral intake with different dike and
submerged vane scenarios. Journal of Water Science and Engineering 10 (3), 246–255. https://doi.org/10.1016/j.wse.2017.10.001.
Kasthuri, B. & Pundarikanthan, N. V. 1987 Discussion on separation zone at open- channel junction. Journal of Hydraulic Engineering
113 (4), 543–548.
Keshavarzi, A. & Habibi, L. 2005 Optimizing water intake angle by flow separation analysis. Journal of Irrigation and Drain 54, 543–552.
https://doi.org/10.1002/ird.207.
Kirkgöz, M. S. & Ardiçlioğ
lu, M. 1997 Velocity profiles of developing and developed open channel flow. Journal of Hydraulic Engineering
1099–1105. 10.1061/(ASCE)0733-9429(1997)123:12(1099).
Nakato, T., Kennedy, J. F. & Bauerly, D. 1990 Pumpstation intake-shoaling control with submerge vanes. Journal of Hydraulic Engineering.
https://doi.org/10.1061/(ASCE)0733-9429(1990)116:1(119).

Neary, V. S., Sotiropoulos, F. & Odgaard, A. J. 1999 Three-dimensional numerical model of lateral-intake in flows. Journal of Hydraulic
Engineering. https://doi.org/10.1061/(ASCE)0733-9429(1999)125:2(126).
Nikbin, S. & Borghei, S. M. 2011 Experimental investigation of submerged vanes effect on dimensions of flow separation zone at 90°
openchannel junction. In: 06rd National Congress on Civil Engineering University of Semnan. (In Persian). Available from: https://
civilica.com/doc/120494.
Odgaard, J. A. & Wang, Y. 1991 Sediment management with submerged vanes, I: theory. Journal of Hydraulic Engineering 117 (3), 267–283.
Ouyang, H. T. 2009 Investigation on the dimensions and shape of a submerged vane for sediment management in alluvial channels. Journal of
Hydraulic Engineering. https://doi.org/10.1061/(ASCE)0733-9429(2009)135:3(209).
Ramamurthy, A. S., Junying, Q. & Diep, V. 2007 Numerical and experimental study of dividing open-channel flows. Journal of Hydraulic
Engineering. https://doi.org/10.1061/(ASCE)0733-9429(2007)133:10(1135).
Samimi Behbahan, T. 2011 Laboratory investigation of submerged vane shapes effect on river banks protection. Australian Journal of Basic
and Applied Sciences 5 (12), 1402–1407.
Seyedian, S., Karami Moghaddam, K. & Shafai Begestan, M. 2008 Determine the optimal radius in lateral intakes 55° and 90° using variation
of flow velocity. In: 07th Iranian Hydraulic Conference. Power & Water University of Technology (PWUT) (in Persian). Available from:
https://civilica.com/doc/56251.
Zolghadr, M. & Shafai Bejestan, M. 2020 Six legged concrete (SLC) elements as scour countermeasures at wing wall bridge abutments.
International Journal of River Basin Management. doi: 10.1080/15715124.2020.1726357.
Zolghadr, M., Zomorodian, S. M. A., Shabani, R. & Azamatulla Md., H. 2020 Migration of sand mining pit in rivers: an experimental,
numerical and case study. Measurement. https://doi.org/10.1016/j.measurement.2020.108944.

Figura 1. Parámetros del medidor Palmer-Bowlus

Three-Dimensional Numerical Modeling of the Palmer-Bowlus Measuring Flume Applying the FLOW-3D Software.

TOAPAXI-ALVAREZ*, JorgeSILA-BASTIDA, Roberto    TORRES-JACOBOWITZ, Cristina.

The Palmer-Bowlus flume was developed in 1936, as an adaptation of the Venturi flume for the use in sewer systems, due to the difficulty in modifying the pipe invert. There are commercially available single-body Palmer-Bowlus flume with their respective discharge curves, which increase the cost of sewer projects. Based on the physical model of the Palmer-Bowlus flume (Torres & Vásquez, 2010), the aim of this research was to carry out the three-dimensional numerical modeling of these flow meters, considering four pipe diameters: 160 mm, 200 mm, 250 mm and 400 mm; the selected diameters are the most used ones, according to the information provided by the Empresa Pública Metropolitana de Agua Potable y Saneamiento de Quito (EPMAPS). The discharge curves were calibrated and validated using the FLOW-3D program. Meshing had a great influence on the quality results and duration of the numerical simulation; in contrast, the roughness and turbulence models (RNG y k-e) had little influence. The discharge curves obtained in the numerical modeling have good approximation to those obtained in the physical model.

Palmer-Bowlus 수로는 1936년에 하수도 시스템에 사용하기 위해 Venturi 수로를 개조한 것으로 파이프 인버트를 수정하는 것이 어렵기 때문에 개발되었습니다. 각각의 배출 곡선이 있는 시판되는 단일 몸체 Palmer-Bowlus 수로가 있으며, 이는 하수도 프로젝트 비용을 증가시킵니다.

Palmer-Bowlus 수로의 물리적 모델을 기반으로(Torres & Vásquez, 2010), 이 연구의 목적은 160mm, 200mm, 4개의 파이프 직경을 고려하여 이러한 유량계의 3차원 수치 모델링을 수행하는 것이었습니다. 250mm 및 400mm; Empresa Pública Metropolitana de Agua Potable y Sanaeamiento de Quito(EPMAPS)에서 제공한 정보에 따르면 선택한 지름이 가장 많이 사용되는 지름입니다.

방전 곡선은 FLOW-3D 프로그램을 사용하여 보정 및 검증되었습니다. 메싱은 수치 시뮬레이션의 품질 결과와 기간에 큰 영향을 미쳤습니다. 대조적으로, 거칠기 및 난류 모델(RNG y k-e)은 거의 영향을 미치지 않았습니다. 수치 모델링에서 얻은 방전 곡선은 물리적 모델에서 얻은 것과 유사합니다.

Figura 1. Parámetros del medidor Palmer-Bowlus
Figura 1. Parámetros del medidor Palmer-Bowlus
Figura 2. Diagrama de flujo de la modelación del medidor Palmer-Bowlus en FLOW-3D
Figura 2. Diagrama de flujo de la modelación del medidor Palmer-Bowlus en FLOW-3D
Figura 3. Captura de pantalla del modelo numérico Q=22.047( 𝑙 𝑠 ), Ho=20.038 cm
Figura 3. Captura de pantalla del modelo numérico Q=22.047( 𝑙 𝑠 ), Ho=20.038 cm

REFERENCIAS

Aulestia, C. (2017). Modelación numérica en tres dimensiones de flujo en
las compuertas de la captación del Proyecto Toachi – Pilatón
aplicando dinámica de fluidos computacional (CFD). [Tesis
Maestría]. Quito, Ecuador: Escuela Politécnica Nacional.
Casa, E. (2016). Modelación numérica del flujo rasante en una rápida
escalonada aplicando la dinámica de fluidos computacional
(CFD) Programa FLOW-3D. [Tesis maestría]. Quito, Ecuador:
Escuela Politécnica Nacional.
Chow, V. T. (2004). Hidráulica de canales abiertos (Primera ed.). (J.
Saldarriaga, Trad.) Santafé de Bogotá, Colombia: McGrawHill.
Domínguez, F. (1999). Hidráulica (Sexta Edición ed.). Santiango de Chile,
Chile: Editorial Universitaria.
Fernández, J. (2012). Introducción a la dinámica de fluidos computacional
(CFD) por el método de volúmenes finitos. Barcelona: Editorial
Reverté, S.A.
Flow Science, Inc. (2016). Flow-3D v11.2 Documentation. Flow Science,
Inc. Santa Fe: Flow Science.
Ludwig, J., & Ludwig, R. (1951). Design of Palmer-Bowlus Flumes.
Sewafe and Insdustrial Wastes, 23(9), 1096-1107. Obtenido de
https://www.jstor.org/stable/25031687
Recasens, J. (2014). Modelación tridimensional del glujo de entrada en un
sumidero. Barcelona: UPC BARCELONATECH.
Sotelo, G. (1997). Hidráulica General Vol. 1. México D.F.: LIMUSA S.A.
Torres, C., & Vásquez, E. (2010). Análisis de medidores de caudal para
flujo subcrítico en sistemas de alcantarillado. [Tesis
ingeniería]. Quito, Ecuador: Escuela Politécnica Nacional.
Versteeg, H. K., & Malalasekera, W. (1995). An Introduction to
computational fluid dynamics – The finite volume method. New
York: John Wiley & Sons.

Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.

On-Chip Fabrication and In-Flow 3D-Printing of Cell-Laden Microgel Constructs: From Chip to Scaffold Materials in One Integral Process

세포가 함유된 마이크로겔의 온칩 제작 및 인-플로우 3D 프린팅
구성:하나의 통합 프로세스에서 칩에서 스캐폴드 재료까지

Vollmer, Gültekin Tamgüney, Aldo Boccacini
Submitted date: 10/05/2021 • Posted date: 11/05/2021
Licence: CC BY-NC-ND 4.0

바이오프린팅은 세포가 실린 스캐폴드의 제조를 위한 유력한 기술로 발전했습니다. 바이오잉크는 바이오프린팅의 가장 중요한 구성요소입니다. 최근 마이크로겔은 세포 보호 및 세포 미세 환경 제어를 가능하게 하는 매우 유망한 바이오 잉크로 도입되었습니다. 그러나 이들의 미세유체 제작은 본질적으로 한계가 있는 것으로 보입니다.

여기에서 우리는 안정적인 스캐폴드에 직접 유입되는 바이오프린팅과 함께 세포가 실린 마이크로겔의 미세유체 생산을 위한 미세유체 및 3D 인쇄의 직접 결합을 소개합니다. 방법론은 세포를 단분산 미세 방울로 연속 온칩 캡슐화하여 후속 유입 교차 연결을 통해 세포가 함유된 마이크로겔을 생성할 수 있으며, 이는 미세관을 종료한 후 자동으로 얇은 연속 마이크로겔 필라멘트로 끼이게 됩니다.

3D 프린트 헤드로의 통합으로 독립형 3차원 스캐폴드에 필라멘트를 직접 유입 인쇄할 수 있습니다. 이 방법은 다양한 교차 연결 방법 및 세포주에 대해 설명됩니다. 이러한 발전으로 미세유체학은 더 이상 바이오 제조의 병목을 초래하는 현상이 아닙니다.

Bioprinting has evolved into a thriving technology for the fabrication of cell-laden scaffolds. Bioinks are the most critical component for bioprinting. Recently, microgels have been introduced as a very promising bioink enabling cell protection and the control of the cellular microenvironment. However, their microfluidic fabrication inherently seemed to be a limitation. Here we introduce a direct coupling of microfluidics and 3D-printing for the microfluidic production of cell-laden microgels with direct in-flow bioprinting into stable scaffolds. The methodology enables the continuous on-chip encapsulation of cells into monodisperse microdroplets with subsequent in-flow cross-linking to produce cell-laden microgels, which after exiting a microtubing are automatically jammed into thin continuous microgel filaments. The integration into a 3D printhead allows direct in-flow printing of the filaments into free-standing three-dimensional scaffolds. The method is demonstrated for different cross-linking methods and cell lines. With this advancement, microfluidics is no longer a bottleneck for biofabrication.

Fig. 1: Three-dimensional schematic view of the multilayer double 3D-focusing microfluidic channel system, (b) control of droplet diameter via the Capiilary number Ca, and accessible hydrodynamic regimes for droplet production: squeezing (c), dripping (d) and jetting (e). The scale bars are 200 µm.
Fig. 1: Three-dimensional schematic view of the multilayer double 3D-focusing microfluidic channel system, (b) control of droplet diameter via the Capiilary number Ca, and accessible hydrodynamic regimes for droplet production: squeezing (c), dripping (d) and jetting (e). The scale bars are 200 µm.
Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.
Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.
Fig. 3: a) Photograph of a standard meander-shaped layer fabricated by microgel filament deposition printing. The lines have a thickness of 300 µm. b) photograph of a cross-bar pattern obtained by on-top deposition of several microgel filaments. The average linewidth is 1 mm. c) photograph of a donut-shaped microgel construct. The microgels have been fluorescently labelled by FITC-dextran to demonstrate the intrinsic microporosity corresponding to the black non-fluorescent regions, d) light microscopy image of a construct edge showing that fused adhesive microgels form a continuous, three-dimensional selfsupporting scaffold with intrinsic micropores.
Fig. 3: a) Photograph of a standard meander-shaped layer fabricated by microgel filament deposition printing. The lines have a thickness of 300 µm. b) photograph of a cross-bar pattern obtained by on-top deposition of several microgel filaments. The average linewidth is 1 mm. c) photograph of a donut-shaped microgel construct. The microgels have been fluorescently labelled by FITC-dextran to demonstrate the intrinsic microporosity corresponding to the black non-fluorescent regions, d) light microscopy image of a construct edge showing that fused adhesive microgels form a continuous, three-dimensional selfsupporting scaffold with intrinsic micropores.
Fig. 4: a) Scheme of the perfusion chamber consisting of an upstream and downstream chamber, perfusion ports, and removable scaffolds to stabilize the microgel construct during 3D-printing, b) photograph of a microgel construct in the perfusion chamber directly after printing and removal of the scaffolds, c) confocal microscopy image of the permeation front of a fluorescent dye, where the high dye concentration in the micropores can be clearly seen, d) confocal microscopy image of YFP-labelled HEK-cells within a microgel construct.
Fig. 4: a) Scheme of the perfusion chamber consisting of an upstream and downstream chamber, perfusion ports, and removable scaffolds to stabilize the microgel construct during 3D-printing, b) photograph of a microgel construct in the perfusion chamber directly after printing and removal of the scaffolds, c) confocal microscopy image of the permeation front of a fluorescent dye, where the high dye concentration in the micropores can be clearly seen, d) confocal microscopy image of YFP-labelled HEK-cells within a microgel construct.
Fig. 5: a) Layer-by-layer printing of microgel construct with integrated perfusion channel. After printing of the first layer, a hollow perfusion channel is inserted. Subsequently, the second and third layers are printed. b) The construct is directly printed into a perfusion chamber. The perfusion chamber provides whole construct permeation via flows cin and cout, as well as independent flow through the perfusion channel via flows vin and vout. c) Photograph of a perfusion chamber containing the construct directly after printing. The flow of the fluorescein solution through the integrated PVA hollow channel is clearly visible.
Fig. 5: a) Layer-by-layer printing of microgel construct with integrated perfusion channel. After printing of the first layer, a hollow perfusion channel is inserted. Subsequently, the second and third layers are printed. b) The construct is directly printed into a perfusion chamber. The perfusion chamber provides whole construct permeation via flows cin and cout, as well as independent flow through the perfusion channel via flows vin and vout. c) Photograph of a perfusion chamber containing the construct directly after printing. The flow of the fluorescein solution through the integrated PVA hollow channel is clearly visible.
Fig. 6: a) Photograph of an alginate capsule fiber formed after exiting the microtube. b) Confocal fluorescence microscopy image of part of a 3D-printed alginate capsule construct. The fluorescence arises from encapsulated fluorescently labelled polystyrene microbeads to demonstrate the integrity and stability of the alginate capsules.
Fig. 6: a) Photograph of an alginate capsule fiber formed after exiting the microtube. b) Confocal fluorescence microscopy image of part of a 3D-printed alginate capsule construct. The fluorescence arises from encapsulated fluorescently labelled polystyrene microbeads to demonstrate the integrity and stability of the alginate capsules.

Keywords

biomaterials, microgels, microfluidics, 3D printing, bioprinting

References

  1. A. Atala, Chem. Rev. 2020, 120, 10545-10546.
  2. J. Groll, J. A. Burdick, D. W. Cho, B. Derby, M. Gelinsky, S. C. Heilshorn, T. Jüngst, J. Malda, V. A
    Mironov, K. Nakayama, A. Ovisanikov, W. Sun, S. Takeuchi, J. J. Yoo, T. B. F. Woodfield,
    Biofabrication 2019, 11, 013001.
  3. W. Sun, B. Starly, A. C. Daly, J. A. Burdick, J. Groll, G. Skeldon, W. Shu, Y. Sakai, M. Shinohara,
    M. Nishikawa, J. Jang, D.-W. Cho, M. Nie, S. Takeuchi, S. Ostrovidov, A. Khademhosseini, R. D. Kamm,
    V. Mironov, L. Moroni, I. T. Ozbolat, Biofabrication 2020, 12, 022002.
  4. R. Levato, T. Juengst, R. G. Scheuring, T. Blunk, J. Groll, J. Malda, Adv. Mater. 2020, 32, 1906423.
  5. C. B. Highley, K. H. Song, A. C. Daly, J. A. Burdick, Adv. Sci. 2019, 6, 1801076.
  6. D. Velasco, E. Tumarkin, E. Kumacheva, Small 2012, 8, 1633-1642.
  7. W. Jiang, M. Li, Z. Chen, K. W. Leong, Lab Chip 2016, 16, 4482-4506.
  8. A. C. Daly, L. Riley, T. Segura, J. A. Burdick, Nat. Rev. 2020, 5, 20-43.
  9. A. S. Mao, B. Özkale, N. J. Shah, K. H. Vining, T. Descombes, L. Zhang, C. M. Tringides, S.-W.
    Wong, J.-W. Shin, D. T. Scadden, D. A. Weitz, D. J. Mooney, Proc. Natl. Acad. Sci. 2019, 116, 15392-15397.
  10. S. R. Pajoumshariati, M. Azizi, D. Wesner, P. G. Miller, M. L. Shuler, A. Abbaspourrad, ACS Appl.
    Mater. Interfaces 2018, 10, 9235-9246.
  11. A. S. Mao, J.-W. Shin, S. Utech, H. Wang, O. Uzun, W. Li, M. Cooper, Y. Hu, L. Zhang, D. A.
    Weitz, D. J. Mooney, Nat. Mater. 2017, 16, 236-243.
  12. P. S. Lienemann, T. Rossow, A. S. Mao, Q. Vallmajo-Martin, M. Ehrbar, D. J. Mooney, Lab Chip, 2017, 17, 727.
  13. F. Chen, J. Xue, J. Zhang, M. Bai, X. Yu, X.; C. Fan, Y. Zhao, J. Am. Chem. Soc. 2020, 142, 2889-2896.
  14. Q. Feng, Q. Li, H. Wen, J. Chen, M. Liang, H. Huang, D. Lan, H. Dong, X. Cao, Adv. Funct. Mater.,
    2019, 29, 1096690.
  15. L. P. B. Guerzoni, T. Yoshinari, D. B. Gehlen, D. Rommel, T. Haraszti, M. Akashi, L. De Laporte,
    Biomacromolecules 2019, 20, 3746-3754
  16. T. Rossow, J. A. Heyman, A. J. Ehrlicher, A. Langhoff, D. A. Weitz, R. Haag, S. Seiffert, J. Am.
    Chem. Soc. 2012, 134, 4983-4989.
  17. E. Kapourani, F. Neumann, K. Achazi, J. Dernedde, R. Haag, Macromol. Bioscience 2018, 18,1800116
  18. H. Wang, H. Liu, H. Liu, W. Su, W. Chen, J. Qin, Adv. Mater. Technol. 2019, 4, 1800632.
  19. C. Fan, S.-H. Zhan, Z.-X. Dong, W. Yang, W.-S. Deng, X. Liu, P. Suna, D.-A. Wang, Mater. Sci. Eng. C 2019, 108, 110399.
  20. A. M. Compaan, K. Song, W. Chai, Y. Huang, ACS Appl. Mater. Interfaces 2020, 12, 7855-7868.
  21. S. L. Anna, H. C. Mayer, Phys. Fluids 2006, 18, 121512.
  22. T. Ward, M. Faivre, M. Abkarian, H. A. Stone, Electrophoresis 2005, 26, 3716-3724.
  23. F. Lapierre, N. Wu, Y. Zhu, Proc. SPIE 2011, 8204, 82040H-1.
  24. C. A. Stan, S. K. Y. Tang, G. M. Whitesides, Anal. Chem. 2009, 81, 2399-2402.
  25. J. Tan, J. H. Xu, S. W. Li, G. S. Luo, Chem. Eng. J. 2008, 136, 306-311.
  26. R.-C. Luo, C.-H. Chen, Soft 2012, 1, 1-23.
  27. C. H. Choi, J. H. Jung, T. S. Hwang, C. S. Lee, Macromol. Res. 2009, 17, 163-167.
  28. A. J. D. Krüger, O. Bakirman, P. B. Guerzoni, A. Jans, D. B. Gehlen, D. Rommel, T. Haraszti, A. J.
    C. Kuehne, L. De Laporte, Adv. Mater. 2019, 31, 1903668.
  29. D. B. Kolesky, K. A. Homan, M. A. Skylar-Scott, J. A. Lewis, Proc. Natl. Acad. Sci. 2016, 113, 3179-3184
  30. A. K. Miri, I. Mirzaee, S. Hassan, S. M. Oskui, D. Nieto, A. Khademhosseini, Y. S. Zhang, Lab Chip 2019, 19, 2019.
  31. F. A. Plamper, W. Richtering Acc. Chem. Res. 2017, 50, 131-140.
  32. S. Sun, M. Li, A. Liu, Int. J. Adhesion Adhesives 2013, 41, 98-106.
Figure 3.10: Snapshots of Temperature Profile for Single Track in Keyhole Regime (P = 250W and V = 0.5m/s) at the Preheating Temperature of 100 °C

Multiscale Process Modeling of Residual Deformation and Defect Formation for Laser Powder Bed Fusion Additive Manufacturing

Qian Chen, PhD
University of Pittsburgh, 2021

레이저 분말 베드 퓨전(L-PBF) 적층 제조(AM)는 우수한 기계적 특성으로 그물 모양에 가까운 복잡한 부품을 생산할 수 있습니다. 그러나 빌드 실패 및 다공성과 같은 결함으로 이어지는 원치 않는 잔류 응력 및 왜곡이 L-PBF의 광범위한 적용을 방해하고 있습니다.

L-PBF의 잠재력을 최대한 실현하기 위해 잔류 변형, 용융 풀 및 다공성 형성을 예측하는 다중 규모 모델링 방법론이 개발되었습니다. L-PBF의 잔류 변형 및 응력을 부품 규모에서 예측하기 위해 고유 변형 ​​방법을 기반으로 하는 다중 규모 프로세스 모델링 프레임워크가 제안됩니다.

고유한 변형 벡터는 마이크로 스케일에서 충실도가 높은 상세한 다층 프로세스 시뮬레이션에서 추출됩니다. 균일하지만 이방성인 변형은 잔류 왜곡 및 응력을 예측하기 위해 준 정적 평형 유한 요소 분석(FEA)에서 레이어별로 L-PBF 부품에 적용됩니다.

부품 규모에서의 잔류 변형 및 응력 예측 외에도 분말 규모의 다중물리 모델링을 수행하여 공정 매개변수, 예열 온도 및 스패터링 입자에 의해 유도된 용융 풀 변동 및 결함 형성을 연구합니다. 이러한 요인과 관련된 용융 풀 역학 및 다공성 형성 메커니즘은 시뮬레이션 및 실험을 통해 밝혀졌습니다.

제안된 부품 규모 잔류 응력 및 왜곡 모델을 기반으로 경로 계획 방법은 큰 잔류 변형 및 건물 파손을 방지하기 위해 주어진 형상에 대한 레이저 스캐닝 경로를 조정하기 위해 개발되었습니다.

연속 및 아일랜드 스캐닝 전략을 위한 기울기 기반 경로 계획이 공식화되고 공식화된 컴플라이언스 및 스트레스 최소화 문제에 대한 전체 감도 분석이 수행됩니다. 이 제안된 경로 계획 방법의 타당성과 효율성은 AconityONE L-PBF 시스템을 사용하여 실험적으로 입증되었습니다.

또한 기계 학습을 활용한 데이터 기반 프레임워크를 개발하여 L-PBF에 대한 부품 규모의 열 이력을 예측합니다. 본 연구에서는 실시간 열 이력 예측을 위해 CNN(Convolutional Neural Network)과 RNN(Recurrent Neural Network)을 포함하는 순차적 기계 학습 모델을 제안합니다.

유한 요소 해석과 비교하여 100배의 예측 속도 향상이 달성되어 실제 제작 프로세스보다 빠른 예측이 가능하고 실시간 온도 프로파일을 사용할 수 있습니다.

Laser powder bed fusion (L-PBF) additive manufacturing (AM) is capable of producing complex parts near net shape with good mechanical properties. However, undesired residual stress and distortion that lead to build failure and defects such as porosity are preventing broader applications of L-PBF. To realize the full potential of L-PBF, a multiscale modeling methodology is developed to predict residual deformation, melt pool, and porosity formation. To predict the residual deformation and stress in L-PBF at part-scale, a multiscale process modeling framework based on inherent strain method is proposed.

Inherent strain vectors are extracted from detailed multi-layer process simulation with high fidelity at micro-scale. Uniform but anisotropic strains are then applied to L-PBF part in a layer-by-layer fashion in a quasi-static equilibrium finite element analysis (FEA) to predict residual distortion and stress. Besides residual distortion and stress prediction at part scale, multiphysics modeling at powder scale is performed to study the melt pool variation and defect formation induced by process parameters, preheating temperature and spattering particles. Melt pool dynamics and porosity formation mechanisms associated with these factors are revealed through simulation and experiments.

Based on the proposed part-scale residual stress and distortion model, path planning method is developed to tailor the laser scanning path for a given geometry to prevent large residual deformation and building failures. Gradient based path planning for continuous and island scanning strategy is formulated and full sensitivity analysis for the formulated compliance- and stress-minimization problem is performed.

The feasibility and effectiveness of this proposed path planning method is demonstrated experimentally using the AconityONE L-PBF system. In addition, a data-driven framework utilizing machine learning is developed to predict the thermal history at part-scale for L-PBF.

In this work, a sequential machine learning model including convolutional neural network (CNN) and recurrent neural network (RNN), long shortterm memory unit, is proposed for real-time thermal history prediction. A 100x prediction speed improvement is achieved compared to the finite element analysis which makes the prediction faster than real fabrication process and real-time temperature profile available.

Figure 1.1: Schematic Overview of Metal Laser Powder Bed Fusion Process [2]
Figure 1.1: Schematic Overview of Metal Laser Powder Bed Fusion Process [2]
Figure 1.2: Commercial Powder Bed Fusion Systems
Figure 1.2: Commercial Powder Bed Fusion Systems
Figure 1.3: Commercial Metal Components Fabricated by Powder Bed Fusion Additive Manufacturing: (a) GE Fuel Nozzle; (b) Stryker Hip Biomedical Implant.
Figure 1.3: Commercial Metal Components Fabricated by Powder Bed Fusion Additive Manufacturing: (a) GE Fuel Nozzle; (b) Stryker Hip Biomedical Implant.
Figure 2.1: Proposed Multiscale Process Simulation Framework
Figure 2.1: Proposed Multiscale Process Simulation Framework
Figure 2.2: (a) Experimental Setup for In-situ Thermocouple Measurement in the EOS M290 Build Chamber; (b) Themocouple Locations on the Bottom Side of the Substrate.
Figure 2.2: (a) Experimental Setup for In-situ Thermocouple Measurement in the EOS M290 Build Chamber; (b) Themocouple Locations on the Bottom Side of the Substrate.
Figure 2.3: (a) Finite Element Model for Single Layer Thermal Analysis; (b) Deposition Layer
Figure 2.3: (a) Finite Element Model for Single Layer Thermal Analysis; (b) Deposition Layer
Figure 2.4: Core-skin layer: (a) Surface Morphology; (b) Scanning Strategy; (c) Transient Temperature Distribution and Temperature History at (d) Point 1; (e) Point 2 and (f) Point 3
Figure 2.4: Core-skin layer: (a) Surface Morphology; (b) Scanning Strategy; (c) Transient Temperature Distribution and Temperature History at (d) Point 1; (e) Point 2 and (f) Point 3
Figure 2.5: (a) Scanning Orientation of Each Layer; (b) Finite Element Model for Micro-scale Representative Volume
Figure 2.5: (a) Scanning Orientation of Each Layer; (b) Finite Element Model for Micro-scale Representative Volume
Figure 2.6: Bottom Layer (a) Thermal History; (b) Plastic Strain and (c) Elastic Strain Evolution History
Figure 2.6: Bottom Layer (a) Thermal History; (b) Plastic Strain and (c) Elastic Strain Evolution History
Figure 2.7: Bottom Layer Inherent Strain under Default Process Parameters along Horizontal Scanning Path
Figure 2.7: Bottom Layer Inherent Strain under Default Process Parameters along Horizontal Scanning Path
Figure 2.8: Snapshots of the Element Activation Process
Figure 2.8: Snapshots of the Element Activation Process
Figure 2.9: Double Cantilever Beam Structure Built by the EOS M290 DMLM Process (a) Before and (b) After Cutting off; (c) Faro Laser ScanArm V3 for Distortion Measurement
Figure 2.9: Double Cantilever Beam Structure Built by the EOS M290 DMLM Process (a) Before and (b) After Cutting off; (c) Faro Laser ScanArm V3 for Distortion Measurement
Figure 2.10: Square Canonical Structure Built by the EOS M290 DMLM Process
Figure 2.10: Square Canonical Structure Built by the EOS M290 DMLM Process
Figure 2.11: Finite Element Mesh for the Square Canonical and Snapshots of Element Activation Process
Figure 2.11: Finite Element Mesh for the Square Canonical and Snapshots of Element Activation Process
Figure 2.12: Simulated Distortion Field for the Double Cantilever Beam before Cutting off the Supports: (a) Inherent Strain Method; (b) Simufact Additive 3.1
Figure 2.12: Simulated Distortion Field for the Double Cantilever Beam before Cutting off the Supports: (a) Inherent Strain Method; (b) Simufact Additive 3.1
Figure 3.10: Snapshots of Temperature Profile for Single Track in Keyhole Regime (P = 250W and V = 0.5m/s) at the Preheating Temperature of 100 °C
Figure 3.10: Snapshots of Temperature Profile for Single Track in Keyhole Regime (P = 250W and V = 0.5m/s) at the Preheating Temperature of 100 °C
s) at the Preheating Temperature of 500 °C
s) at the Preheating Temperature of 500 °C
Figure 3.15: Melt Pool Cross Section Comparison Between Simulation and Experiment for Single Track
Figure 3.15: Melt Pool Cross Section Comparison Between Simulation and Experiment for Single Track

Bibliography

[1] I. Astm, ASTM52900-15 Standard Terminology for Additive Manufacturing—General
Principles—Terminology, ASTM International, West Conshohocken, PA 3(4) (2015) 5.
[2] W.E. King, A.T. Anderson, R.M. Ferencz, N.E. Hodge, C. Kamath, S.A. Khairallah, A.M.
Rubenchik, Laser powder bed fusion additive manufacturing of metals; physics, computational,
and materials challenges, Applied Physics Reviews 2(4) (2015) 041304.
[3] W. Yan, Y. Lu, K. Jones, Z. Yang, J. Fox, P. Witherell, G. Wagner, W.K. Liu, Data-driven
characterization of thermal models for powder-bed-fusion additive manufacturing, Additive
Manufacturing (2020) 101503.
[4] K. Dai, L. Shaw, Thermal and stress modeling of multi-material laser processing, Acta
Materialia 49(20) (2001) 4171-4181.
[5] K. Dai, L. Shaw, Distortion minimization of laser-processed components through control of
laser scanning patterns, Rapid Prototyping Journal 8(5) (2002) 270-276.
[6] S.S. Bo Cheng, Kevin Chou, Stress and deformation evaluations of scanning strategy effect in
selective laser melting, Additive Manufacturing (2017).
[7] C. Fu, Y. Guo, Three-dimensional temperature gradient mechanism in selective laser melting
of Ti-6Al-4V, Journal of Manufacturing Science and Engineering 136(6) (2014) 061004.
[8] P. Prabhakar, W.J. Sames, R. Dehoff, S.S. Babu, Computational modeling of residual stress
formation during the electron beam melting process for Inconel 718, Additive Manufacturing 7
(2015) 83-91.
[9] A. Hussein, L. Hao, C. Yan, R. Everson, Finite element simulation of the temperature and
stress fields in single layers built without-support in selective laser melting, Materials & Design
(1980-2015) 52 (2013) 638-647.
[10] P.Z. Qingcheng Yang, Lin Cheng, Zheng Min, Minking Chyu, Albert C. To, articleFinite
element modeling and validation of thermomechanicalbehavior of Ti-6Al-4V in directed energy
deposition additivemanufacturing, Additive Manufacturing (2016).
[11] E.R. Denlinger, J. Irwin, P. Michaleris, Thermomechanical Modeling of Additive
Manufacturing Large Parts, Journal of Manufacturing Science and Engineering 136(6) (2014)
061007.
[12] E.R. Denlinger, M. Gouge, J. Irwin, P. Michaleris, Thermomechanical model development
and in situ experimental validation of the Laser Powder-Bed Fusion process, Additive
Manufacturing 16 (2017) 73-80.
[13] V.J. Erik R Denlinger, G.V. Srinivasan, Tahany EI-Wardany, Pan Michaleris, Thermal
modeling of Inconel 718 processed with powder bed fusionand experimental validation using in
situ measurements, Additive Manufacturing 11 (2016) 7-15.
[14] N. Patil, D. Pal, H.K. Rafi, K. Zeng, A. Moreland, A. Hicks, D. Beeler, B. Stucker, A
Generalized Feed Forward Dynamic Adaptive Mesh Refinement and Derefinement Finite Element
Framework for Metal Laser Sintering—Part I: Formulation and Algorithm Development, Journal
of Manufacturing Science and Engineering 137(4) (2015) 041001.
[15] D. Pal, N. Patil, K.H. Kutty, K. Zeng, A. Moreland, A. Hicks, D. Beeler, B. Stucker, A
Generalized Feed-Forward Dynamic Adaptive Mesh Refinement and Derefinement FiniteElement Framework for Metal Laser Sintering—Part II: Nonlinear Thermal Simulations and
Validations, Journal of Manufacturing Science and Engineering 138(6) (2016) 061003.
[16] N. Keller, V. Ploshikhin, New method for fast predictions of residual stress and distortion of
AM parts, Solid Freeform Fabrication Symposium, Austin, Texas, 2014, pp. 1229-1237.
[17] S.A. Khairallah, A.T. Anderson, A. Rubenchik, W.E. King, Laser powder-bed fusion additive
manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and
denudation zones, Acta Materialia 108 (2016) 36-45.
[18] M.J. Matthews, G. Guss, S.A. Khairallah, A.M. Rubenchik, P.J. Depond, W.E. King,
Denudation of metal powder layers in laser powder bed fusion processes, Acta Materialia 114
(2016) 33-42.
[19] A.A. Martin, N.P. Calta, S.A. Khairallah, J. Wang, P.J. Depond, A.Y. Fong, V. Thampy, G.M.
Guss, A.M. Kiss, K.H. Stone, Dynamics of pore formation during laser powder bed fusion additive
manufacturing, Nature communications 10(1) (2019) 1987.
[20] R. Shi, S.A. Khairallah, T.T. Roehling, T.W. Heo, J.T. McKeown, M.J. Matthews,
Microstructural control in metal laser powder bed fusion additive manufacturing using laser beam
shaping strategy, Acta Materialia (2019).
[21] S.A. Khairallah, A.A. Martin, J.R. Lee, G. Guss, N.P. Calta, J.A. Hammons, M.H. Nielsen,
K. Chaput, E. Schwalbach, M.N. Shah, Controlling interdependent meso-nanosecond dynamics
and defect generation in metal 3D printing, Science 368(6491) (2020) 660-665.
[22] W. Yan, W. Ge, Y. Qian, S. Lin, B. Zhou, W.K. Liu, F. Lin, G.J. Wagner, Multi-physics
modeling of single/multiple-track defect mechanisms in electron beam selective melting, Acta
Materialia 134 (2017) 324-333.
[23] S. Shrestha, Y. Kevin Chou, A Numerical Study on the Keyhole Formation During Laser
Powder Bed Fusion Process, Journal of Manufacturing Science and Engineering 141(10) (2019).
[24] S. Shrestha, B. Cheng, K. Chou, An Investigation into Melt Pool Effective Thermal
Conductivity for Thermal Modeling of Powder-Bed Electron Beam Additive Manufacturing.
[25] D. Rosenthal, Mathematical theory of heat distribution during welding and cutting, Welding
journal 20 (1941) 220-234.
[26] P. Promoppatum, S.-C. Yao, P.C. Pistorius, A.D. Rollett, A comprehensive comparison of the
analytical and numerical prediction of the thermal history and solidification microstructure of
Inconel 718 products made by laser powder-bed fusion, Engineering 3(5) (2017) 685-694.
[27] M. Tang, P.C. Pistorius, J.L. Beuth, Prediction of lack-of-fusion porosity for powder bed
fusion, Additive Manufacturing 14 (2017) 39-48.
[28] T. Moran, P. Li, D. Warner, N. Phan, Utility of superposition-based finite element approach
for part-scale thermal simulation in additive manufacturing, Additive Manufacturing 21 (2018)
215-219.
[29] Y. Yang, M. Knol, F. van Keulen, C. Ayas, A semi-analytical thermal modelling approach
for selective laser melting, Additive Manufacturing 21 (2018) 284-297.
[30] B. Cheng, S. Shrestha, K. Chou, Stress and deformation evaluations of scanning strategy
effect in selective laser melting, Additive Manufacturing 12 (2016) 240-251.
[31] L.H. Ahmed Hussein, Chunze Yan, Richard Everson, Finite element simulation of the
temperature and stress fields in single layers built without-support in selective laser melting,
Materials and Design 52 (2013) 638-647.
[32] H. Peng, D.B. Go, R. Billo, S. Gong, M.R. Shankar, B.A. Gatrell, J. Budzinski, P. Ostiguy,
R. Attardo, C. Tomonto, Part-scale model for fast prediction of thermal distortion in DMLS
additive manufacturing; Part 2: a quasi-static thermo-mechanical model, Austin, Texas (2016).
[33] M.F. Zaeh, G. Branner, Investigations on residual stresses and deformations in selective laser
melting, Production Engineering 4(1) (2010) 35-45.
[34] C. Li, C. Fu, Y. Guo, F. Fang, A multiscale modeling approach for fast prediction of part
distortion in selective laser melting, Journal of Materials Processing Technology 229 (2016) 703-
712.
[35] C. Li, Z. Liu, X. Fang, Y. Guo, On the Simulation Scalability of Predicting Residual Stress
and Distortion in Selective Laser Melting, Journal of Manufacturing Science and Engineering
140(4) (2018) 041013.
[36] S. Afazov, W.A. Denmark, B.L. Toralles, A. Holloway, A. Yaghi, Distortion Prediction and
Compensation in Selective Laser Melting, Additive Manufacturing 17 (2017) 15-22.
[37] Y. Lee, W. Zhang, Modeling of heat transfer, fluid flow and solidification microstructure of
nickel-base superalloy fabricated by laser powder bed fusion, Additive Manufacturing 12 (2016)
178-188.
[38] L. Scime, J. Beuth, A multi-scale convolutional neural network for autonomous anomaly
detection and classification in a laser powder bed fusion additive manufacturing process, Additive
Manufacturing 24 (2018) 273-286.
[39] L. Scime, J. Beuth, Using machine learning to identify in-situ melt pool signatures indicative
of flaw formation in a laser powder bed fusion additive manufacturing process, Additive
Manufacturing 25 (2019) 151-165.
[40] X. Xie, J. Bennett, S. Saha, Y. Lu, J. Cao, W.K. Liu, Z. Gan, Mechanistic data-driven
prediction of as-built mechanical properties in metal additive manufacturing, npj Computational
Materials 7(1) (2021) 1-12.
[41] C. Wang, X. Tan, S. Tor, C. Lim, Machine learning in additive manufacturing: State-of-theart and perspectives, Additive Manufacturing (2020) 101538.
[42] J. Li, R. Jin, Z.Y. Hang, Integration of physically-based and data-driven approaches for
thermal field prediction in additive manufacturing, Materials & Design 139 (2018) 473-485.
[43] M. Mozaffar, A. Paul, R. Al-Bahrani, S. Wolff, A. Choudhary, A. Agrawal, K. Ehmann, J.
Cao, Data-driven prediction of the high-dimensional thermal history in directed energy deposition
processes via recurrent neural networks, Manufacturing letters 18 (2018) 35-39.
[44] A. Paul, M. Mozaffar, Z. Yang, W.-k. Liao, A. Choudhary, J. Cao, A. Agrawal, A real-time
iterative machine learning approach for temperature profile prediction in additive manufacturing
processes, 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA),
IEEE, 2019, pp. 541-550.
[45] S. Clijsters, T. Craeghs, J.-P. Kruth, A priori process parameter adjustment for SLM process
optimization, Innovative developments on virtual and physical prototyping, Taylor & Francis
Group., 2012, pp. 553-560.
[46] R. Mertens, S. Clijsters, K. Kempen, J.-P. Kruth, Optimization of scan strategies in selective
laser melting of aluminum parts with downfacing areas, Journal of Manufacturing Science and
Engineering 136(6) (2014) 061012.
[47] J.-P. Kruth, J. Deckers, E. Yasa, R. Wauthlé, Assessing and comparing influencing factors of
residual stresses in selective laser melting using a novel analysis method, Proceedings of the
institution of mechanical engineers, Part B: Journal of Engineering Manufacture 226(6) (2012)
980-991.
[48] Y. Lu, S. Wu, Y. Gan, T. Huang, C. Yang, L. Junjie, J. Lin, Study on the microstructure,
mechanical property and residual stress of SLM Inconel-718 alloy manufactured by differing
island scanning strategy, Optics & Laser Technology 75 (2015) 197-206.
[49] E. Foroozmehr, R. Kovacevic, Effect of path planning on the laser powder deposition process:
thermal and structural evaluation, The International Journal of Advanced Manufacturing
Technology 51(5-8) (2010) 659-669.
[50] L.H. Ahmed Hussein, Chunze Yan, Richard Everson, Finite element simulation of the
temperature and stress fields in single layers built without-support in selective laser melting,
Materials and Design (2013).
[51] J.-P. Kruth, M. Badrossamay, E. Yasa, J. Deckers, L. Thijs, J. Van Humbeeck, Part and
material properties in selective laser melting of metals, Proceedings of the 16th international
symposium on electromachining, 2010, pp. 1-12.
[52] L. Thijs, K. Kempen, J.-P. Kruth, J. Van Humbeeck, Fine-structured aluminium products with
controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder, Acta Materialia
61(5) (2013) 1809-1819.
[53] D. Ding, Z.S. Pan, D. Cuiuri, H. Li, A tool-path generation strategy for wire and arc additive
manufacturing, The international journal of advanced manufacturing technology 73(1-4) (2014)
173-183.
[54] B.E. Carroll, T.A. Palmer, A.M. Beese, Anisotropic tensile behavior of Ti–6Al–4V
components fabricated with directed energy deposition additive manufacturing, Acta Materialia
87 (2015) 309-320.
[55] D. Ding, Z. Pan, D. Cuiuri, H. Li, A practical path planning methodology for wire and arc
additive manufacturing of thin-walled structures, Robotics and Computer-Integrated
Manufacturing 34 (2015) 8-19.
[56] D. Ding, Z. Pan, D. Cuiuri, H. Li, S. van Duin, N. Larkin, Bead modelling and implementation
of adaptive MAT path in wire and arc additive manufacturing, Robotics and Computer-Integrated
Manufacturing 39 (2016) 32-42.
[57] R. Ponche, O. Kerbrat, P. Mognol, J.-Y. Hascoet, A novel methodology of design for Additive
Manufacturing applied to Additive Laser Manufacturing process, Robotics and ComputerIntegrated Manufacturing 30(4) (2014) 389-398.
[58] D.E. Smith, R. Hoglund, Continuous fiber angle topology optimization for polymer fused
fillament fabrication, Annu. Int. Solid Free. Fabr. Symp. Austin, TX, 2016.
[59] J. Liu, J. Liu, H. Yu, H. Yu, Concurrent deposition path planning and structural topology
optimization for additive manufacturing, Rapid Prototyping Journal 23(5) (2017) 930-942.
[60] Q. Xia, T. Shi, Optimization of composite structures with continuous spatial variation of fiber
angle through Shepard interpolation, Composite Structures 182 (2017) 273-282.
[61] C. Kiyono, E. Silva, J. Reddy, A novel fiber optimization method based on normal distribution
function with continuously varying fiber path, Composite Structures 160 (2017) 503-515.
[62] C.J. Brampton, K.C. Wu, H.A. Kim, New optimization method for steered fiber composites
using the level set method, Structural and Multidisciplinary Optimization 52(3) (2015) 493-505.
[63] J. Liu, A.C. To, Deposition path planning-integrated structural topology optimization for 3D
additive manufacturing subject to self-support constraint, Computer-Aided Design 91 (2017) 27-
45.
[64] H. Shen, J. Fu, Z. Chen, Y. Fan, Generation of offset surface for tool path in NC machining
through level set methods, The International Journal of Advanced Manufacturing Technology
46(9-12) (2010) 1043-1047.
[65] C. Zhuang, Z. Xiong, H. Ding, High speed machining tool path generation for pockets using
level sets, International Journal of Production Research 48(19) (2010) 5749-5766.
[66] K.C. Mills, Recommended values of thermophysical properties for selected commercial
alloys, Woodhead Publishing2002.
[67] S.S. Sih, J.W. Barlow, The prediction of the emissivity and thermal conductivity of powder
beds, Particulate Science and Technology 22(4) (2004) 427-440.
[68] L. Dong, A. Makradi, S. Ahzi, Y. Remond, Three-dimensional transient finite element
analysis of the selective laser sintering process, Journal of materials processing technology 209(2)
(2009) 700-706.
[69] J.J. Beaman, J.W. Barlow, D.L. Bourell, R.H. Crawford, H.L. Marcus, K.P. McAlea, Solid
freeform fabrication: a new direction in manufacturing, Kluwer Academic Publishers, Norwell,
MA 2061 (1997) 25-49.
[70] G. Bugeda Miguel Cervera, G. Lombera, Numerical prediction of temperature and density
distributions in selective laser sintering processes, Rapid Prototyping Journal 5(1) (1999) 21-26.
[71] T. Mukherjee, W. Zhang, T. DebRoy, An improved prediction of residual stresses and
distortion in additive manufacturing, Computational Materials Science 126 (2017) 360-372.
[72] A.J. Dunbar, E.R. Denlinger, M.F. Gouge, P. Michaleris, Experimental validation of finite
element modeling for laser powderbed fusion deformation, Additive Manufacturing 12 (2016)
108-120.
[73] J. Goldak, A. Chakravarti, M. Bibby, A new finite element model for welding heat sources,
Metallurgical and Materials Transactions B 15(2) (1984) 299-305.
[74] J. Liu, Q. Chen, Y. Zhao, W. Xiong, A. To, Quantitative Texture Prediction of Epitaxial
Columnar Grains in Alloy 718 Processed by Additive Manufacturing, Proceedings of the 9th
International Symposium on Superalloy 718 & Derivatives: Energy, Aerospace, and Industrial
Applications, Springer, 2018, pp. 749-755.
[75] J. Irwin, P. Michaleris, A line heat input model for additive manufacturing, Journal of
Manufacturing Science and Engineering 138(11) (2016) 111004.
[76] M. Gouge, J. Heigel, P. Michaleris, T. Palmer, Modeling forced convection in the thermal
simulation of laser cladding processes, International Journal of Advanced Manufacturing
Technology 79 (2015).
[77] J. Heigel, P. Michaleris, E. Reutzel, Thermo-mechanical model development and validation
of directed energy deposition additive manufacturing of Ti–6Al–4V, Additive manufacturing 5
(2015) 9-19.
[78] E.R. Denlinger, J.C. Heigel, P. Michaleris, Residual stress and distortion modeling of electron
beam direct manufacturing Ti-6Al-4V, Proceedings of the Institution of Mechanical Engineers,
Part B: Journal of Engineering Manufacture 229(10) (2015) 1803-1813.
[79] X. Liang, Q. Chen, L. Cheng, Q. Yang, A. To, A modified inherent strain method for fast
prediction of residual deformation in additive manufacturing of metal parts, 2017 Solid Freeform
Fabrication Symposium Proceedings, Austin, Texas, 2017.
[80] X. Liang, L. Cheng, Q. Chen, Q. Yang, A. To, A Modified Method for Estimating Inherent
Strains from Detailed Process Simulation for Fast Residual Distortion Prediction of Single-Walled
Structures Fabricated by Directed Energy Deposition, Additive Manufacturing 23 (2018) 471-486.
[81] L. Sochalski-Kolbus, E.A. Payzant, P.A. Cornwell, T.R. Watkins, S.S. Babu, R.R. Dehoff,
M. Lorenz, O. Ovchinnikova, C. Duty, Comparison of residual stresses in Inconel 718 simple parts
made by electron beam melting and direct laser metal sintering, Metallurgical and Materials
Transactions A 46(3) (2015) 1419-1432.
[82] P. Mercelis, J.-P. Kruth, Residual stresses in selective laser sintering and selective laser
melting, Rapid Prototyping Journal 12(5) (2006) 254-265.
[83] N. Hodge, R. Ferencz, J. Solberg, Implementation of a thermomechanical model for the
simulation of selective laser melting, Computational Mechanics 54(1) (2014) 33-51.
[84] A.S. Wu, D.W. Brown, M. Kumar, G.F. Gallegos, W.E. King, An experimental investigation
into additive manufacturing-induced residual stresses in 316L stainless steel, Metallurgical and
Materials Transactions A 45(13) (2014) 6260-6270.
[85] C. Li, J. liu, Y. Guo, Efficient predictive model of part distortion and residual stress in
selective laser melting, Solid Freeform Fabrication 2016, 2017.
[86] Y. Zhao, Y. Koizumi, K. Aoyagi, D. Wei, K. Yamanaka, A. Chiba, Molten pool behavior and
effect of fluid flow on solidification conditions in selective electron beam melting (SEBM) of a
biomedical Co-Cr-Mo alloy, Additive Manufacturing 26 (2019) 202-214.
[87] J.-H. Cho, S.-J. Na, Implementation of real-time multiple reflection and Fresnel absorption of
laser beam in keyhole, Journal of Physics D: Applied Physics 39(24) (2006) 5372.
[88] Q. Guo, C. Zhao, M. Qu, L. Xiong, L.I. Escano, S.M.H. Hojjatzadeh, N.D. Parab, K. Fezzaa,
W. Everhart, T. Sun, In-situ characterization and quantification of melt pool variation under
constant input energy density in laser powder-bed fusion additive manufacturing process, Additive
Manufacturing (2019).
[89] E. Assuncao, S. Williams, D. Yapp, Interaction time and beam diameter effects on the
conduction mode limit, Optics and Lasers in Engineering 50(6) (2012) 823-828.
[90] R. Cunningham, C. Zhao, N. Parab, C. Kantzos, J. Pauza, K. Fezzaa, T. Sun, A.D. Rollett,
Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging,
Science 363(6429) (2019) 849-852.
[91] W. Tan, N.S. Bailey, Y.C. Shin, Investigation of keyhole plume and molten pool based on a
three-dimensional dynamic model with sharp interface formulation, Journal of Physics D: Applied
Physics 46(5) (2013) 055501.
[92] W. Tan, Y.C. Shin, Analysis of multi-phase interaction and its effects on keyhole dynamics
with a multi-physics numerical model, Journal of Physics D: Applied Physics 47(34) (2014)
345501.
[93] R. Fabbro, K. Chouf, Keyhole modeling during laser welding, Journal of applied Physics
87(9) (2000) 4075-4083.
[94] Q. Guo, C. Zhao, M. Qu, L. Xiong, S.M.H. Hojjatzadeh, L.I. Escano, N.D. Parab, K. Fezzaa,
T. Sun, L. Chen, In-situ full-field mapping of melt flow dynamics in laser metal additive
manufacturing, Additive Manufacturing 31 (2020) 100939.
[95] Y. Ueda, K. Fukuda, K. Nakacho, S. Endo, A new measuring method of residual stresses with
the aid of finite element method and reliability of estimated values, Journal of the Society of Naval
Architects of Japan 1975(138) (1975) 499-507.
[96] M.R. Hill, D.V. Nelson, The inherent strain method for residual stress determination and its
application to a long welded joint, ASME-PUBLICATIONS-PVP 318 (1995) 343-352.
[97] H. Murakawa, Y. Luo, Y. Ueda, Prediction of welding deformation and residual stress by
elastic FEM based on inherent strain, Journal of the society of Naval Architects of Japan 1996(180)
(1996) 739-751.
[98] M. Yuan, Y. Ueda, Prediction of residual stresses in welded T-and I-joints using inherent
strains, Journal of Engineering Materials and Technology, Transactions of the ASME 118(2)
(1996) 229-234.
[99] L. Zhang, P. Michaleris, P. Marugabandhu, Evaluation of applied plastic strain methods for
welding distortion prediction, Journal of Manufacturing Science and Engineering 129(6) (2007)
1000-1010.
[100] M. Bugatti, Q. Semeraro, Limitations of the Inherent Strain Method in Simulating Powder
Bed Fusion Processes, Additive Manufacturing 23 (2018) 329-346.
[101] L. Cheng, X. Liang, J. Bai, Q. Chen, J. Lemon, A. To, On Utilizing Topology Optimization
to Design Support Structure to Prevent Residual Stress Induced Build Failure in Laser Powder Bed
Metal Additive Manufacturing, Additive Manufacturing (2019).
[102] Q. Chen, X. Liang, D. Hayduke, J. Liu, L. Cheng, J. Oskin, R. Whitmore, A.C. To, An
inherent strain based multiscale modeling framework for simulating part-scale residual
deformation for direct metal laser sintering, Additive Manufacturing 28 (2019) 406-418.
[103] S. Osher, J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based
on Hamilton-Jacobi formulations, Journal of computational physics 79(1) (1988) 12-49.
[104] M.Y. Wang, X. Wang, D. Guo, A level set method for structural topology optimization,
Computer methods in applied mechanics and engineering 192(1) (2003) 227-246.
[105] G. Allaire, F. Jouve, A.-M. Toader, Structural optimization using sensitivity analysis and a
level-set method, Journal of computational physics 194(1) (2004) 363-393.
[106] Y. Wang, Z. Luo, Z. Kang, N. Zhang, A multi-material level set-based topology and shape
optimization method, Computer Methods in Applied Mechanics and Engineering 283 (2015)
1570-1586.
[107] P. Dunning, C. Brampton, H. Kim, Simultaneous optimisation of structural topology and
material grading using level set method, Materials Science and Technology 31(8) (2015) 884-894.
[108] P. Liu, Y. Luo, Z. Kang, Multi-material topology optimization considering interface
behavior via XFEM and level set method, Computer methods in applied mechanics and
engineering 308 (2016) 113-133.
[109] J. Liu, Q. Chen, Y. Zheng, R. Ahmad, J. Tang, Y. Ma, Level set-based heterogeneous object
modeling and optimization, Computer-Aided Design (2019).
[110] J. Liu, Q. Chen, X. Liang, A.C. To, Manufacturing cost constrained topology optimization
for additive manufacturing, Frontiers of Mechanical Engineering 14(2) (2019) 213-221.
[111] Z. Kang, Y. Wang, Integrated topology optimization with embedded movable holes based
on combined description by material density and level sets, Computer methods in applied
mechanics and engineering 255 (2013) 1-13.
[112] P.D. Dunning, H. Alicia Kim, A new hole insertion method for level set based structural
topology optimization, International Journal for Numerical Methods in Engineering 93(1) (2013)
118-134.
[113] J.A. Sethian, A fast marching level set method for monotonically advancing fronts,
Proceedings of the National Academy of Sciences 93(4) (1996) 1591-1595.
[114] J.A. Sethian, Level set methods and fast marching methods: evolving interfaces in
computational geometry, fluid mechanics, computer vision, and materials science, Cambridge
university press1999.
[115] C. Le, J. Norato, T. Bruns, C. Ha, D. Tortorelli, Stress-based topology optimization for
continua, Structural and Multidisciplinary Optimization 41(4) (2010) 605-620.
[116] A. Takezawa, G.H. Yoon, S.H. Jeong, M. Kobashi, M. Kitamura, Structural topology
optimization with strength and heat conduction constraints, Computer Methods in Applied
Mechanics and Engineering 276 (2014) 341-361.
[117] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9(8) (1997)
1735-1780.
[118] A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional
neural networks, Advances in neural information processing systems 25 (2012) 1097-1105.
[119] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image
recognition, arXiv preprint arXiv:1409.1556 (2014).
[120] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, Proceedings
of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[121] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A.
Khosla, M. Bernstein, Imagenet large scale visual recognition challenge, International journal of
computer vision 115(3) (2015) 211-252.
[122] S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with
region proposal networks, Advances in neural information processing systems 28 (2015) 91-99.
[123] E.J. Schwalbach, S.P. Donegan, M.G. Chapman, K.J. Chaput, M.A. Groeber, A discrete
source model of powder bed fusion additive manufacturing thermal history, Additive
Manufacturing 25 (2019) 485-498.
[124] D.G. Duffy, Green’s functions with applications, Chapman and Hall/CRC2015.
[125] J. Martínez-Frutos, D. Herrero-Pérez, Efficient matrix-free GPU implementation of fixed
grid finite element analysis, Finite Elements in Analysis and Design 104 (2015) 61-71.
[126] F. Dugast, P. Apostolou, A. Fernandez, W. Dong, Q. Chen, S. Strayer, R. Wicker, A.C. To,
Part-scale thermal process modeling for laser powder bed fusion with matrix-free method and GPU
computing, Additive Manufacturing 37 (2021) 101732.
[127] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I.
Polosukhin, Attention is all you need, Advances in neural information processing systems, 2017,
pp. 5998-6008.
[128] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional
transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018).

Figure 2 Idea and details of T-shaped weir.

Introducing the T-shaped weir: a new nonlinear weir

Behzad NorooziJalal BazarganAkbar Safarzadeh

Abstract

본 연구에서는 LW(Labyrinth Weir)와 PKW(Piano Key Weir)가 결합된 T자형 웨어(TSW)라는 새로운 비선형 웨어를 도입하여 수압 성능을 비교하였다.

PKW. 입구 키, 출구 키 또는 두 키 모두에서 수직 벽의 존재에 따라 TSW 위어는 각각 A, B 또는 C 유형 웨어로 분류되었습니다. 다른 TSW 사례의 흐름 패턴을 분석하고 배출 계수 곡선을 제공했습니다. 또한 테스트된 둑의 유체역학을 정확하게 연구하기 위해 FLOW-3D 소프트웨어를 사용하여 3D 수치 시뮬레이션을 수행했습니다.

결과는 출구 키(C-TSW 유형)의 상류에 수직 벽을 삽입하는 것이 PKW의 유압 성능에 미미한 영향을 미치는 것으로 나타났습니다. B-TSW의 토출계수는 PKW 대비 최대 16% 증가하였으며, Ht/p 0.45까지 수직벽의 성능향상 효과 증가 B-TSW는 유지되었습니다.

실험적 및 수치적 실험을 통해 가장 높은 방전 용량을 갖는 B-TSW에서 수직벽의 최적 높이비(Pd/P)는 0.4로 결정되었다.

In the present study, a new nonlinear weir called the T-shaped weir (TSW), which is a combination of the labyrinth weir (LW) and the piano key weir (PKW), was introduced, and its hydraulic performance was compared with the PKW. Based on the presence of the vertical walls at the inlet key, outlet key, or both keys, the TSW weirs were classified as type A, B, or C weirs, respectively. The flow pattern of different TSW cases was analyzed, and the discharge coefficient curves were provided. Furthermore, to accurately study the hydrodynamics of the tested weirs, 3D numerical simulations were performed using the FLOW-3D software. The results showed that inserting a vertical wall at the upstream of the outlet keys (C-TSW type) has a negligible effect on the hydraulic performance of the PKW. A maximum increase of 16% occurred in the discharge coefficient of the B-TSW in comparison to the PKW, and up to a head to height ratio (Ht/p) of 0.45, the effect of the vertical wall on increasing the performance of the B-TSW was maintained. Based on the experimental and numerical tests, the optimal height ratio of the vertical wall (Pd/P) in B-TSW with highest discharge capacity was determined to be equal to 0.4.

HIGHLIGHTS

Listen

  • A new nonlinear weir called the T-shaped weir (TSW), which is a combination of the labyrinth weir (LW) and the piano key weir (PKW), is introduced.
  • To investigate the hydrodynamics of the tested weirs in more detail, 3D numerical models are developed on the CFD-software FLOW-3D.
  • By testing different vertical wall sizes, the optimal size of the vertical wall is determined for B-TSW weir.

Keywords

discharge coefficientlabyrinth weirlocal submergencepiano key weirT-shaped weir

Figure 2 Idea and details of T-shaped weir.
Figure 2 Idea and details of T-shaped weir.

Figure 19. Water surface profile at the middle part of the inlet key for H/P = 0.4.
Figure 19. Water surface profile at the middle part of the inlet key for H/P = 0.4.
Figure 21 Transverse water surface profile in the outlet key of tested weirs  for H/P = 0.4.
Figure 21 Transverse water surface profile in the outlet key of tested weirs for H/P = 0.4.

REFERENCES

Anderson R. M. & Tullis B. P. 2011 Influence of Piano Key Weirs Geometry on Discharge. In Labyrinth and Piano Key Weirs – PKW 2011. CRC Press, Leiden, pp. 75–80.

Anderson R. M. & Tullis B. P. 2012 Comparison of piano key and rectangular labyrinth weir hydraulics. Journal of Hydraulic Engineering 138 (4), 358–361.

Azamathulla H. M., Haghiabi A. H. & Parsaie A. 2016 Prediction of side weir discharge coefficient by support vector machine technique. Water Science and Technology: Water Supply 16 (4), 1002–1016.

Bremer F. L. & Oertel M. 2017 Numerical investigation of wall thickness influence on Piano key Weir discharge coefficients: A preliminary study. In Labyrinth and Piano Key Weirs III – PKW 2017. CRC Press, London, UK, pp. 101–108.

Cicero G. M., Delisle J. R., Lefebvre V. & Vermeulen J. 2013 Experimental and Numerical Study of the Hydraulic Performance of A Trapezoidal PKW. In Labyrinths and Piano Key Weirs PKW 2013. CRC Press, Boca Raton, FL, pp. 265–272.

Crookston B. & Tullis B. 2012 Labyrinth Weirs: Nappe interference and local submergence. Journal of Irrigation and Drainage Engineering 138 (8), 757–765.

Crookston B., Anderson R. M. & Tullis B. P. 2017 Free-flow discharge estimation method for piano key weir geometries. Journal of Hydro-Environment Research 19, 60–167.

Ghasemlounia R. & Saghebian S. M. 2021 Uncertainty assessment of kernel based approaches on scour depth modeling in downstream of ski-jump bucket spillways. Water Supply 21 (5), 2333–2346. doi:10.2166/ws.2021.063.

Kabiri-Samani A. & Javaheri A. 2012 Discharge coefficients for free and submerged flow over piano key weirs. Journal of Hydraulic Research 50 (1), 114–120.

Lefebvre V., Vermeulen J. & Blancher B. 2013 Influence of Geometrical Parameters on PK-Weirs Discharge with 3D Numerical Analysis. In: Labyrinth and Piano key Weirs II – PKW 2013. CRC Press, London, pp. 49–56.

Lempérière F. & Ouamane A. 2003 The piano keys weir: a new cost-effective solution for spillways. International Journal on Hydropower & Dams 10 (5), 144–149.

Machiels O., Erpicum S., Archambeau P., Dewals B. J. & Pirotton M. 2011 Influence of piano key weir height on its discharge capacity. In Proc. Int. Conf. Labyrinth and Piano Key Weirs Liège B, pp. 59–66.

Machiels O., Pirotton M., Pierre A., Dewals B. & Erpicum S. 2014 Experimental parametric study and design of piano key weirs. Journal of Hydraulic Research 52 (3), 326–335.

Parsaie A., Azamathulla H. M. & Haghiabi A. H. 2018 Prediction of discharge coefficient of cylindrical weir-gate using GMDH-PSO. ISH Journal of Hydraulic Engineering 24 (2), 116–123.

Paxson G. & Savage B. 2006 Labyrinth spillways: comparison of two popular USA design methods and consideration of non-standard approach conditions and geometries. Division of Civil Engineering, p.37.

Pourshahbaz H., Abbasi S., Pandey M., Pu J. H., Taghvaei P. & Tofangdar N. 2020 Morphology and hydrodynamics numerical simulation around groynes. ISH Journal of Hydraulic Engineering 1–9.

Pralong J., Vermeulen J., Blancher B., Laugier F., Erpicum S., Machiels O., Pirotton M., Boillat J.-L., Leite Ribeiro M. & Schleiss A. 2011a A naming convention for the Piano Key Weirs geometrical parameters. In Labyrinth and Piano key Weirs – PKW 2011. CRC Press, London, pp. 271–278.

Pralong J., Montarros F., Blancher B. & Laugier F. 2011b A sensitivity analysis of Piano Key Weirs geometrical parameters based on 3D numerical modelling. In Labyrinth and Piano key Weirs – PKW 2011. CRC Press, London, pp. 133–139.

Ribeiro M. L., Bieri M., Boillat J. L., Schleiss A., Delorme F. & Laugier F. 2009 Hydraulic capacity improvement of existing spillways–design of a piano key weir. In Proceedings (on CD) of the 23rd Congress of the Int. Commission on Large Dams CIGB-ICOLD, Brasilia, Vol. 2, No. CONF, pp. 100–118.

Ribeiro M. L., Pfister M., Schleiss A. J. & Boillat J. L. 2012 Hydraulic design of A-type piano key weirs. Journal of Hydraulic Research 50 (4), 400–408.

Roache P. 1994 Perspective: a method for uniform reporting of grid refinement studies. Journal of Fluids Engineering 116 (3), 405–413.

Safarzadeh A. & Mohajeri S. H. 2018 Hydrodynamics of rectangular broad-crested porous weirs. Journal of Irrigation and Drainage Engineering 144 (10), 04018028.

Safarzadeh A. & Noroozi B. 2014 Three dimensional hydrodynamics of arced piano key spillways. Journal of Hydraulics 9 (3), 61–79.

Safarzadeh A. & Noroozi B. 2017 3D hydrodynamics of trapezoidal piano key spillways. International Journal of Civil Engineering 15 (1), 89–101.

Safarzadeh A., Zaji A. H. & Bonakdari H. 2017 Comparative assessment of the hybrid genetic algorithm–artificial neural network and genetic programming methods for the prediction of longitudinal velocity field around a single straight groyne. Applied Soft Computing 60, 213–228.

Tullis B. P., Young J. C. & Chandler M. A. 2007 Head-discharge relationships for submerged labyrinth weirs. Journal of Hydraulic Engineering 133 (3), 248–254.

Xinlei G., Zhiping L., Tao Wang H., Jiazhen L., Qingfu X. & Yongxin G. 2019 Discharge capacity evaluation and hydraulic design of a piano key weir. Water Supply 19 (3), 871–878.

Zahiri A., Azamathulla H. M. & Bagheri S. 2013 Discharge coefficient for compound sharp crested side weirs in subcritical flow conditions. Journal of Hydrology 480, 162–166.

Zahiri A., Tang X. & Azamathulla H. M. 2014 Mathematical modeling of flow discharge over compound sharp-crested weirs. Journal of Hydro-Environment Research 8 (3), 194–199.

Fig. 4. Numerical modeling of dual spillways: (a) Andong-1; (b) Andong-2; (c) Imha-1; (d) Juam-1; (e) Andong-3; (f) Imha-2; (g) Imha-3; and (h) Juam-3.

Interference of Dual Spillways Operations

Jai Hong Lee, Ph.D., P.E., M.ASCE; Pierre Y. Julien, Ph.D., M.ASCE; and Christopher I. Thornton, Ph.D., P.E., M.ASCE

Abstract

이중 여수로 간섭은 여수로가 서로 가깝게 배치될 때 수압 성능의 손실을 나타냅니다. 배수로 간섭은 물리적 실험과 수치 시뮬레이션을 모두 사용하여 조사됩니다.

이중 여수로 구성의 4개 물리적 모델의 단계 및 배출 측정값을 한국의 4개 댐 부지에서 Flow-3D 계산 결과와 비교합니다.

두 개의 배수로를 함께 사용하는 것을 각 배수로의 단일 작동과 비교합니다. 두 여수로를 동시에 운영할 경우 두 여수로를 통한 총 유량은 최대 7.6%까지 감소합니다.

간섭 계수는 단계 He가 설계 단계 Hd를 초과하고 두 배수로를 분리하는 거리 D가 배수로 너비 W에 비해 짧을 때 가장 중요합니다. 매개변수 DHd/WHe는 계산 및 측정된 간섭 계수와 매우 잘 관련됩니다.

안동댐 설계방류에 대한 홍수경로 예시는 간섭계수를 적용한 경우와 적용하지 않은 경우 저수지 수위의 차이가 42cm임을 보여줍니다. 결과적으로 댐 안전을 위해 추가 여수로의 너비(간섭 계수 포함)를 늘려야 합니다.

Dual spillway interference refers to the loss of hydraulic performance of spillways when they are placed close together. Spillway interference is examined using both physical experiments and numerical simulations. Stage and discharge measurements from four physical models with dual spillways configurations are compared to the Flow-3D computational results at four dam sites in South Korea. The conjunctive use of two spillways is compared with the singular operation of each spillway. When both spillways are operated at the same time, the total flow rate through the two spillways is reduced by up to 7.6%. Interference coefficients are most significant when the stage He exceeds the design stage Hd and when the distance D separating two spillways is short compared to the spillway width W. The parameter DHd/WHecorrelates very well with the calculated and measured interference coefficients. A flood routing example for the design discharge at Andong dam shows a 42 cm difference in reservoir water level with and without application of the interference coefficient. Consequently, the width of additional spillways (including the interference coefficient) should be increased for dam safety.

Fig. 1. Definition sketch for dual spillways
Fig. 1. Definition sketch for dual spillways
Fig. 2. Stage-discharge rating curves for dual spillway operations.
Fig. 2. Stage-discharge rating curves for dual spillway operations.
Fig. 3. Physical modeling of dual spillways: (a) Andong-1; (b) Andong-2; (c) Imha-1; and (d) Juam-1
Fig. 3. Physical modeling of dual spillways: (a) Andong-1; (b) Andong-2; (c) Imha-1; and (d) Juam-1
Fig. 4. Numerical modeling of dual spillways: (a) Andong-1; (b) Andong-2; (c) Imha-1; (d) Juam-1; (e) Andong-3; (f) Imha-2; (g) Imha-3; and (h) Juam-3.
Fig. 4. Numerical modeling of dual spillways: (a) Andong-1; (b) Andong-2; (c) Imha-1; (d) Juam-1; (e) Andong-3; (f) Imha-2; (g) Imha-3; and (h) Juam-3.
Fig. 4. (Continued.)
Fig. 4. (Continued.)
Fig. 5. Meshes and calculation domain for numerical modeling of Andong dam.
Fig. 5. Meshes and calculation domain for numerical modeling of Andong dam.
Fig. 6. Stage-discharge rating curve for existing and additional spillways (Andong-1): (a) existing spillway; (b) additional spillway; and (c) dual spillway simulations.
Fig. 6. Stage-discharge rating curve for existing and additional spillways (Andong-1): (a) existing spillway; (b) additional spillway; and (c) dual spillway simulations.
Fig. 7. Discharge comparison of physical experiments and numerical simulations. The upper panel is the comparative result for the existing spillway (ES) and the lower panel is for the additional spillway (AS) at four dams.
Fig. 7. Discharge comparison of physical experiments and numerical simulations. The upper panel is the comparative result for the existing spillway (ES) and the lower panel is for the additional spillway (AS) at four dams.
Fig. 8. Interference coefficients for dual spillways simulations with various scenarios.
Fig. 8. Interference coefficients for dual spillways simulations with various scenarios.
Fig. 9. Regression model for the distance-width ratio (D=W) and head ratio (Hd=He) by dual spillway simulations
Fig. 9. Regression model for the distance-width ratio (D=W) and head ratio (Hd=He) by dual spillway simulations
Fig. 10. Physical and numerical model validation: (a) numerical modeling; (b) solids of overflow weir of the spillway; and (c) physical models of reservoir and spillway
Fig. 10. Physical and numerical model validation: (a) numerical modeling; (b) solids of overflow weir of the spillway; and (c) physical models of reservoir and spillway
Fig. 11. Interference coefficients for dual spillways operations with various scenarios. The dashed lines indicate the results of the validation model with dual conditions of 1 þ 2, 1 þ 4, 1 þ 6, 3 þ 4, and 4 þ 5.
Fig. 11. Interference coefficients for dual spillways operations with various scenarios. The dashed lines indicate the results of the validation model with dual conditions of 1 þ 2, 1 þ 4, 1 þ 6, 3 þ 4, and 4 þ 5.
Fig. 12. Results of reservoir operations under the PMF at Andong dam.
Fig. 12. Results of reservoir operations under the PMF at Andong dam.

References

Cassidy, J. J. 1965. “Irrotational flow over spillways of finite height.”
J. Eng. Mech. Div. 91 (6): 155–173.
Chanel, P., and J. Doering. 2008. “Assessment of spillway modeling using
computational fluid dynamics.” Can. J. Civ. Eng. 35 (12): 1481–1485.
https://doi.org/10.1139/L08-094.
Chow, V. T. 1959. Open-channel hydraulics, 365–380. New York:
McGraw-Hill.
Ho, D., B. Cooper, K. Riddette, and S. Donohoo. 2006. “Application of
numerical modelling to spillways in Australia.” In Proc., Int. Symp.
on Dams in the Societies of the 21st Century, 22nd Int. Congress on
Large Dams (ICOLD), edited by L. Berga, et al. London: Taylor &
Francis.
Huff, F. A. 1967. “Time distribution of rainfall in heavy storms.” Water
Resour. Res. 3 (4): 1007–1019. https://doi.org/10.1029/WR003i004
p01007.
Kim, D. G., and J. H. Park. 2005. “Analysis of flow structure over ogeespillway in consideration of scale and roughness effects by using CFD
model.” KSCE J. Civ. Eng. 9 (2): 161–169. https://doi.org/10.1007
/BF02829067.
Koutsunis, N. A. 2015. “Impact of climatic changes on downstream hydraulic geometry and its influence on flood hydrograph
routing—Applied to the bluestone dam watershed.” M.S. degree,
Dept. of Civil and Environmental Engineering, Colorado State Univ.
Lee, J. H., and P. Y. Julien. 2016a. “ENSO impacts on temperature over
South Korea.” Int. J. Climatol. 36 (11): 3651. https://doi.org/10.1002
/joc.4581.
Lee, J. H., and P. Y. Julien. 2016b. “Teleconnections of the ENSO and
South Korean precipitation patterns.” J. Hydrol. 534: 237–250.
https://doi.org/10.1016/j.jhydrol.2016.01.011.
Lee, J. H., and P. Y. Julien. 2017. “Influence of the El Nino/southern ˜
oscillation on South Korean streamflow variability.” Hydrol. Processes
31 (12): 2162–2178. https://doi.org/10.1002/hyp.11168.
Li, S., S. Cain, N. Wosnik, C. Miller, H. Kocahan, and R. Wyckoff. 2011.
“Numerical modeling of probable maximum flood flowing through a
system of spillways.” J. Hydraul. Eng. 137 (1): 66–74. https://doi.org
/10.1061/(ASCE)HY.1943-7900.0000279.
MOCT (Ministry of Construction and Transportation). 2003. Hydraulic
model study of Soyanggang multipurpose dam auxiliary spillway.
[In Korean.] Governing City, South Korea: MOCT.
Olsen, N. R., and H. M. Kjellesvig. 1998. “Three-dimensional numerical
flow modeling for estimation of spillway capacity.” J. Hydraul. Res.
36 (5): 775–784. https://doi.org/10.1080/00221689809498602.
Savage, B. M., and M. C. Johnson. 2001. “Flow over ogee spillway:
Physical and numerical model case study.” J. Hydraul. Eng. 127 (8):
640–649. https://doi.org/10.1061/(ASCE)0733-9429(2001)127:8(640).
USACE (US Army Corps of Engineers). 2008. Hydrologic modeling
system HEC-HMS, user’s manual version 3.2. Davis, CA: USACE.
USBR (US Bureau of Reclamation). 1980. Hydraulic laboratory techniques: A water resources technical publication. Denver: US Dept.
of the Interior, Bureau of Reclamation.
Yakhot, V., and S. A. Orszag. 1986. “Renormalization group analysis of
turbulence. I: Basic theory.” J. Sci. Comput. 1 (1): 3–51. https://doi
.org/10.1007/BF01061452.
Yakhot, V., and L. M. Smith. 1992. “The renormalization group, the
e-expansion and derivation of turbulence models.” J. Sci. Comput.
7 (1): 35–61. https://doi.org/10.1007/BF01060210.
Zeng, J., L. Zhang, M. Ansar, E. Damisse, and J. A. Gonzalez-Castro. 2017.
“Applications of computational fluid dynamics to flow ratings at prototype spillways and weirs. I: Data generation and validation.” J. Irrig.
Drain. Eng. 143 (1): 04016072. https://doi.org/10.1061/(ASCE)IR
.1943-4774.0001112.

Figure 3 Simulation PTC pipes enhanced with copper foam and nanoparticles in FLOW-3D software.

다공성 미디어 및 나노유체에 의해 강화된 수집기로 태양광 CCHP 시스템의 최적화

Optimization of Solar CCHP Systems with Collector Enhanced by Porous Media and Nanofluid


Navid Tonekaboni,1Mahdi Feizbahr,2 Nima Tonekaboni,1Guang-Jun Jiang,3,4 and Hong-Xia Chen3,4

Abstract

태양열 집열기의 낮은 효율은 CCHP(Solar Combined Cooling, Heating, and Power) 사이클의 문제점 중 하나로 언급될 수 있습니다. 태양계를 개선하기 위해 나노유체와 다공성 매체가 태양열 집열기에 사용됩니다.

다공성 매질과 나노입자를 사용하는 장점 중 하나는 동일한 조건에서 더 많은 에너지를 흡수할 수 있다는 것입니다. 이 연구에서는 평균 일사량이 1b인 따뜻하고 건조한 지역의 600 m2 건물의 전기, 냉방 및 난방을 생성하기 위해 다공성 매질과 나노유체를 사용하여 태양열 냉난방 복합 발전(SCCHP) 시스템을 최적화했습니다.

본 논문에서는 침전물이 형성되지 않는 lb = 820 w/m2(이란) 정도까지 다공성 물질에서 나노유체의 최적량을 계산하였다. 이 연구에서 태양열 집열기는 구리 다공성 매체(95% 다공성)와 CuO 및 Al2O3 나노 유체로 향상되었습니다.

나노유체의 0.1%-0.6%가 작동 유체로 물에 추가되었습니다. 나노유체의 0.5%가 태양열 집열기 및 SCCHP 시스템에서 가장 높은 에너지 및 엑서지 효율 향상으로 이어지는 것으로 밝혀졌습니다.

본 연구에서 포물선형 집열기(PTC)의 최대 에너지 및 엑서지 효율은 각각 74.19% 및 32.6%입니다. 그림 1은 태양 CCHP의 주기를 정확하게 설명하기 위한 그래픽 초록으로 언급될 수 있습니다.

The low efficiency of solar collectors can be mentioned as one of the problems in solar combined cooling, heating, and power (CCHP) cycles. For improving solar systems, nanofluid and porous media are used in solar collectors. One of the advantages of using porous media and nanoparticles is to absorb more energy under the same conditions. In this research, a solar combined cooling, heating, and power (SCCHP) system has been optimized by porous media and nanofluid for generating electricity, cooling, and heating of a 600 m2 building in a warm and dry region with average solar radiation of Ib = 820 w/m2 in Iran. In this paper, the optimal amount of nanofluid in porous materials has been calculated to the extent that no sediment is formed. In this study, solar collectors were enhanced with copper porous media (95% porosity) and CuO and Al2O3 nanofluids. 0.1%–0.6% of the nanofluids were added to water as working fluids; it is found that 0.5% of the nanofluids lead to the highest energy and exergy efficiency enhancement in solar collectors and SCCHP systems. Maximum energy and exergy efficiency of parabolic thermal collector (PTC) riches in this study are 74.19% and 32.6%, respectively. Figure 1 can be mentioned as a graphical abstract for accurately describing the cycle of solar CCHP.

1. Introduction

Due to the increase in energy consumption, the use of clean energy is one of the important goals of human societies. In the last four decades, the use of cogeneration cycles has increased significantly due to high efficiency. Among clean energy, the use of solar energy has become more popular due to its greater availability [1]. Low efficiency of energy production, transmission, and distribution system makes a new system to generate simultaneously electricity, heating, and cooling as an essential solution to be widely used. The low efficiency of the electricity generation, transmission, and distribution system makes the CCHP system a basic solution to eliminate waste of energy. CCHP system consists of a prime mover (PM), a power generator, a heat recovery system (produce extra heating/cooling/power), and thermal energy storage (TES) [2]. Solar combined cooling, heating, and power (SCCHP) has been started three decades ago. SCCHP is a system that receives its propulsive force from solar energy; in this cycle, solar collectors play the role of propulsive for generating power in this system [3].

Increasing the rate of energy consumption in the whole world because of the low efficiency of energy production, transmission, and distribution system causes a new cogeneration system to generate electricity, heating, and cooling energy as an essential solution to be widely used. Building energy utilization fundamentally includes power required for lighting, home electrical appliances, warming and cooling of building inside, and boiling water. Domestic usage contributes to an average of 35% of the world’s total energy consumption [4].

Due to the availability of solar energy in all areas, solar collectors can be used to obtain the propulsive power required for the CCHP cycle. Solar energy is the main source of energy in renewable applications. For selecting a suitable area to use solar collectors, annual sunshine hours, the number of sunny days, minus temperature and frosty days, and the windy status of the region are essentially considered [5]. Iran, with an average of more than 300 sunny days, is one of the suitable countries to use solar energy. Due to the fact that most of the solar radiation is in the southern regions of Iran, also the concentration of cities is low in these areas, and transmission lines are far apart, one of the best options is to use CCHP cycles based on solar collectors [6]. One of the major problems of solar collectors is their low efficiency [7]. Low efficiency increases the area of collectors, which increases the initial cost of solar systems and of course increases the initial payback period. To increase the efficiency of solar collectors and improve their performance, porous materials and nanofluids are used to increase their workability.

There are two ways to increase the efficiency of solar collectors and mechanical and fluid improvement. In the first method, using porous materials or helical filaments inside the collector pipes causes turbulence of the flow and increases heat transfer. In the second method, using nanofluids or salt and other materials increases the heat transfer of water. The use of porous materials has grown up immensely over the past twenty years. Porous materials, especially copper porous foam, are widely used in solar collectors. Due to the high contact surface area, porous media are appropriate candidates for solar collectors [8]. A number of researchers investigated Solar System performance in accordance with energy and exergy analyses. Zhai et al. [9] reviewed the performance of a small solar-powered system in which the energy efficiency was 44.7% and the electrical efficiency was 16.9%.

Abbasi et al. [10] proposed an innovative multiobjective optimization to optimize the design of a cogeneration system. Results showed the CCHP system based on an internal diesel combustion engine was the applicable alternative at all regions with different climates. The diesel engine can supply the electrical requirement of 31.0% and heating demand of 3.8% for building.

Jiang et al. [11] combined the experiment and simulation together to analyze the performance of a cogeneration system. Moreover, some research focused on CCHP systems using solar energy. It integrated sustainable and renewable technologies in the CCHP, like PV, Stirling engine, and parabolic trough collector (PTC) [21215].

Wang et al. [16] optimized a cogeneration solar cooling system with a Rankine cycle and ejector to reach the maximum total system efficiency of 55.9%. Jing et al. analyzed a big-scale building with the SCCHP system and auxiliary heaters to produced electrical, cooling, and heating power. The maximum energy efficiency reported in their work is 46.6% [17]. Various optimization methods have been used to improve the cogeneration system, minimum system size, and performance, such as genetic algorithm [1819].

Hirasawa et al. [20] investigated the effect of using porous media to reduce thermal waste in solar systems. They used the high-porosity metal foam on top of the flat plate solar collector and observed that thermal waste decreased by 7% due to natural heat transfer. Many researchers study the efficiency improvement of the solar collector by changing the collector’s shapes or working fluids. However, the most effective method is the use of nanofluids in the solar collector as working fluid [21]. In the experimental study done by Jouybari et al. [22], the efficiency enhancement up to 8.1% was achieved by adding nanofluid in a flat plate collector. In this research, by adding porous materials to the solar collector, collector efficiency increased up to 92% in a low flow regime. Subramani et al. [23] analyzed the thermal performance of the parabolic solar collector with Al2O3 nanofluid. They conducted their experiments with Reynolds number range 2401 to 7202 and mass flow rate 0.0083 to 0.05 kg/s. The maximum efficiency improvement in this experiment was 56% at 0.05 kg/s mass flow rate.

Shojaeizadeh et al. [24] investigated the analysis of the second law of thermodynamic on the flat plate solar collector using Al2O3/water nanofluid. Their research showed that energy efficiency rose up to 1.9% and the exergy efficiency increased by a maximum of 0.72% compared to pure water. Tiwari et al. [25] researched on the thermal performance of solar flat plate collectors for working fluid water with different nanofluids. The result showed that using 1.5% (optimum) particle volume fraction of Al2O3 nanofluid as an absorbing medium causes the thermal efficiency to enhance up to 31.64%.

The effect of porous media and nanofluids on solar collectors has already been investigated in the literature but the SCCHP system with a collector embedded by both porous media and nanofluid for enhancing the ratio of nanoparticle in nanofluid for preventing sedimentation was not discussed. In this research, the amount of energy and exergy of the solar CCHP cycles with parabolic solar collectors in both base and improved modes with a porous material (copper foam with 95% porosity) and nanofluid with different ratios of nanoparticles was calculated. In the first step, it is planned to design a CCHP system based on the required load, and, in the next step, it will analyze the energy and exergy of the system in a basic and optimize mode. In the optimize mode, enhanced solar collectors with porous material and nanofluid in different ratios (0.1%–0.7%) were used to optimize the ratio of nanofluids to prevent sedimentation.

2. Cycle Description

CCHP is one of the methods to enhance energy efficiency and reduce energy loss and costs. The SCCHP system used a solar collector as a prime mover of the cogeneration system and assisted the boiler to generate vapor for the turbine. Hot water flows from the expander to the absorption chiller in summer or to the radiator or fan coil in winter. Finally, before the hot water wants to flow back to the storage tank, it flows inside a heat exchanger for generating domestic hot water [26].

For designing of solar cogeneration system and its analysis, it is necessary to calculate the electrical, heating (heating load is the load required for the production of warm water and space heating), and cooling load required for the case study considered in a residential building with an area of 600 m2 in the warm region of Iran (Zahedan). In Table 1, the average of the required loads is shown for the different months of a year (average of electrical, heating, and cooling load calculated with CARRIER software).Table 1 The average amount of electric charges, heating load, and cooling load used in the different months of the year in the city of Zahedan for a residential building with 600 m2.

According to Table 1, the maximum magnitude of heating, cooling, and electrical loads is used to calculate the cogeneration system. The maximum electric load is 96 kW, the maximum amount of heating load is 62 kW, and the maximum cooling load is 118 kW. Since the calculated loads are average, all loads increased up to 10% for the confidence coefficient. With the obtained values, the solar collector area and other cogeneration system components are calculated. The cogeneration cycle is capable of producing 105 kW electric power, 140 kW cooling capacity, and 100 kW heating power.

2.1. System Analysis Equations

An analysis is done by considering the following assumptions:(1)The system operates under steady-state conditions(2)The system is designed for the warm region of Iran (Zahedan) with average solar radiation Ib = 820 w/m2(3)The pressure drops in heat exchangers, separators, storage tanks, and pipes are ignored(4)The pressure drop is negligible in all processes and no expectable chemical reactions occurred in the processes(5)Potential, kinetic, and chemical exergy are not considered due to their insignificance(6)Pumps have been discontinued due to insignificance throughout the process(7)All components are assumed adiabatic

Schematic shape of the cogeneration cycle is shown in Figure 1 and all data are given in Table 2.

Figure 1 Schematic shape of the cogeneration cycle.Table 2 Temperature and humidity of different points of system.

Based on the first law of thermodynamic, energy analysis is based on the following steps.

First of all, the estimated solar radiation energy on collector has been calculated:where α is the heat transfer enhancement coefficient based on porous materials added to the collector’s pipes. The coefficient α is increased by the porosity percentage, the type of porous material (in this case, copper with a porosity percentage of 95), and the flow of fluid to the collector equation.

Collector efficiency is going to be calculated by the following equation [9]:

Total energy received by the collector is given by [9]

Also, the auxiliary boiler heat load is [2]

Energy consumed from vapor to expander is calculated by [2]

The power output form by the screw expander [9]:

The efficiency of the expander is 80% in this case [11].

In this step, cooling and heating loads were calculated and then, the required heating load to reach sanitary hot water will be calculated as follows:

First step: calculating the cooling load with the following equation [9]:

Second step: calculating heating loads [9]:

Then, calculating the required loud for sanitary hot water will be [9]

According to the above-mentioned equations, efficiency is [9]

In the third step, calculated exergy analysis as follows.

First, the received exergy collector from the sun is calculated [9]:

In the previous equation, f is the constant of air dilution.

The received exergy from the collector is [9]

In the case of using natural gas in an auxiliary heater, the gas exergy is calculated from the following equation [12]:

Delivering exergy from vapor to expander is calculated with the following equation [9]:

In the fourth step, the exergy in cooling and heating is calculated by the following equation:

Cooling exergy in summer is calculated [9]:

Heating exergy in winter is calculated [9]:

In the last step based on thermodynamic second law, exergy efficiency has been calculated from the following equation and the above-mentioned calculated loads [9]:

3. Porous Media

The porous medium that filled the test section is copper foam with a porosity of 95%. The foams are determined in Figure 2 and also detailed thermophysical parameters and dimensions are shown in Table 3.

Figure 2 Copper foam with a porosity of 95%.Table 3 Thermophysical parameters and dimensions of copper foam.

In solar collectors, copper porous materials are suitable for use at low temperatures and have an easier and faster manufacturing process than ceramic porous materials. Due to the high coefficient conductivity of copper, the use of copper metallic foam to increase heat transfer is certainly more efficient in solar collectors.

Porous media and nanofluid in solar collector’s pipes were simulated in FLOW-3D software using the finite-difference method [27]. Nanoparticles Al2O3 and CUO are mostly used in solar collector enhancement. In this research, different concentrations of nanofluid are added to the parabolic solar collectors with porous materials (copper foam with porosity of 95%) to achieve maximum heat transfer in the porous materials before sedimentation. After analyzing PTC pipes with the nanofluid flow in FLOW-3D software, for energy and exergy efficiency analysis, Carrier software results were used as EES software input. Simulation PTC with porous media inside collector pipe and nanofluids sedimentation is shown in Figure 3.

Figure 3 Simulation PTC pipes enhanced with copper foam and nanoparticles in FLOW-3D software.

3.1. Nano Fluid

In this research, copper and silver nanofluids (Al2O3, CuO) have been added with percentages of 0.1%–0.7% as the working fluids. The nanoparticle properties are given in Table 4. Also, system constant parameters are presented in Table 4, which are available as default input in the EES software.Table 4 Properties of the nanoparticles [9].

System constant parameters for input in the software are shown in Table 5.Table 5 System constant parameters.

The thermal properties of the nanofluid can be obtained from equations (18)–(21). The basic fluid properties are indicated by the index (bf) and the properties of the nanoparticle silver with the index (np).

The density of the mixture is shown in the following equation [28]:where ρ is density and ϕ is the nanoparticles volume fraction.

The specific heat capacity is calculated from the following equation [29]:

The thermal conductivity of the nanofluid is calculated from the following equation [29]:

The parameter β is the ratio of the nanolayer thickness to the original particle radius and, usually, this parameter is taken equal to 0.1 for the calculated thermal conductivity of the nanofluids.

The mixture viscosity is calculated as follows [30]:

In all equations, instead of water properties, working fluids with nanofluid are used. All of the above equations and parameters are entered in the EES software for calculating the energy and exergy of solar collectors and the SCCHP cycle. All calculation repeats for both nanofluids with different concentrations of nanofluid in the solar collector’s pipe.

4. Results and Discussion

In the present study, relations were written according to Wang et al. [16] and the system analysis was performed to ensure the correctness of the code. The energy and exergy charts are plotted based on the main values of the paper and are shown in Figures 4 and 5. The error rate in this simulation is 1.07%.

Figure 4 Verification charts of energy analysis results.

Figure 5 Verification charts of exergy analysis results.

We may also investigate the application of machine learning paradigms [3141] and various hybrid, advanced optimization approaches that are enhanced in terms of exploration and intensification [4255], and intelligent model studies [5661] as well, for example, methods such as particle swarm optimizer (PSO) [6062], differential search (DS) [63], ant colony optimizer (ACO) [616465], Harris hawks optimizer (HHO) [66], grey wolf optimizer (GWO) [5367], differential evolution (DE) [6869], and other fusion and boosted systems [4146485054557071].

At the first step, the collector is modified with porous copper foam material. 14 cases have been considered for the analysis of the SCCHP system (Table 6). It should be noted that the adding of porous media causes an additional pressure drop inside the collector [922263072]. All fourteen cases use copper foam with a porosity of 95 percent. To simulate the effect of porous materials and nanofluids, the first solar PTC pipes have been simulated in the FLOW-3D software and then porous media (copper foam with porosity of 95%) and fluid flow with nanoparticles (AL2O3 and CUO) are generated in the software. After analyzing PTC pipes in FLOW-3D software, for analyzing energy and exergy efficiency, software outputs were used as EES software input for optimization ratio of sedimentation and calculating energy and exergy analyses.Table 6 Collectors with different percentages of nanofluids and porous media.

In this research, an enhanced solar collector with both porous media and Nanofluid is investigated. In the present study, 0.1–0.5% CuO and Al2O3 concentration were added to the collector fully filled by porous media to achieve maximum energy and exergy efficiencies of solar CCHP systems. All steps of the investigation are shown in Table 6.

Energy and exergy analyses of parabolic solar collectors and SCCHP systems are shown in Figures 6 and 7.

Figure 6 Energy and exergy efficiencies of the PTC with porous media and nanofluid.

Figure 7 Energy and exergy efficiency of the SCCHP.

Results show that the highest energy and exergy efficiencies are 74.19% and 32.6%, respectively, that is achieved in Step 12 (parabolic collectors with filled porous media and 0.5% Al2O3). In the second step, the maximum energy efficiency of SCCHP systems with fourteen steps of simulation are shown in Figure 7.

In the second step, where 0.1, −0.6% of the nanofluids were added, it is found that 0.5% leads to the highest energy and exergy efficiency enhancement in solar collectors and SCCHP systems. Using concentrations more than 0.5% leads to sediment in the solar collector’s pipe and a decrease of porosity in the pipe [73]. According to Figure 7, maximum energy and exergy efficiencies of SCCHP are achieved in Step 12. In this step energy efficiency is 54.49% and exergy efficiency is 18.29%. In steps 13 and 14, with increasing concentration of CUO and Al2O3 nanofluid solution in porous materials, decreasing of energy and exergy efficiency of PTC and SCCHP system at the same time happened. This decrease in efficiency is due to the formation of sediment in the porous material. Calculations and simulations have shown that porous materials more than 0.5% nanofluids inside the collector pipe cause sediment and disturb the porosity of porous materials and pressure drop and reduce the coefficient of performance of the cogeneration system. Most experience showed that CUO and AL2O3 nanofluids with less than 0.6% percent solution are used in the investigation on the solar collectors at low temperatures and discharges [74]. One of the important points of this research is that the best ratio of nanofluids in the solar collector with a low temperature is 0.5% (AL2O3 and CUO); with this replacement, the cost of solar collectors and SCCHP cycle is reduced.

5. Conclusion and Future Directions

In the present study, ways for increasing the efficiency of solar collectors in order to enhance the efficiency of the SCCHP cycle are examined. The research is aimed at adding both porous materials and nanofluids for estimating the best ratio of nanofluid for enhanced solar collector and protecting sedimentation in porous media. By adding porous materials (copper foam with porosity of 95%) and 0.5% nanofluids together, high efficiency in solar parabolic collectors can be achieved. The novelty in this research is the addition of both nanofluids and porous materials and calculating the best ratio for preventing sedimentation and pressure drop in solar collector’s pipe. In this study, it was observed that, by adding 0.5% of AL2O3 nanofluid in working fluids, the energy efficiency of PTC rises to 74.19% and exergy efficiency is grown up to 32.6%. In SCCHP cycle, energy efficiency is 54.49% and exergy efficiency is 18.29%.

In this research, parabolic solar collectors fully filled by porous media (copper foam with a porosity of 95) are investigated. In the next step, parabolic solar collectors in the SCCHP cycle were simultaneously filled by porous media and different percentages of Al2O3 and CuO nanofluid. At this step, values of 0.1% to 0.6% of each nanofluid were added to the working fluid, and the efficiency of the energy and exergy of the collectors and the SCCHP cycle were determined. In this case, nanofluid and the porous media were used together in the solar collector and maximum efficiency achieved. 0.5% of both nanofluids were used to achieve the biggest efficiency enhancement.

In the present study, as expected, the highest efficiency is for the parabolic solar collector fully filled by porous material (copper foam with a porosity of 95%) and 0.5% Al2O3. Results of the present study are as follows:(1)The average enhancement of collectors’ efficiency using porous media and nanofluids is 28%.(2)Solutions with 0.1 to 0.5% of nanofluids (CuO and Al2O3) are used to prevent collectors from sediment occurrence in porous media.(3)Collector of solar cogeneration cycles that is enhanced by both porous media and nanofluid has higher efficiency, and the stability of output temperature is more as well.(4)By using 0.6% of the nanofluids in the enhanced parabolic solar collectors with copper porous materials, sedimentation occurs and makes a high-pressure drop in the solar collector’s pipe which causes decrease in energy efficiency.(5)Average enhancement of SCCHP cycle efficiency is enhanced by both porous media and nanofluid 13%.

Nomenclature

:Solar radiation
a:Heat transfer augmentation coefficient
A:Solar collector area
Bf:Basic fluid
:Specific heat capacity of the nanofluid
F:Constant of air dilution
:Thermal conductivity of the nanofluid
:Thermal conductivity of the basic fluid
:Viscosity of the nanofluid
:Viscosity of the basic fluid
:Collector efficiency
:Collector energy receives
:Auxiliary boiler heat
:Expander energy
:Gas energy
:Screw expander work
:Cooling load, in kilowatts
:Heating load, in kilowatts
:Solar radiation energy on collector, in Joule
:Sanitary hot water load
Np:Nanoparticle
:Energy efficiency
:Heat exchanger efficiency
:Sun exergy
:Collector exergy
:Natural gas exergy
:Expander exergy
:Cooling exergy
:Heating exergy
:Exergy efficiency
:Steam mass flow rate
:Hot water mass flow rate
:Specific heat capacity of water
:Power output form by the screw expander
Tam:Average ambient temperature
:Density of the mixture.

Greek symbols

ρ:Density
ϕ:Nanoparticles volume fraction
β:Ratio of the nanolayer thickness.

Abbreviations

CCHP:Combined cooling, heating, and power
EES:Engineering equation solver.

Data Availability

For this study, data were generated by CARRIER software for the average electrical, heating, and cooling load of a residential building with 600 m2 in the city of Zahedan, Iran.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China under Contract no. 71761030 and Natural Science Foundation of Inner Mongolia under Contract no. 2019LH07003.

References

  1. A. Fudholi and K. Sopian, “Review on solar collector for agricultural produce,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 9, no. 1, p. 414, 2018.View at: Publisher Site | Google Scholar
  2. G. Yang and X. Zhai, “Optimization and performance analysis of solar hybrid CCHP systems under different operation strategies,” Applied Thermal Engineering, vol. 133, pp. 327–340, 2018.View at: Publisher Site | Google Scholar
  3. J. Wang, Z. Han, and Z. Guan, “Hybrid solar-assisted combined cooling, heating, and power systems: a review,” Renewable and Sustainable Energy Reviews, vol. 133, p. 110256, 2020.View at: Publisher Site | Google Scholar
  4. Y. Tian and C. Y. Zhao, “A review of solar collectors and thermal energy storage in solar thermal applications,” Applied Energy, vol. 104, pp. 538–553, 2013.View at: Publisher Site | Google Scholar
  5. J. M. Hassan, Q. J. Abdul-Ghafour, and M. F. Mohammed, “CFD simulation of enhancement techniques in flat plate solar water collectors,” Al-Nahrain Journal for Engineering Sciences, vol. 20, no. 3, pp. 751–761, 2017.View at: Google Scholar
  6. M. Jahangiri, O. Nematollahi, A. Haghani, H. A. Raiesi, and A. Alidadi Shamsabadi, “An optimization of energy cost of clean hybrid solar-wind power plants in Iran,” International Journal of Green Energy, vol. 16, no. 15, pp. 1422–1435, 2019.View at: Publisher Site | Google Scholar
  7. I. H. Yılmaz and A. Mwesigye, “Modeling, simulation and performance analysis of parabolic trough solar collectors: a comprehensive review,” Applied Energy, vol. 225, pp. 135–174, 2018.View at: Google Scholar
  8. F. Wang, J. Tan, and Z. Wang, “Heat transfer analysis of porous media receiver with different transport and thermophysical models using mixture as feeding gas,” Energy Conversion and Management, vol. 83, pp. 159–166, 2014.View at: Publisher Site | Google Scholar
  9. H. Zhai, Y. J. Dai, J. Y. Wu, and R. Z. Wang, “Energy and exergy analyses on a novel hybrid solar heating, cooling and power generation system for remote areas,” Applied Energy, vol. 86, no. 9, pp. 1395–1404, 2009.View at: Publisher Site | Google Scholar
  10. M. H. Abbasi, H. Sayyaadi, and M. Tahmasbzadebaie, “A methodology to obtain the foremost type and optimal size of the prime mover of a CCHP system for a large-scale residential application,” Applied Thermal Engineering, vol. 135, pp. 389–405, 2018.View at: Google Scholar
  11. R. Jiang, F. G. F. Qin, X. Yang, S. Huang, and B. Chen, “Performance analysis of a liquid absorption dehumidifier driven by jacket-cooling water of a diesel engine in a CCHP system,” Energy and Buildings, vol. 163, pp. 70–78, 2018.View at: Publisher Site | Google Scholar
  12. F. A. Boyaghchi and M. Chavoshi, “Monthly assessments of exergetic, economic and environmental criteria and optimization of a solar micro-CCHP based on DORC,” Solar Energy, vol. 166, pp. 351–370, 2018.View at: Publisher Site | Google Scholar
  13. F. A. Boyaghchi and M. Chavoshi, “Multi-criteria optimization of a micro solar-geothermal CCHP system applying water/CuO nanofluid based on exergy, exergoeconomic and exergoenvironmental concepts,” Applied Thermal Engineering, vol. 112, pp. 660–675, 2017.View at: Publisher Site | Google Scholar
  14. B. Su, W. Han, Y. Chen, Z. Wang, W. Qu, and H. Jin, “Performance optimization of a solar assisted CCHP based on biogas reforming,” Energy Conversion and Management, vol. 171, pp. 604–617, 2018.View at: Publisher Site | Google Scholar
  15. F. A. Al-Sulaiman, F. Hamdullahpur, and I. Dincer, “Performance assessment of a novel system using parabolic trough solar collectors for combined cooling, heating, and power production,” Renewable Energy, vol. 48, pp. 161–172, 2012.View at: Publisher Site | Google Scholar
  16. J. Wang, Y. Dai, L. Gao, and S. Ma, “A new combined cooling, heating and power system driven by solar energy,” Renewable Energy, vol. 34, no. 12, pp. 2780–2788, 2009.View at: Publisher Site | Google Scholar
  17. Y.-Y. Jing, H. Bai, J.-J. Wang, and L. Liu, “Life cycle assessment of a solar combined cooling heating and power system in different operation strategies,” Applied Energy, vol. 92, pp. 843–853, 2012.View at: Publisher Site | Google Scholar
  18. J.-J. Wang, Y.-Y. Jing, and C.-F. Zhang, “Optimization of capacity and operation for CCHP system by genetic algorithm,” Applied Energy, vol. 87, no. 4, pp. 1325–1335, 2010.View at: Publisher Site | Google Scholar
  19. L. Ali, “LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine,” Neural Computing and Applications, vol. 87, pp. 1–10, 2020.View at: Google Scholar
  20. S. Hirasawa, R. Tsubota, T. Kawanami, and K. Shirai, “Reduction of heat loss from solar thermal collector by diminishing natural convection with high-porosity porous medium,” Solar Energy, vol. 97, pp. 305–313, 2013.View at: Publisher Site | Google Scholar
  21. E. Bellos, C. Tzivanidis, and Z. Said, “A systematic parametric thermal analysis of nanofluid-based parabolic trough solar collectors,” Sustainable Energy Technologies and Assessments, vol. 39, p. 100714, 2020.View at: Publisher Site | Google Scholar
  22. H. J. Jouybari, S. Saedodin, A. Zamzamian, M. E. Nimvari, and S. Wongwises, “Effects of porous material and nanoparticles on the thermal performance of a flat plate solar collector: an experimental study,” Renewable Energy, vol. 114, pp. 1407–1418, 2017.View at: Publisher Site | Google Scholar
  23. J. Subramani, P. K. Nagarajan, S. Wongwises, S. A. El-Agouz, and R. Sathyamurthy, “Experimental study on the thermal performance and heat transfer characteristics of solar parabolic trough collector using Al2O3 nanofluids,” Environmental Progress & Sustainable Energy, vol. 37, no. 3, pp. 1149–1159, 2018.View at: Publisher Site | Google Scholar
  24. E. Shojaeizadeh, F. Veysi, and A. Kamandi, “Exergy efficiency investigation and optimization of an Al2O3-water nanofluid based Flat-plate solar collector,” Energy and Buildings, vol. 101, pp. 12–23, 2015.View at: Publisher Site | Google Scholar
  25. A. K. Tiwari, P. Ghosh, and J. Sarkar, “Solar water heating using nanofluids–a comprehensive overview and environmental impact analysis,” International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 3, pp. 221–224, 2013.View at: Google Scholar
  26. D. R. Rajendran, E. Ganapathy Sundaram, P. Jawahar, V. Sivakumar, O. Mahian, and E. Bellos, “Review on influencing parameters in the performance of concentrated solar power collector based on materials, heat transfer fluids and design,” Journal of Thermal Analysis and Calorimetry, vol. 140, no. 1, pp. 33–51, 2020.View at: Publisher Site | Google Scholar
  27. M. Feizbahr, C. Kok Keong, F. Rostami, and M. Shahrokhi, “Wave energy dissipation using perforated and non perforated piles,” International Journal of Engineering, vol. 31, no. 2, pp. 212–219, 2018.View at: Google Scholar
  28. K. Khanafer and K. Vafai, “A critical synthesis of thermophysical characteristics of nanofluids,” International Journal of Heat and Mass Transfer, vol. 54, no. 19-20, pp. 4410–4428, 2011.View at: Publisher Site | Google Scholar
  29. K. Farhana, K. Kadirgama, M. M. Rahman et al., “Improvement in the performance of solar collectors with nanofluids – a state-of-the-art review,” Nano-Structures & Nano-Objects, vol. 18, p. 100276, 2019.View at: Publisher Site | Google Scholar
  30. M. Turkyilmazoglu, “Condensation of laminar film over curved vertical walls using single and two-phase nanofluid models,” European Journal of Mechanics-B/Fluids, vol. 65, pp. 184–191, 2017.View at: Publisher Site | Google Scholar
  31. X. Zhang, J. Wang, T. Wang, R. Jiang, J. Xu, and L. Zhao, “Robust feature learning for adversarial defense via hierarchical feature alignment,” Information Sciences, vol. 2020, 2020.View at: Google Scholar
  32. X. Zhang, T. Wang, W. Luo, and P. Huang, “Multi-level fusion and attention-guided CNN for image dehazing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 1, 2020.View at: Publisher Site | Google Scholar
  33. X. Zhang, M. Fan, D. Wang, P. Zhou, and D. Tao, “Top-k feature selection framework using robust 0-1 integer programming,” IEEE Transactions on Neural Networks and Learning Systems, vol. 1, pp. 1–15, 2020.View at: Publisher Site | Google Scholar
  34. X. Zhang, D. Wang, Z. Zhou, and Y. Ma, “Robust low-rank tensor recovery with rectification and alignment,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 238–255, 2019.View at: Google Scholar
  35. X. Zhang, R. Jiang, T. Wang, and J. Wang, “Recursive neural network for video deblurring,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 1, 2020.View at: Publisher Site | Google Scholar
  36. X. Zhang, T. Wang, J. Wang, G. Tang, and L. Zhao, “Pyramid channel-based feature attention network for image dehazing,” Computer Vision and Image Understanding, vol. 1, 2020.View at: Google Scholar
  37. M. Mirmozaffari, “Machine learning algorithms based on an optimization model,” 2020.View at: Google Scholar
  38. M. Mirmozaffari, M. Yazdani, A. Boskabadi, H. Ahady Dolatsara, K. Kabirifar, and N. Amiri Golilarz, “A novel machine learning approach combined with optimization models for eco-efficiency evaluation,” Applied Sciences, vol. 10, no. 15, p. 5210, 2020.View at: Publisher Site | Google Scholar
  39. M. Vosoogha and A. Addeh, “An intelligent power prediction method for wind energy generation based on optimized fuzzy system,” Computational Research Progress in Applied Science & Engineering (CRPASE), vol. 5, pp. 34–43, 2019.View at: Google Scholar
  40. A. Javadi, N. Mikaeilvand, and H. Hosseinzdeh, “Presenting a new method to solve partial differential equations using a group search optimizer method (GSO),” Computational Research Progress in Applied Science and Engineering, vol. 4, no. 1, pp. 22–26, 2018.View at: Google Scholar
  41. F. J. Golrokh, Gohar Azeem, and A. Hasan, “Eco-efficiency evaluation in cement industries: DEA malmquist productivity index using optimization models,” ENG Transactions, vol. 1, pp. 1–8, 2020.View at: Google Scholar
  42. H. Yu, “Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis,” Engineering with Computers, vol. 1, pp. 1–29, 2020.View at: Google Scholar
  43. C. Yu, “SGOA: annealing-behaved grasshopper optimizer for global tasks,” Engineering with Computers, vol. 1, pp. 1–28, 2021.View at: Google Scholar
  44. W. Shan, Z. Qiao, A. A. Heidari, H. Chen, H. Turabieh, and Y. Teng, “Double adaptive weights for stabilization of moth flame optimizer: balance analysis, engineering cases, and medical diagnosis,” Knowledge-Based Systems, vol. 1, p. 106728, 2020.View at: Google Scholar
  45. J. Tu, H. Chen, J. Liu et al., “Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance,” Knowledge-Based Systems, vol. 212, p. 106642, 2021.View at: Publisher Site | Google Scholar
  46. Y. Zhang, “Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis,” Neurocomputing, vol. 1, 2020.View at: Google Scholar
  47. Y. Zhang, R. Liu, X. Wang, H. Chen, and C. Li, “Boosted binary Harris hawks optimizer and feature selection,” Engineering with Computers, vol. 1, pp. 1–30, 2020.View at: Google Scholar
  48. H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, and S.-J. Wang, “An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease,” Neurocomputing, vol. 184, pp. 131–144, 2016.View at: Publisher Site | Google Scholar
  49. L. Hu, G. Hong, J. Ma, X. Wang, and H. Chen, “An efficient machine learning approach for diagnosis of paraquat-poisoned patients,” Computers in Biology and Medicine, vol. 59, pp. 116–124, 2015.View at: Publisher Site | Google Scholar
  50. L. Shen, H. Chen, Z. Yu et al., “Evolving support vector machines using fruit fly optimization for medical data classification,” Knowledge-Based Systems, vol. 96, pp. 61–75, 2016.View at: Publisher Site | Google Scholar
  51. J. Xia, H. Chen, Q. Li et al., “Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach,” Computer Methods and Programs in Biomedicine, vol. 147, pp. 37–49, 2017.View at: Publisher Site | Google Scholar
  52. C. Li, L. Hou, B. Y. Sharma et al., “Developing a new intelligent system for the diagnosis of tuberculous pleural effusion,” Computer Methods and Programs in Biomedicine, vol. 153, pp. 211–225, 2018.View at: Publisher Site | Google Scholar
  53. X. Zhao, X. Zhang, Z. Cai et al., “Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients,” Computational Biology and Chemistry, vol. 78, pp. 481–490, 2019.View at: Publisher Site | Google Scholar
  54. M. Wang and H. Chen, “Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis,” Applied Soft Computing Journal, vol. 88, 2020.View at: Publisher Site | Google Scholar
  55. X. Xu and H.-L. Chen, “Adaptive computational chemotaxis based on field in bacterial foraging optimization,” Soft Computing, vol. 18, no. 4, pp. 797–807, 2014.View at: Publisher Site | Google Scholar
  56. R. U. Khan, X. Zhang, R. Kumar, A. Sharif, N. A. Golilarz, and M. Alazab, “An adaptive multi-layer botnet detection technique using machine learning classifiers,” Applied Sciences, vol. 9, no. 11, p. 2375, 2019.View at: Publisher Site | Google Scholar
  57. A. Addeh, A. Khormali, and N. A. Golilarz, “Control chart pattern recognition using RBF neural network with new training algorithm and practical features,” ISA Transactions, vol. 79, pp. 202–216, 2018.View at: Publisher Site | Google Scholar
  58. N. Amiri Golilarz, H. Gao, R. Kumar, L. Ali, Y. Fu, and C. Li, “Adaptive wavelet based MRI brain image de-noising,” Frontiers in Neuroscience, vol. 14, p. 728, 2020.View at: Publisher Site | Google Scholar
  59. N. A. Golilarz, H. Gao, and H. Demirel, “Satellite image de-noising with Harris hawks meta heuristic optimization algorithm and improved adaptive generalized Gaussian distribution threshold function,” IEEE Access, vol. 7, pp. 57459–57468, 2019.View at: Publisher Site | Google Scholar
  60. M. Eisazadeh and J. Rezapour, “Multi-objective optimization of the composite sheets using PSO algorithm,” 2017.View at: Google Scholar
  61. I. Bargegol, M. Nikookar, R. V. Nezafat, E. J. Lashkami, and A. M. Roshandeh, “Timing optimization of signalized intersections using shockwave theory by genetic algorithm,” Computational Research Progress in Applied Science & Engineering, vol. 1, pp. 160–167, 2015.View at: Google Scholar
  62. B. Bai, Z. Guo, C. Zhou, W. Zhang, and J. Zhang, “Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering,” Information Sciences, vol. 546, pp. 42–59, 2021.View at: Publisher Site | Google Scholar
  63. J. Liu, C. Wu, G. Wu, and X. Wang, “A novel differential search algorithm and applications for structure design,” Applied Mathematics and Computation, vol. 268, pp. 246–269, 2015.View at: Publisher Site | Google Scholar
  64. X. Zhao, D. Li, B. Yang, C. Ma, Y. Zhu, and H. Chen, “Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton,” Applied Soft Computing, vol. 24, pp. 585–596, 2014.View at: Publisher Site | Google Scholar
  65. D. Zhao, “Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy,” Knowledge-Based Systems, vol. 24, p. 106510, 2020.View at: Google Scholar
  66. H. Chen, A. A. Heidari, H. Chen, M. Wang, Z. Pan, and A. H. Gandomi, “Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies,” Future Generation Computer Systems, vol. 111, pp. 175–198, 2020.View at: Publisher Site | Google Scholar
  67. J. Hu, H. Chen, A. A. Heidari et al., “Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection,” Knowledge-Based Systems, vol. 213, p. 106684, 2021.View at: Publisher Site | Google Scholar
  68. G. Sun, B. Yang, Z. Yang, and G. Xu, “An adaptive differential evolution with combined strategy for global numerical optimization,” Soft Computing, vol. 24, pp. 1–20, 2019.View at: Google Scholar
  69. G. Sun, C. Li, and L. Deng, “An adaptive regeneration framework based on search space adjustment for differential evolution,” Neural Computing and Applications, vol. 24, pp. 1–17, 2021.View at: Google Scholar
  70. A. Addeh and M. Iri, “Brain tumor type classification using deep features of MRI images and optimized RBFNN,” ENG Transactions, vol. 2, pp. 1–7, 2021.View at: Google Scholar
  71. F. J. Golrokh and A. Hasan, “A comparison of machine learning clustering algorithms based on the DEA optimization approach for pharmaceutical companies in developing countries,” Soft Computing, vol. 1, pp. 1–8, 2020.View at: Google Scholar
  72. H. Tyagi, P. Phelan, and R. Prasher, “Predicted efficiency of a low-temperature nanofluid-based direct absorption solar collector,” Journal of Solar Energy Engineering, vol. 131, no. 4, 2009.View at: Publisher Site | Google Scholar
  73. S. Rashidi, M. Bovand, and J. A. Esfahani, “Heat transfer enhancement and pressure drop penalty in porous solar heat exchangers: a sensitivity analysis,” Energy Conversion and Management, vol. 103, pp. 726–738, 2015.View at: Publisher Site | Google Scholar
  74. N. Akram, R. Sadri, S. N. Kazi et al., “A comprehensive review on nanofluid operated solar flat plate collectors,” Journal of Thermal Analysis and Calorimetry, vol. 139, no. 2, pp. 1309–1343, 2020.View at: Publisher Site | Google Scholar
Energy and exergy analysis of an enhanced solar CCHP system with a collector embedded by porous media and nano fluid

Energy and exergy analysis of an enhanced solar CCHP system with a collector embedded by porous media and nano fluid

Year 2021, Volume 7, Issue 6, 1489 – 1505, 02.09.2021

N. TONEKABONI  H. SALARIAN  M. Eshagh NIMVARI  J. KHALEGHINIA https://doi.org/10.18186/thermal.990897

Abstract

The low efficiency of Collectors that absorb energy can be mentioned as one of the drawbacks in solar cogeneration cycles. In the present study, solar systems have been improved by adding porous media and Nanofluid to collectors. One advantage of using porous media and nanomaterials is to absorb more energy while the surface area is reduced. In this study, first, solar collectors are enhanced using 90% porosity copper in solar combined cooling, heating and power systems (SCCHP). Second, different percentages of CuO and Al2O3 nano-fluids are added to a flat plate and parabolic collectors to enhance thermal properties. Simulations are performed in different modes (simple parabolic collectors, simple flat plate collectors, improved flat plate collectors, parabolic collectors with porous media, and flat plate and parabolic collectors with different density of CuO and Al2O3 nanofluids). A case study is investigated for warm and dry regions with mean solar radiation Ib = 820 w / m2 in Iran. The maximum energy and exergy efficiencies are 60.12% and 18.84%, respectively, that is related to enhanced parabolic solar collectors with porous media and nanofluids. Adding porous media and nano-fluids increases an average 14.4% collector energy efficiency and 8.08% collector exergy efficiency.

Keywords

Exergy analysisSolar cogeneration systemPorous mediaNanofluid

References

  • [1] Center TU. Annual report on China building energy efficiency. China Construction Industry Press (In Chinese). 2016.
  • [2] Tonekaboni N, Salarian H, Fatahian E, Fatahian H. Energy and exergy economic analysis of cogeneration cycle of homemade CCHP with PVT collector. Canadian Journal of Basic and Applied Sciences 2015;3:224-233.
  • [3] Hassan JM, Abdul-Ghafour QJ, Mohammed MF. CFD simulation of enhancement techniques in flat plate solar water collectors. Al-Nahrain Journal for Engineering Sciences 2017;20:751-761.
  • [4] Sopian K, Daud WR, Othman MY, Yatim B. Thermal performance of the double-pass solar collector with and without porous media. Renewable Energy 1999;18:557-564. https://doi.org/10.1016/S0960-1481(99)00007-5
  • [5] Feizbahr M, Kok Keong C, Rostami F, Shahrokhi M. Wave energy dissipation using perforated and non perforated piles. International Journal of Engineering 2018;31:212-219. https://doi.org/10.5829/ije.2018.31.02b.04
  • [6] Tian Y, Zhao CY. A review of solar collectors and thermal energy storage in solar thermal applications. Applied Energy 2013;104:538-553. https://doi.org/10.1016/j.apenergy.2012.11.051
  • [7] Wang F, Tan J, Wang Z. Heat transfer analysis of porous media receiver with different transport and thermophysical models using mixture as feeding gas. Energy Conversion and Management 2014;83:159-166. https://doi.org/10.1016/j.enconman.2014.03.068
  • [8] Korti AI. Numerical 3-D heat flow simulations on double-pass solar collector with and without porous media. Journal of Thermal Engineering 2015;1:10-23. https://doi.org/10.18186/jte.86295
  • [9] Sharma N, Diaz G. Performance model of a novel evacuated-tube solar collector based on minichannels. Solar Energy 2011;85:881-890. https://doi.org/10.1016/j.solener.2011.02.001
  • [10] Tyagi VV, Kaushik SC, Tyagi SK. Advancement in solar photovoltaic/thermal (PV/T) hybrid collector technology. Renewable and Sustainable Energy Reviews 2012;16:1383-1398. https://doi.org/10.1016/j.rser.2011.12.013
  • [11] Zhai H, Dai YJ, Wu JY, Wang RZ. Energy and exergy analyses on a novel hybrid solar heating, cooling and power generation system for remote areas. Applied Energy 2009;86:1395-1404. https://doi.org/10.1016/j.apenergy.2008.11.020
  • [12] Wang J, Dai Y, Gao L, Ma S. A new combined cooling, heating and power system driven by solar energy. Renewable Energy 2009;34:2780-2788. https://doi.org/10.1016/j.renene.2009.06.010
  • [13] Jing YY, Bai H, Wang JJ, Liu L. Life cycle assessment of a solar combined cooling heating and power system in different operation strategies. Applied Energy 2012;92:843-853. https://doi.org/10.1016/j.apenergy.2011.08.046
  • [14] Temir G, Bilge D. Thermoeconomic analysis of a trigeneration system. applied thermal engineering. Applied Thermal Engineering 2004;24:2689-2699. https://doi.org/10.1016/j.applthermaleng.2004.03.014
  • [15] Wang JJ, Jing YY, Zhang CF. Optimization of capacity and operation for CCHP system by genetic algorithm. Applied Energy 2010;87:1325-1335. https://doi.org/10.1016/j.apenergy.2009.08.005
  • [16] Kleinstreuer C, Chiang H. Analysis of a porous-medium solar collector. Heat Transfer Engineering 1990;11:45-55. https://doi.org/10.1080/01457639008939728
  • [17] Mbaye M, Bilgen E. Natural convection and conduction in porous wall, solar collector systems without vents. Jornal of Solar Energy Engineering 1992;114:40-46. https://doi.org/10.1115/1.2929980
  • [18] Hirasawa S, Tsubota R, Kawanami T, Shirai K. Reduction of heat loss from solar thermal collector by diminishing natural convection with high-porosity porous medium. Solar Energy 2013;97:305-313. https://doi.org/10.1016/j.solener.2013.08.035
  • [19] Jouybari HJ, Saedodin S, Zamzamian A, Nimvari ME, Wongwises S. Effects of porous material and nanoparticles on the thermal performance of a flat plate solar collector: an experimental study. Renewable Energy 2017;114:1407-1418. https://doi.org/10.1016/j.renene.2017.07.008
  • [20] Subramani J, Nagarajan PK, Wongwises S, El‐Agouz SA, Sathyamurthy R. Experimental study on the thermal performance and heat transfer characteristics of solar parabolic trough collector using Al2O3 nanofluids. Environmental Progress & Sustainable Energy 2018;37:1149-1159. https://doi.org/10.1002/ep.12767
  • [21] Yousefi T, Veysi F, Shojaeizadeh E, Zinadini S. An experimental investigation on the effect of Al2O3–H2O nanofluid on the efficiency of flat-plate solar collectors. Renewable Energy 2012;39:293-298. https://doi.org/10.1016/j.renene.2011.08.056
  • [22] Tyagi H, Phelan P, Prasher R. Predicted efficiency of a low-temperature nanofluid-based direct absorption solar collector. Journal of Solar Energy Engineering 2009;131:041004. https://doi.org/10.1115/1.3197562
  • [23] Shojaeizadeh E, Veysi F, Kamandi A. Exergy efficiency investigation and optimization of an Al2O3–water nanofluid based Flat-plate solar collector. Energy and Buildings 2015;101:12-23. https://doi.org/10.1016/j.enbuild.2015.04.048
  • [24] Tiwari AK, Ghosh P, Sarkar J. Solar water heating using nanofluids–a comprehensive overview and environmental impact analysis. International Journal of Emerging Technology and Advanced Engineering 2013;3:221-224. [25] Akram N, Sadri R, Kazi SN, Zubir MN, Ridha M, Ahmed W, et al. A comprehensive review on nanofluid operated solar flat plate collectors. Journal of Thermal Analysis and Calorimetry 2020;139:1309-1343. https://doi.org/10.1007/s10973-019-08514-z
  • [26] Lemington N. Study of solar driven adsorption cooling potential in Indonesia. Journal of Thermal Engineering 2017;3:1044-1051. https://doi.org/10.18186/thermal.290257
  • [27] Tong Y, Lee H, Kang W, Cho H. Energy and exergy comparison of a flat-plate solar collector using water, Al2O3 nanofluid, and CuO nanofluid. Applied Thermal Engineering 2019;159:113959. https://doi.org/10.1016/j.applthermaleng.2019.113959
  • [28] Khanafer K, Vafai K. A critical synthesis of thermophysical characteristics of nanofluids. International Journal of Heat And Mass Transfer 2011;54:4410-4428. https://doi.org/10.1016/j.ijheatmasstransfer.2011.04.048
  • [29] Farhana K, Kadirgama K, Rahman MM, Ramasamy D, Noor MM, Najafi G, et al. Improvement in the performance of solar collectors with nanofluids—A state-of-the-art review. Nano-Structures & Nano-Objects 2019;18:100276. https://doi.org/10.1016/j.nanoso.2019.100276
  • [30] Turkyilmazoglu M. Condensation of laminar film over curved vertical walls using single and two-phase nanofluid models. European Journal of Mechanics-B/Fluids 2017;65:184-91. https://doi.org/10.1016/j.euromechflu.2017.04.007
  • [31] Chen CC, Huang PC. Numerical study of heat transfer enhancement for a novel flat-plate solar water collector using metal-foam blocks. International Journal of Heat And Mass Transfer 2012;55:6734-6756. https://doi.org/10.1016/j.ijheatmasstransfer.2012.06.082
  • [32] Huang PC, Chen CC, Hwang HY. Thermal enhancement in a flat-plate solar water collector by flow pulsation and metal-foam blocks. International Journal of Heat and Mass Transfer 2013;61:696-720. https://doi.org/10.1016/j.ijheatmasstransfer.2013.02.037
  • [33] Hajipour M, Dehkordi AM. Mixed-convection flow of Al2O3–H O nanofluid in a channel partially filled with porous metal foam: experimental and numerical study. Experimental Thermal and Fluid Science 2014;53:49-56. https://doi.org/10.1016/j.expthermflusci.2013.11.002
  • [34] Rashidi S, Bovand M, Esfahani JA. Heat transfer enhancement and pressure drop penalty in porous solar heat exchangers: a sensitivity analysis. Energy Conversion and Management 2015;103:726-738. https://doi.org/10.1016/j.enconman.2015.07.019
  • [35] Manikandan GK, Iniyan S, Goic R. Enhancing the optical and thermal efficiency of a parabolic trough collector–A review. Applied Energy 2019;235:1524-1540. https://doi.org/10.1016/j.apenergy.2018.11.048

Details

Primary LanguageEnglish
SubjectsEngineering
Journal SectionArticles
AuthorsN. TONEKABONI  This is me
Islamic Azad University Nour Branch
0000-0002-1563-4407
IranH. SALARIAN  This is me (Primary Author)
Islamic Azad University Nour Branch
0000-0002-2161-0276
IranM. Eshagh NIMVARI  This is me
Amol University of Special Modern Technologies
0000-0002-7401-315X
IranJ. KHALEGHINIA  This is me
Islamic Azad University Nour Branch
0000-0001-5357-193X
Iran
Publication DateSeptember 2, 2021
Application DateDecember 28, 2020
Acceptance DateMay 9, 2020
Published in IssueYear 2021, Volume 7, Issue 6
Figure 1. Typical road and rail tunnel sections.

터널의 화재 위험을 평가하는 컴퓨터 모델(FASIT)

A Computer Model to Assess Fire Hazards in Tunnels (FASlT)

David A. Charters, W. Alan Gray, Andrew C. McIntosh
Charters is now with NHS Estates in Leeds (previously with AEA Consultancy
Services), and Gray and Mclntosh are with the University of Leeds, England.

Abstract

터널에서 화재 성장 움직임을 시뮬레이션하는 컴퓨터 모델이 설명되고 터널 시스템에 대한 간략한 개요가 표시됩니다. 질량 흐름, 속도, 연기 농도 및 열 전달을 예측하는 방법과 위험 출력 매개 변수 목록이 표시됩니다. 실험에 대한 모델의 유효성 검사와 향후 작업에 대한 가능한 방향도 제시됩니다.

Introduction

최근 도로 및 철도 터널의 화재 안전에 대해 운송 업계와 여행자들 사이에서 많은 우려가 제기되고 있습니다.

1,2,3 터널에서 연소 생성물은 한 방향 또는 두 방향을 제외한 모든 방향으로 제한되어 매우 빠른 연기 이동과 생명에 대한 빠른 위협을 초래할 수 있습니다.

이 분야의 많은 초기 작업은 Thomas에 의해 수행되었습니다. 4,5 AEA Consultancy Services와 University of Leeds의 연료 및 에너지부는 현재 터널의 구멍으로 인한 위험을 예측하는 컴퓨터 모델을 개발 중입니다.

이 모델은 터널 내 설비의 위험과 화재 위험 수준, 화재 방지 시스템의 이점을 평가하는 데 도움이 됩니다.

유사한 ‘구역’ 화재 모델에서 Considine et al. 7은 유해 물질 운송을 포함하는 피트에 대한 모델을 개발했으며 Miclea 등은 터널 환기에 대한 화재의 영향을 평가하고 비상 환기를 논의하는 터널 환기 모델을 개발했으며 Laage 등은 터널 환기 모델을 개발했습니다.

9는 특히 광산 네트워크의 화재에 대한 모델을 개발했습니다. 다른 터널 화재 모델에서 Kumar et al.10 및 Jones et al.11은 터널 화재의 유체 흐름을 예측하기 위해 전산 유체 역학(CFD) 또는 ‘장’ 모델을 사용합니다.

AEA/Leeds University에서 개발 중인 코드는 터널의 화재 위험을 예측하기 위한 더 큰 모델의 일부가 되도록 의도되었습니다.

이 코드는 FASIT(Fire growth And Smoke movement In Tunnels) 모델이라고 합니다.12 FASIT는 구조가 모듈식이므로 화염, 연기, 부력 흐름, 열 전달 등에 대한 개선된 모델을 많은 수의 재작성 없이 통합할 수 있습니다.

Figure 1. Typical road and rail tunnel sections.
Figure 1. Typical road and rail tunnel sections.
Figure 2. Tunnel zone/layer schematic.
Figure 2. Tunnel zone/layer schematic.
Figure 3. Schematic of plume mass flows°
Figure 3. Schematic of plume mass flows°

References

  1. Bertrand, A., “Opening Address,”Safety in Road and Rail Tunnels, 1992.
  2. Haack, A., “Fire Protection Traffic Tunnels-Initial Recognitions from Large Scale Tests,”Safety in Road and Rail Tunnels, 1992.
  3. Luchian, S.F., “The Central Artery/Tunnel Project Memorial Tunnel Fire Test Program,”Safety in Road and Rail Tunnels, 1992.
  4. Thomas, P.H., “The Movement of Buoyant Fluid Against a Stream and the Venting of Underground Fires,”Fire Research Note 351/1958, Fire Research Station, U.K., 1958.Google Scholar 
  5. Thomas, P.H., “The Movement of Smoke in Horizontal Passages Against an Air Flow,”Fire Research Note 723/1968, Fire Research Station, U.K., 1968.Google Scholar 
  6. Charters, D.A., “Fire Risk Assessment in Rail Tunnels,”Safety in Road and Rail Tunnels, 1992.
  7. Considine, M., Parry, S.T., and Blything, K.,Risk Assessments of Hazardous Substances Through Road Tunnels in the United Kingdom, Department of Transport, 1989.
  8. Miclea, P.C. and Murphy, R.E., “Assessment of Emergency Ventilation Capability in Case of Train Fire in a Tunnel Using Subway Environment Simulation (SES) Computer Program,”Proceedings of 4th U.S. Mine Ventilation Symposium, SME, 1989.
  9. Laage, L.W. and Yang, H., “Mine Fire Experiments at the Waldo Mine,”Proceedings of 5th U.S. Mine Ventilation Symposium, SME, 1991.
  10. Kumar, S. and Cox, G.,Mathematical Modeling of Fire in Road Tunnels—Validation of JASMINE Department of Transport, 1986.
  11. Simcox, S., Wilkies, N.S. and Jones, I.P., “Computer Simulation of the Flows of Hot Gases from Fire at King’s Cross Underground Station,”Institution of Mechanical Engineers, 1989.
  12. Charters, D.A., Gray, W. A., and McIntosh, A.C.,FASIT Tunnel Fire Computer Model—Physical Basis, AEA Technology/Leeds University, 1993.
  13. Heskestad, G., “Fire Plumes,”The SFPE Handbook of Fire Protection Engineering, SFPE/NFPA, 1988, Chapters 1–6.
  14. Drysdale, D.D.,An Introduction to Fire Dynamics, Wiley, 1985.
  15. British Standard (Draft for Development) 180,Guide for the Assessment of Toxic Hazards in Fire in Buildings and Transport British Standards Institution, 1989.
  16. Vantelon, J.P.,et al., Investigation of Fire-Induced Smoke Movement in Tunnels and Stations: An Application to the Paris Metro, Third International Symposium on Fire Safety Science, Elsevier, 1991.
  17. Heselden, A.J.M., “Studies of Fire and Smoke Behavior Relevant to Tunnels,”Current Paper CP66/78, Building Research Establishment, 1978.
  18. Emmons, H.W., “The Ceiling Jet in Fires,”Proceedings of the 3rd International Symposium of Fire Safety Science, Elsevier, 1991.
  19. Carslaw, H.S. and Jaeger, J.C.,Conduction of Heat in Solids, 2nd edition, Oxford University Press, 1959.
  20. Final Report on the Tests in the Ofenegg Tunnel, Commission for Safety Measures in Road Tunnels, Bern, 1965.
  21. Feizlmayr, A.H.,Brandversuche in Einen Tunnel, Bundesministerium für Banten und Technik, Heft 50, Vienna, 1976.Google Scholar 
  22. Keski-Rahkonen, O., Holmlund, C., Loikkanen, P., Ludrigsen, H., and Mikkola, E.,Two Full-Scale Pilot Fire Experiments in a Tunnel, VTT Finland, 1986.
  23. Marshall, I.A., Hines, M.A., Cutler, D.P., and Packer, S.D.,Fire Gallery Tests for Non-Metallic Materials Intended for Underground Use Project No. 7255-10/058, CEC, 1984.
  24. Private communication between Beckett, H. (HSE) and Burke, G. (AEA), 1986.
  25. McCaughey, M.N. and Fletcher, D.F.,Simulation of a Fire in a Tunnel, SRD, 1992.
  26. Fletcher, D.F. and Owens, M.P.,Tunnel Fire Modeling Using FLOW 3D: Progress and Suggested Future Work, SRD, 1993.
Fig. 2 Schematic diagram of the experimental Rijke tube

RIJKE 튜브 내부의 열음향 장에 대한 새로운 조사

A novel investigation of the thermoacoustic field inside a Rijke tube

B. EntezamW. Van Moorhem and J. MajdalaniPublished Online:22 Aug 2012 https://doi.org/10.2514/6.1998-2582

Abstract

이 논문에서는 Rijke 튜브 내부의 시간 종속 유동장의 실험 연구 및 계산 시뮬레이션에서 진행한 결과를 제시하고 해석합니다. 기존의 추측과 스케일링 분석을 기반으로 한 이론적 논의가 진행됩니다. 주요 결과에는 열 구동 진동에서 중요한 역할을 하는 것으로 보이는 유사성 매개변수가 포함됩니다. 이 매개변수는 열 섭동을 속도, 압력 및 특성 길이의 제곱과 관련시킵니다. 열 진동을 압력 및 속도 진동의 결합된 효과에 기인하는 간단한 이론은 계산, 실험 및 스케일링 고려 사항을 통해 논의됩니다. 이전의 분석 이론은 열 진동을 속도 또는 압력 진동에 연결했기 때문에 현재 분석 모델은 기존 추측에 동의하고 조정합니다. Rayleigh 기준에 따라 열원은 Rijke-tube 하단에서 1/4의 임계 거리에 위치해야 공명이 발생합니다. 이 관찰은 결합이 최대화되는 임계점이 음향 속도와 압력의 곱인 음향 강도가 가장 큰 공간 위치에 해당하기 때문에 제안된 해석을 확인합니다. 수치 시뮬레이션은 Rijke 튜브 내부의 압력 진동이 열 입력이 증가함에 따라 기하급수적으로 증가한다는 것을 보여줍니다. 충분히 작은 열 입력으로 음향 싱크가 소스를 초과하고 음향 감쇠가 발생합니다. 열 입력이 임계 임계값 이상으로 증가하면 음향 싱크가 불충분해져서 ​​내부 에너지 축적으로 인해 빠른 음향 증폭이 발생합니다.

In this paper, results proceeding from experimental studies and computational simulations of the time-dependent flowfield inside a Rijke tube are presented and interpreted. A theoretical discussion based on existing speculations and scaling analyses is carried out. The main results include a similarity parameter that appears to play an important role in the heat driven oscillations. This parameter relates heat perturbations to velocity, pressure, and the square of a characteristic length. A simple theory that attributes heat oscillations to the combined effects of pressure and velocity oscillations is discussed via computational, experimental, and scaling considerations. Since previous analytical theories link heat oscillations to either velocity or pressure oscillations, the current analytical model agrees with and reconciles between existing speculations. In compliance with the Rayleigh criterion, it is found that the heat source must be positioned at a critical distance of 1/4 from the Rijke-tube lower end for resonance to occur. This observation confirms our proposed interpretation since the critical point where coupling is maximized corresponds to a spatial location where the acoustic intensity, product of both acoustic velocities and pressures, is largest. Numerical simulations show that pressure oscillations inside the Rijke tube grow exponentially with increasing heat input With a sufficiently small heat input, the acoustic sinks exceed the sources and acoustic damping takes place. When the heat input is augmented beyond a critical threshold, acoustic sinks become insufficient causing rapid acoustic amplification by virtue of internal energy accumulation.

Fig. 2 Schematic diagram of the experimental Rijke tube
Fig. 2 Schematic diagram of the experimental Rijke tube
A novel investigation of the thermoacoustic field inside a Rijke tube
A novel investigation of the thermoacoustic field inside a Rijke tube

References

‘Entezam, B., Majdalani, J., and Van Moorhem, W. K.,
“Modeling of a Rijke-Tube Pulse Combustor Using
Computational Fluid Dynamics,” AIAA Paper 97-2718,
Seattle, WA, July 1997.

2George, W., and Reethof, G., “On the Fragility of
Acoustically Agglomerated Submicron Fly Ash
Particles,” Journal of Vibration, Acoustics, Stress, and
Reliability in Design, Vol. 108, July 1986, pp. 322-329.
3Tiwary R., and Reethof, G., “Hydrodynamic
Interaction of Spherical Aerosol Particles in a High
Intensity Acoustic Field,” Journal of Sound and
Vibration, Vol. 108, 1986, pp. 33-49.
4Reethof, G., “Acoustic Agglomeration of Power Plant
Fly Ash for Environmental and Hot Gas Clean-up,”
Transaction of the ASME, Vol. 110, Oct., 1988, pp.
552-557.
5
Song, L., Reethof, G., and Koopmann, G. H., “An
Improved Simulation Model of Acoustic
Agglomeration,” NCA Vol. 5, 89-WA, American
Society of Mechanical Engineers, Winter Annual
Meeting, San Francisco, CA, Dec., 10-15, 1989.
6Reethof, G., Koopmann, G. H., and Dorchak, T.,
“Acoustic Agglomeration for Paniculate Control at
High Temperature and high Pressure – Some Recent
results,” NCA Vol. 4, 89-WA, American Society of
Mechanical Engineers, Winter Annual Meeting, San
Francisco, CA, Dec., 10-15, 1989.
7Richards , G. A., and Bedick, R. C, “Application of
Acoustics in Advanced Energy Systems,” NCA Vol. 3,
89-WA, American Society of Mechanical Engineers,
Winter Annual Meeting, San Francisco, CA, Dec., 10-
15, 1989.
8Yavuzkurt, S., Ha, M. Y., Reethof, G., and Koopmann,
G., “Effect of Acoustic Field on the Combustion of
Coal Particles in a Rat Flame Burner,” Proceedings of
the Ist
Annual Pittsburgh Coal Conference, Pittsburgh,
PA, Sep., 1984, pp. 53-58.
^rice, E. W., “Review of Combustion Instability
Characteristics of Solid Propellants,” Advances in
Tactical Rocket Propulsion, AGARD Conference
Proceedings, No. 1, Part 2, Chap. 5, Technivision
Services, Maidenhead, England, 1968, pp. 141-194.
10Zinn, B.T., “State of the Art and Research Needs of
Pulsating Combustion,” NCA Vol. 19, 84-WA,
American Society of Mechanical Engineers, 1984.
“Rayleigh, J.W.S., The Theory of Sound, Vol. 1 and 2,
Dover Publications, New York, 1945, pp. 231-235.
12Zinn, B.T., Miller, N., Carvalho, J.A. Jr., and Daniel.
B. R., “Pulsating Combustion of Coal in a Rijke Type
Combustor,” Proceedings of the 19th International
Symposium on Combustion, 1982, pp. 1197-1203.
13Evans, R.E., and Putnam, A.A., “Rijke Tube
Apparatus,” Journal of Applied Physics, Vol. 360,
1966.
14Feldman, K. T., “Review of the Literature on Rijke
Thermoacoustic Phenomena, ” Journal of Sound and
Vibration, Vol. 7, 1968, pp. 83-89.
15Carvalho, J.R., Ferreira, C., Bressan, C., and Ferreira,
G., “Definition of Heater Location to Drive Maximum
Amplitude Acoustic Oscillations hi a Rijke Tube,”
Combustion and Flame, Vol. 76, 1989, pp. 17-27.
16Raun, R.L., Beckstead, M. W., Finlinson, J. C. , and
Brooks, K. P., “A Review of Rijke Tubes, Rijke
Burners and Related Devices,” Progress in Energy and
Combustion Science, Vol. 19, 1993, pp. 313-364.
17Chu, B. T., “Stability of Systems Containing a HeatSource-The Rayleigh Criterion, “NACA Research
Memorandum 56D27, 1956.
18Zinn, B. T., Daniel, B. R., and Shesdari, T.S.,
“Application of Pulsating Combustion in the Burning of
Solid Fuels,” Proceedings of the Symposium on Pulse
Combustion Technology for Heating Applications,
Argonne National Laboratory, 1979, pp. 239-248.
19Feldman, K.T., “Review of the Literature on
Soundhauss Thermoacoustic Phenomena ” Journal of
Sound and Vibration, Vol. 7, 1968, pp. 71-82.
20Flow Science Incorporated, Los Alamos, New
Mexico.

Fig.(9) Turbulent dissipation for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s

실험적 및 수치적 계단식 배수로의 에너지 소산 연구

The energy dissipation of Stepped Spillways experimentally and numerically

계단식 여수로는 댐의 통합된 부분인 수압 구조로, 넘침 흐름의 안전한 통과를 허용합니다. 이 논문에서는 에너지 소산을 최대한 활용하기 위해 여수로의 상대적인 계단 높이가 다른 영향을 조사하기 위해 실험적 및 수치적 연구를 수행했습니다.

여수로 위의 흐름 모델링은 RANS(Reynolds Averaged Navier-Stokes) 방정식을 푸는 상용 3D CFD 모델인 FLOW-3D를 사용하여 수행되었습니다.

FLOW-3D는 에너지 소산율을 분석하고 얻기 위해 사용되었습니다. 최대 에너지 소산을 달성할 수 있는 계단의 최상의 기하학은 관련 문헌을 검토하고 FLOW-3D에서 제안된 모델을 발명하여 결정되었습니다.

결과는 배수로의 상대적 계단 높이(hs/H) = 0.25. FLOW-3D를 사용한 수치모델은 다양한 실험모델에 대한 측정 데이터와 잘 일치하는 것으로 나타났습니다.

A. ShawkyAwada ,T. Hemdan Nasr-Allah a , Y. Abdallah Mohamed , b G. Mohamed Abdel-Aalb.
a Benah University, Faculty of Engineering, Egypt
b Zagazig University, Faculty of Engineering, Egypt

KEYWORDS

Stepped spillway, FLOW-3D, energy dissipation

Photo (1) general view of laboratory apparatus and flow direction
Photo (1) general view of laboratory apparatus and flow direction
Photo (2) stepped spillways for (hs/H) =0.17,0.25
Photo (2) stepped spillways for (hs/H) =0.17,0.25
Fig.(6) Pressure contours for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s Fig.(7) Velocity magnitude for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s
Fig.(6) Pressure contours for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s Fig.(7) Velocity magnitude for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s
Fig.(8) Flow depth for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s
Fig.(8) Flow depth for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s
Fig.(9) Turbulent dissipation for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s
Fig.(9) Turbulent dissipation for (hs/H)= 0.5, 0.33, 0.25, 0.17and 0.11) for Q = 40 l/s

REFERENCE

1- A. Hazzab, C. Chafi (2006),” Experimental investigation of flow and energy dissipation in stepped spillways “,
Larhyss Journal, ISSN 1112-3680,vol. 05, pp.91-104.
2- H. Chanson and S. Felder (2007), “Dynamic Similarity and Scale Effects in Turbulent Free-Surface Flows above
Triangular Cavities”, 16th Australasian Fluid Mechanics Conference Crown Plaza, Gold Coast, Australia.
3- G.A. Barani, M.B. Rahnama and N. Sohrabipoor (2005), “Investigation of Flow Energy Dissipation over Different
Stepped Spillways”, American Journal of Applied Sciences 2 (6): 1101-1105, ISSN 1546-9239.
4- Iman Naderi Rad and Mehdi Teimouri (2010),”An Investigation of Flow Energy Dissipation in Simple Stepped
Spillways by Numerical Model”, European Journal of Scientific Research ISSN 1450-216X Vol.47 No.4, pp.544-553.
5- Felder, S., and Chanson, H. (2011). “Energy Dissipation down a Stepped Spillway with Non-Uniform Step
Heights.” Journal of Hydraulic Engineering, ASCE, Vol. 137, No. 11, pp. 1543-1548 (DOI 10.1061/(ASCE)HY.1943-
7900.0000455) (ISSN 0733-9429).
6- Hubert Chanson (2008), “Physical modeling scale effects and self similarity of stepped spillways flows”, World
Environmental and Water Resources Congress, Ahupua’a.
7- Chanson, H., YASUDA, Y., and OHTSU, I. (2002). “Flow Resistance in Skimming Flows and its Modelling.” Can
Jl of Civ. Eng., Vol. 29, No. 6, pp. 809-819 (ISSN 0315-1468).

8- Chanson (2004), Hydraulics of stepped chutes: The transition flow, Journal of Hydraulic Research Vol. 42, No. 1 ,
pp. 43–54.
9- Moussa Rassaei, Sedigheh Rahbar (2014), Numerical flow model stepped spillways in order to maximize energy
dissipation using FLUENT software, IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org ISSN (e): 2250-3021,
ISSN (p): 2278-8719 Vol. 04, Issue 06 , PP 17-25.
10- Jean G Chatila & Bassam R Jurdi (2004), Stepped Spillway as an Energy Dissipater, Canadian Water Resources
Journal Vol. 29(3): 147–158 .
11- A.H. Nikseresht, N. Talebbeydokhti and M.J. Rezaei, (2013), Numerical simulation of two-phase flow on steppool spillways, Scientia Iranica A ,20 (2), 222–230.
12- Khosro Morovati , Afshin Eghbalzadeh and Saba Soori,(2016), Numerical Study of Energy Dissipation of Pooled
Stepped Spillways, Civil Engineering Journal , Vol. 2, No. 5.
13- Abbas Mansoori , Shadi Erfanian and Farhad Khamchin Moghadam (2017), A Study of the Conditions of Energy
Dissipation in Stepped Spillways with Λ -shaped step Using FLOW-3D, Civil Engineering Journal Vol. 3, No. 10,
October, 2017

Forming characteristics and control method of weld bead for GMAW on curved surface

곡면에 GMAW용 용접 비드의 형성 특성 및 제어 방법

Forming characteristics and control method of weld bead for GMAW on curved surface

The International Journal of Advanced Manufacturing Technology (2021)Cite this article

Abstract

곡면에서 GMAW 기반 적층 가공의 용접 성형 특성은 중력의 영향을 크게 받습니다. 성형면의 경사각이 크면 혹 비드(hump bead)와 같은 심각한 결함이 발생합니다.

본 논문에서는 양생면에서 용접 비드 형성의 형성 특성과 제어 방법을 연구하기 위해 용접 용융 풀 유동 역학의 전산 모델을 수립하고 제안된 모델을 검증하기 위해 증착 실험을 수행하였습니다.

결과는 용접 비드 경사각(α)이 증가함에 따라 역류의 속도가 증가하고 상향 용접의 경우 α > 60°일 때 불규칙한 험프 결함이 나타나는 것으로 나타났습니다.

상부 과잉 액체의 하향 압착력과 하부 상향 유동의 반동력과 표면장력 사이의 상호작용은 용접 혹 형성의 주요 요인이었다. 하향 용접의 경우 양호한 형태를 얻을 수 있었으며, 용접 비드 경사각이 증가함에 따라 용접 높이는 감소하고 용접 폭은 증가하였습니다.

하향 및 상향 용접을 위한 곡면의 용융 거동 및 성형 특성을 기반으로 험프 결함을 제어하기 위해 위브 용접을 통한 증착 방법을 제안하였습니다.

성형 궤적의 변화로 인해 용접 방향의 중력 성분이 크게 감소하여 용융 풀 흐름의 안정성이 향상되었으며 복잡한 표면에서 안정적이고 일관된 용접 비드를 얻는 데 유리했습니다.

하향 용접과 상향 용접 사이의 단일 비드의 치수 편차는 7% 이내였으며 하향 및 상향 혼합 혼합 비드 중첩 증착에서 비드의 변동 편차는 0.45로 GMAW 기반 적층 제조 공정에서 허용될 수 있었습니다.

이러한 발견은 GMAW를 기반으로 하는 곡선 적층 적층 제조의 용접 비드 형성 제어에 기여했습니다.

The weld forming characteristics of GMAW-based additive manufacturing on curved surface are dramatically influenced by gravity. Large inclined angle of the forming surface would lead to severe defects such as hump bead. In this paper, a computational model of welding molten pool flow dynamics was established to research the forming characteristic and control method of weld bead forming on cured surface, and deposition experiments were conducted to verify the proposed model. Results indicated that the velocity of backward flows increased with the increase of weld bead tilt angle (α) and irregular hump defects appeared when α > 60° for upward welding. The interaction between the downward squeezing force of the excess liquid at the top and the recoil force of the upward flow at the bottom and the surface tension were primary factors for welding hump formation. For downward welding, a good morphology shape could be obtained, and the weld height decreased and the weld width increased with the increase of weld bead tilt angle. Based on the molten behaviors and forming characteristics on curved surface for downward and upward welding, the method of deposition with weave welding was proposed to control hump defects. Gravity component in the welding direction was significantly reduced due to the change of forming trajectory, which improved the stability of the molten pool flow and was beneficial to obtain stable and consistent weld bead on complex surface. The dimensional deviations of the single bead between downward and upward welding were within 7% and the fluctuation deviation of the bead in multi-bead overlapping deposition with mixing downward and upward welding was 0.45, which could be acceptable in GMAW-based additive manufacturing process. These findings contributed to the weld bead forming control of curve layered additive manufacturing based on GMAW.

Keywords

  • Molten pool behaviors
  • GMAW-based WAAM
  • Deposition with weave welding
  • Welding on curved surface
  • Fig. 1extended data figure 1
  • Fig. 2extended data figure 2
  • Fig. 3extended data figure 3
  • Fig. 4extended data figure 4
  • Fig. 5extended data figure 5
  • Fig. 6extended data figure 6
  • Fig. 7extended data figure 7
  • Fig. 8extended data figure 8
  • Fig. 9extended data figure 9
  • Fig. 10extended data figure 10
  • Fig. 11extended data figure 11
  • Fig. 12extended data figure 12
  • Fig. 13extended data figure 13
  • Fig. 14extended data figure 14
  • Fig. 15extended data figure 15
  • Fig. 16extended data figure 16
  • Fig. 17extended data figure 17
  • Fig. 18extended data figure 18
  • Fig. 19extended data figure 19
  • Fig. 20extended data figure 20
  • Fig. 21extended data figure 21
  • Fig. 22extended data figure 22
  • Fig. 23extended data figure 23
  • Fig. 24extended data figure 24
  • Fig. 25extended data figure 25
  • Fig. 26extended data figure 26
  • Fig. 27extended data figure 27
  • Fig. 28extended data figure 28
  • Fig. 29extended data figure 29
  • Fig. 30extended data figure 30
  • Fig. 31extended data figure 31
  • Fig. 32extended data figure 32
  • Fig. 33extended data figure 33
  • Fig. 34extended data figure 34
  • Fig. 35extended data figure 35
  • Fig. 36extended data figure 36
  • Fig. 37extended data figure 37
  • Fig. 38extended data figure 38

References

  1. 1.Williams SW, Martina F, Addison AC, Ding J, Pardal G, Colegrove P (2016) Wire + arc additive manufacturing. Mater Sci Technol (United Kingdom) 32:641–647. https://doi.org/10.1179/1743284715Y.0000000073Article Google Scholar 
  2. 2.Pan ZX, Ding DH, Wu BT, Cuiuri D, Li HJ, Norrish J (2018) Arc welding processes for additive manufacturing: a review. In: Transactions on intelligent welding manufacturing. Springer Singapore, pp 3–24. https://doi.org/10.1007/978-981-10-5355-9_1
  3. 3.Panchagnula JS, Simhambhatla S (2018) Manufacture of complex thin-walled metallic objects using weld-deposition based additive manufacturing. Robot Comput Integr Manuf 49:194–203. https://doi.org/10.1016/j.rcim.2017.06.003Article Google Scholar 
  4. 4.Lu S, Zhou J, Zhang JS (2015) Optimization of welding thickness on casting-steel surface for production of forging die. Int J Adv Manuf Technol 76:1411–1419. https://doi.org/10.1007/s00170-014-6371-9Article Google Scholar 
  5. 5.Huang B, Singamneni SB (2015) Curved layer adaptive slicing (CLAS) for fused deposition modelling. Rapid Prototyp J 21:354–367. https://doi.org/10.1108/RPJ-06-2013-0059Article Google Scholar 
  6. 6.Jin Y, Du J, He Y, Fu GQ (2017) Modeling and process planning for curved layer fused deposition. Int J Adv Manuf Technol 91:273–285. https://doi.org/10.1007/s00170-016-9743-5Article Google Scholar 
  7. 7.Xie FB, Chen LF, Li ZY, Tang K (2020) Path smoothing and feed rate planning for robotic curved layer additive manufacturing. Robot Comput Integr Manuf 65. https://doi.org/10.1016/j.rcim.2020.101967
  8. 8.Ding YY, Dwivedi R, Kovacevic R (2017) Process planning for 8-axis robotized laser-based direct metal deposition system: a case on building revolved part. Robot Comput Integr Manuf 44:67–76. https://doi.org/10.1016/j.rcim.2016.08.008Article Google Scholar 
  9. 9.Cho DW, Na SJ (2015) Molten pool behaviors for second pass V-groove GMAW. Int J Heat Mass Transf 88:945–956. https://doi.org/10.1016/j.ijheatmasstransfer.2015.05.021Article Google Scholar 
  10. 10.Cho DW, Na SJ, Cho MH, Lee JS (2013) A study on V-groove GMAW for various welding positions. J Mater Process Technol 213:1640–1652. https://doi.org/10.1016/j.jmatprotec.2013.02.015Article Google Scholar 
  11. 11.Hejripour F, Valentine DT, Aidun DK (2018) Study of mass transport in cold wire deposition for wire arc additive manufacturing. Int J Heat Mass Transf 125:471–484. https://doi.org/10.1016/j.ijheatmasstransfer.2018.04.092Article Google Scholar 
  12. 12.Yuan L, Pan ZX, Ding DH, He FY, Duin SV, Li HJ, Li WH (2020) Investigation of humping phenomenon for the multi-directional robotic wire and arc additive manufacturing. Robot Comput Integr Manuf 63. https://doi.org/10.1016/j.rcim.2019.101916
  13. 13.Nguyen MC, Medale M, Asserin O, Gounand S, Gilles P (2017) Sensitivity to welding positions and parameters in GTA welding with a 3D multiphysics numerical model. Numer Heat Transf Part A Appl 71:233–249. https://doi.org/10.1080/10407782.2016.1264747Article Google Scholar 
  14. 14.Gu H, Li L (2019) Computational fluid dynamic simulation of gravity and pressure effects in laser metal deposition for potential additive manufacturing in space. Int J Heat Mass Transf 140:51–65. https://doi.org/10.1016/j.ijheatmasstransfer.2019.05.081Article Google Scholar 
  15. 15.Cho MH, Farson DF (2007) Understanding bead hump formation in gas metal arc welding using a numerical simulation. Metall Mater Trans B Process Metall Mater Process Sci 38:305–319. https://doi.org/10.1007/s11663-007-9034-5Article Google Scholar 
  16. 16.Nguyen TC, Weckman DC, Johnson DA, Kerr HW (2005) The humping phenomenon during high speed gas metal arc welding. Sci Technol Weld Join 10:447–459. https://doi.org/10.1179/174329305X44134Article Google Scholar 
  17. 17.Philip Y, Xu ZY, Wang Y, Wang R, Ye X (2019) Investigation of humping defect formation in a lap joint at a high-speed hybrid laser-GMA welding. Results Phys 13. https://doi.org/10.1016/j.rinp.2019.102341
  18. 18.Hu ZQ, Qin XP, Shao T, Liu HM (2018) Understanding and overcoming of abnormity at start and end of the weld bead in additive manufacturing with GMAW. Int J Adv Manuf Technol 95:2357–2368. https://doi.org/10.1007/s00170-017-1392-9Article Google Scholar 
  19. 19.Tang SY, Wang GL, Huang C, Li RS, Zhou SY, Zhang HO (2020) Investigation, modeling and optimization of abnormal areas of weld beads in wire and arc additive manufacturing. Rapid Prototyp J 26:1183–1195. https://doi.org/10.1108/RPJ-08-2019-0229Article Google Scholar 
  20. 20.Bai X, Colegrove P, Ding J, Zhou XM, Diao CL, Bridgeman P, Honnige JR, Zhang HO, Williams S (2018) Numerical analysis of heat transfer and fluid flow in multilayer deposition of PAW-based wire and arc additive manufacturing. Int J Heat Mass Transf 124:504–516. https://doi.org/10.1016/j.ijheatmasstransfer.2018.03.085Article Google Scholar 
  21. 21.Siewert E, Schein J, Forster G (2013) Determination of enthalpy, temperature, surface tension and geometry of the material transfer in PGMAW for the system argon-iron. J Phys D Appl Phys 46. https://doi.org/10.1088/0022-3727/46/22/224008
  22. 22.Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15:299–305. https://doi.org/10.1007/BF02667333Article Google Scholar 
  23. 23.Fachinotti VD, Cardona A (2008) Semi-analytical solution of the thermal field induced by a moving double-ellipsoidal welding heat source in a semi-infinite body. Mec Comput XXVII:1519–1530
  24. 24.Nguyen NT, Mai YW, Simpson S, Ohta A (2004) Analytical approximate solution for double ellipsoidal heat source in finite thick plate. Weld J 83:82–93Google Scholar 
  25. 25.Goldak J, Chakravarti A, Bibby M (1985) A double ellipsoid finite element model for welding heat sources. IIW Doc. No. 212-603-85
  26. 26.Gu Y, Li YD, Yong Y, Xu FL, Su LF (2019) Determination of parameters of double-ellipsoidal heat source model based on optimization method. Weld World 63:365–376. https://doi.org/10.1007/s40194-018-00678-wArticle Google Scholar 
  27. 27.Wu CS, Tsao KC (1990) Modelling the three-dimensional fluid flow and heat transfer in a moving weld pool. Eng Comput 7:241–248. https://doi.org/10.1108/eb023811Article Google Scholar 
  28. 28.Zhan XH, Liu XB, Wei YH, Chen JC, Chen J, Liu HB (2017) Microstructure and property characteristics of thick Invar alloy plate joints using weave bead welding. J Mater Process Technol 244:97–105. https://doi.org/10.1016/j.jmatprotec.2017.01.014Article Google Scholar 
  29. 29.Zhan XH, Zhang D, Liu XB, Chen J, Wei YH, Liu RP (2017) Comparison between weave bead welding and multi-layer multi-pass welding for thick plate Invar steel. Int J Adv Manuf Technol 88:2211–2225. https://doi.org/10.1007/s00170-016-8926-4Article Google Scholar 
  30. 30.Xu GX, Li L, Wang JY, Zhu J, Li PF (2018) Study of weld formation in swing arc narrow gap vertical GMA welding by numerical modeling and experiment. Int J Adv Manuf Technol 96:1905–1917. https://doi.org/10.1007/s00170-018-1729-zArticle Google Scholar 
  31. 31.Li YZ, Sun YF, Han QL, Zhang GJ, Horvath I (2018) Enhanced beads overlapping model for wire and arc additive manufacturing of multi-layer multi-bead metallic parts. J Mater Process Technol 252:838–848. https://doi.org/10.1016/j.jmatprotec.2017.10.017Article Google Scholar 
CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2

원추형 중앙 배플 수로의 CFD 시뮬레이션

CFD Simulations of Conical Central Baffle Flumes

Abstract

Ankur KapoorAniruddha D. Ghare; and Avinash M. Badar

원추형 중앙 배플 수로는 개방 채널에서 임시 유량 측정을 위한 효과적인 솔루션을 제공합니다. 

원추형 중앙 배플 수로는 원뿔 모양의 장애물 또는 열린 수로의 중심에서 수직으로 향하는 중앙 배플로 구성됩니다. 본 연구에서, 원추형 중앙 배플 수로를 사용하여 개방 채널에서 유량 측정을 위해 이전에 개발된 배출 예측 모델은 더 넓은 적용 범위를 커버하기 위해 직사각형 및 사다리꼴 채널에서 사용하기 위해 실험적으로 재 보정되었습니다. 

제안된 보정 방정식은 FLOW-3D를 사용한 전산유체역학(CFD) 시뮬레이션 결과를 사용하여 확장된 범위의 흐름 및 기하학적 매개변수에 대해 검증되었습니다. 

시뮬레이션 연구는 두 단계로 수행됩니다. 첫 번째 단계는 시뮬레이션의 수면 프로파일과 동일한 배출 및 흐름 조건에 대한 실험 흐름을 비교하여 설정한 정의된 시뮬레이션 문제의 검증입니다. 

두 번째 단계는 무차원 방전 및 측면 경사(중1= 0중1=0, 0.50, 1.00 및 1.50). 80% 미만의 수중에서 방전 예측의 오류는 평균값이 거의 3%로 항상 10% 미만인 것으로 나타났습니다. 

CFD 분석 결과에 따르면 보정된 배출 예측 모델의 사용은 수중 한계 80%까지 권장되었으며, 그 이상에서는 오차가 10% 이상인 것으로 나타났습니다.

Conical central baffle flumes present an effective solution for temporary flow measurements in open channels. A conical central baffle flume consists of a cone-shaped obstruction, or a central baffle, oriented vertically at the center of an open channel. In the present study, a previously developed discharge prediction model for flow measurements in open channels using the conical central baffle flumes has been experimentally recalibrated for use in rectangular and trapezoidal channels to cover a wider application range. The proposed calibration equation has been validated for an extended range of flow and geometrical parameters using the results of computational fluid dynamics (CFD) simulations using Flow-3D. The simulation studies are carried out in two steps. The first step is the validation of the defined simulation problem set up by comparing the water surface profiles of the simulation and experiment flows for the same discharge and flow conditions. The second step is the validation of the proposed discharge prediction model for the extended range (0–0.50) of the dimensionless discharge and side slopes (m1=0m1=0, 0.50, 1.00, and 1.50). It is found that for submergence less than 80%, the error in discharge prediction is always less than 10% with a mean value of nearly 3%. Based on the results of the CFD analysis, the use of the calibrated discharge prediction model has been recommended up to a submergence limit of 80%, beyond which the errors are found to be greater than 10%.

ASCE Library CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2
ASCE Library CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2
CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2
CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2
CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2
CFD Simulations of Conical Central Baffle Flumes | Journal of Irrigation and Drainage Engineering | Vol 148, No 2
Channel Flow Measurement Using Portable Conical Central Baffle | Journal of Irrigation and Drainage Engineering | Vol 145, No 11
Channel Flow Measurement Using Portable Conical Central Baffle | Journal of Irrigation and Drainage Engineering | Vol 145, No 11
여수로 방류에 따른 여수로 바닥 슬래브의 손상 메커니즘 검토

여수로 방류에 따른 여수로 바닥 슬래브의 손상 메커니즘 검토

Examinations of Damage Mechanism on the Chuteway Slabs of Spillway under Various Flow Conditions

  • Yoo, Hyung Ju ;
  • Shin, Dong-Hoon ;
  • Lee, Seung Oh
  • 유형주 (홍익대학교 공과대학 건설환경공학과) ;
  • 신동훈 (K-water연구원 물인프라안전연구소) ;
  • 이승오 (홍익대학교 공과대학 건설환경공학과)
  • Published : 2021.06.03

Abstract

최근 기후변화로 인한 집중호우의 영향으로 홍수 시 댐으로의 유입량이 설계 당시보다 증가하여 댐의 안전성 확보가 필요하다(감사원, 2003). 이에 건설교통부(2003)는 기후변화와 댐 노후화에 대비하여 치수능력증대사업을 추진하여 댐의 홍수배제능력을 확보하였고, 환경부(2020)에서는 40년 이상 경과된 댐을 대상으로 스마트 안전관리체계 구축을 통한 선제적 보수보강, 성능개선 및 자산관리로 댐의 장수명화를 목적으로 댐의 국가안전대진단을 추진하고 있다. 이에 본 연구에서는 댐 시설(여수로)의 노후도 평가 시 활용 될 수 있는 여수로 표면손상 원인규명에 대하여 3차원 수치모형(FLOW-3D 및 COMSOL Multiphysics)을 통해 검토하고자 한다. 연구대상 댐은 𐩒𐩒댐으로 지형 및 여수로를 구축하였으며, 계획방류량(200년 빈도) 및 최대방류량(PMF) 조건에서 모의를 수행하였다. 수치모의 계산의 정확도 검토를 위하여 Baffle의 설치를 통하여 시간에 따른 유량의 변화를 설계 값과 비교하였고 오차가 1.0% 이내를 만족하는 것을 확인하였다. 여수로 표면손상의 다양한 원인 중 기존연구(USBR, 2019)를 통하여 공동침식(Cavitation Erosion) 및 수력잭킹(Hydraulic Jacking)에 초점을 두었으며 방류조건 별 공동지수(Cavitation Index)산정을 통하여 공동침식 위험 구간을 확인하였다. 이음부의 균열 및 공동으로 인한 표층부 콘크리트의 탈락현상을 가속화시키는 수력잭킹 검토를 위하여 국부모형을 구축하였고 음압력(Negative Pressure), 정체압력(Stagnation Pressure), 양압력(Uplift Pressure)의 분포를 확인하였다. 최종적으로 COMSOL Multiphysics를 통하여 압력분포에 따른 구조해석을 수행하여 폰 미세스(Von Mises) 등가응력 및 변위를 검토하여 콘크리트의 탈락가능성을 확인하였다. 본 연구는 여수로 공동부 및 균열부에서의 손상메커니즘을 확인할 수 있는 기초적인 연구이지만 향후에는 다양한 지형조건 및 흐름조건에서의 압력분포 분석 및 유체-구조물 상호작용(Fluid-Structure Interaction, FSI)모의를 수행한다면 구조물 노후도 및 잔존수명 평가에 필요한 손상한계함수 도출이 가능할 것으로 기대된다.

Keywords

Figure 2. (a) Scanning electron microscopy images of Ti6Al4V powder particles and (b) simulated powder bed using discrete element modelling

Laser Powder Bed에서 Laser Drilling에 의한 Keyhole 형성 Ti6Al4V 생체 의학 합금의 융합: 메조스코픽 전산유체역학 시뮬레이션 대 경험적 검증을 사용한 수학적 모델링

Keyhole Formation by Laser Drilling in Laser Powder Bed Fusion of Ti6Al4V Biomedical Alloy: Mesoscopic Computational Fluid Dynamics Simulation versus Mathematical Modelling Using Empirical Validation

Asif Ur Rehman 1,2,3,*
,† , Muhammad Arif Mahmood 4,*
,† , Fatih Pitir 1
, Metin Uymaz Salamci 2,3
,
Andrei C. Popescu 4 and Ion N. Mihailescu 4

Abstract

LPBF(Laser Powder Bed fusion) 공정에서 작동 조건은 열 분포를 기반으로 레이저 유도 키홀 영역을 결정하는 데 필수적입니다. 얕은 구멍과 깊은 구멍으로 분류되는 이러한 영역은 LPBF 프로세스에서 확률과 결함 형성 강도를 제어합니다.

LPBF 프로세스의 핵심 구멍을 연구하고 제어하기 위해 수학적 및 CFD(전산 유체 역학) 모델이 제공됩니다. CFD의 경우 이산 요소 모델링 기법을 사용한 유체 체적 방법이 사용되었으며, 분말 베드 보이드 및 표면에 의한 레이저 빔 흡수를 포함하여 수학적 모델이 개발되었습니다.

동적 용융 풀 거동을 자세히 살펴봅니다. 실험적, CFD 시뮬레이션 및 분석적 컴퓨팅 결과 간에 정량적 비교가 수행되어 좋은 일치를 얻습니다.

LPBF에서 레이저 조사 영역 주변의 온도는 높은 내열성과 분말 입자 사이의 공기로 인해 분말층 주변에 비해 급격히 상승하여 레이저 횡방향 열파의 이동이 느려집니다. LPBF에서 키홀은 에너지 밀도에 의해 제어되는 얕고 깊은 키홀 모드로 분류될 수 있습니다. 에너지 밀도를 높이면 얕은 키홀 구멍 모드가 깊은 키홀 구멍 모드로 바뀝니다.

깊은 키홀 구멍의 에너지 밀도는 다중 반사와 키홀 구멍 내의 2차 반사 빔의 집중으로 인해 더 높아져 재료가 빠르게 기화됩니다.

깊은 키홀 구멍 모드에서는 온도 분포가 높기 때문에 액체 재료가 기화 온도에 가까우므로 얕은 키홀 구멍보다 구멍이 형성될 확률이 훨씬 높습니다. 온도가 급격히 상승하면 재료 밀도가 급격히 떨어지므로 비열과 융해 잠열로 인해 유체 부피가 증가합니다.

그 대가로 표면 장력을 낮추고 용융 풀 균일성에 영향을 미칩니다.

In the laser powder bed fusion (LPBF) process, the operating conditions are essential in determining laser-induced keyhole regimes based on the thermal distribution. These regimes, classified into shallow and deep keyholes, control the probability and defects formation intensity in the LPBF process. To study and control the keyhole in the LPBF process, mathematical and computational fluid dynamics (CFD) models are presented. For CFD, the volume of fluid method with the discrete element modeling technique was used, while a mathematical model was developed by including the laser beam absorption by the powder bed voids and surface. The dynamic melt pool behavior is explored in detail. Quantitative comparisons are made among experimental, CFD simulation and analytical computing results leading to a good correspondence. In LPBF, the temperature around the laser irradiation zone rises rapidly compared to the surroundings in the powder layer due to the high thermal resistance and the air between the powder particles, resulting in a slow travel of laser transverse heat waves. In LPBF, the keyhole can be classified into shallow and deep keyhole mode, controlled by the energy density. Increasing the energy density, the shallow keyhole mode transforms into the deep keyhole mode. The energy density in a deep keyhole is higher due to the multiple reflections and concentrations of secondary reflected beams within the keyhole, causing the material to vaporize quickly. Due to an elevated temperature distribution in deep keyhole mode, the probability of pores forming is much higher than in a shallow keyhole as the liquid material is close to the vaporization temperature. When the temperature increases rapidly, the material density drops quickly, thus, raising the fluid volume due to the specific heat and fusion latent heat. In return, this lowers the surface tension and affects the melt pool uniformity.

Keywords: laser powder bed fusion; computational fluid dynamics; analytical modelling; shallow
and deep keyhole modes; experimental correlation

Figure 1. Powder bed schematic with voids.
Figure 1. Powder bed schematic with voids.
Figure 2. (a) Scanning electron microscopy images of Ti6Al4V powder particles and (b) simulated powder bed using discrete element modelling
Figure 2. (a) Scanning electron microscopy images of Ti6Al4V powder particles and (b) simulated powder bed using discrete element modelling
Figure 3. Temperature field contour formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.
Figure 3. Temperature field contour formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.
Figure 4. Detailed view of shallow depth melt mode with temperature field at 0.695 ms
Figure 4. Detailed view of shallow depth melt mode with temperature field at 0.695 ms
Figure 5. Melt flow stream traces formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms
Figure 5. Melt flow stream traces formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms
Figure 6. Density evolution of the melt pool at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.
Figure 6. Density evolution of the melt pool at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.
Figure 7. Un-melted and melted regions at different time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms
Figure 7. Un-melted and melted regions at different time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms
Figure 8. Transformation from shallow depth melt flow to deep keyhole formation when laser power increased from (a) 170 W to (b) 200 W
Figure 8. Transformation from shallow depth melt flow to deep keyhole formation when laser power increased from (a) 170 W to (b) 200 W
Figure 9. Stream traces and laser beam multiple reflections in deep keyhole melt flow mode
Figure 9. Stream traces and laser beam multiple reflections in deep keyhole melt flow mode
Figure 10. A comparison between analytical and CFD simulation results for peak thermal distribution value in the deep keyhole formation
Figure 10. A comparison between analytical and CFD simulation results for peak thermal distribution value in the deep keyhole formation
Figure 11. A comparison among experiments [49], CFD and analytical simulations for deep keyhole top width and bottom width
Figure 11. A comparison among experiments [49], CFD and analytical simulations for deep keyhole top width and bottom width

References

  1. Kok, Y.; Tan, X.P.; Wang, P.; Nai, M.L.S.; Loh, N.H.; Liu, E.; Tor, S.B. Anisotropy and heterogeneity of microstructure and
    mechanical properties in metal additive manufacturing: A critical review. Mater. Des. 2018, 139, 565–586. [CrossRef]
  2. Ansari, P.; Salamci, M.U. On the selective laser melting based additive manufacturing of AlSi10Mg: The process parameter
    investigation through multiphysics simulation and experimental validation. J. Alloys Compd. 2022, 890, 161873. [CrossRef]
  3. Guo, N.; Leu, M.C. Additive manufacturing: Technology, applications and research needs. Front. Mech. Eng. 2013, 8, 215–243.
    [CrossRef]
  4. Mohsin Raza, M.; Lo, Y.L. Experimental investigation into microstructure, mechanical properties, and cracking mechanism of
    IN713LC processed by laser powder bed fusion. Mater. Sci. Eng. A 2021, 819, 141527. [CrossRef]
  5. Dezfoli, A.R.A.; Lo, Y.L.; Raza, M.M. Prediction of Epitaxial Grain Growth in Single-Track Laser Melting of IN718 Using Integrated
    Finite Element and Cellular Automaton Approach. Materials 2021, 14, 5202. [CrossRef]
  6. Tiwari, S.K.; Pande, S.; Agrawal, S.; Bobade, S.M. Selection of selective laser sintering materials for different applications. Rapid
    Prototyp. J. 2015, 21, 630–648. [CrossRef]
  7. Liu, F.H. Synthesis of bioceramic scaffolds for bone tissue engineering by rapid prototyping technique. J. Sol-Gel Sci. Technol.
    2012, 64, 704–710. [CrossRef]
  8. Ur Rehman, A.; Sglavo, V.M. 3D printing of geopolymer-based concrete for building applications. Rapid Prototyp. J. 2020, 26,
    1783–1788. [CrossRef]
  9. Ur Rehman, A.; Sglavo, V.M. 3D printing of Portland cement-containing bodies. Rapid Prototyp. J. 2021. ahead of print. [CrossRef]
  10. Popovich, A.; Sufiiarov, V. Metal Powder Additive Manufacturing. In New Trends in 3D Printing; InTech: Rijeka, Croatia, 2016.
  11. Jia, T.; Zhang, Y.; Chen, J.K.; He, Y.L. Dynamic simulation of granular packing of fine cohesive particles with different size
    distributions. Powder Technol. 2012, 218, 76–85. [CrossRef]
  12. Ansari, P.; Ur Rehman, A.; Pitir, F.; Veziroglu, S.; Mishra, Y.K.; Aktas, O.C.; Salamci, M.U. Selective Laser Melting of 316L
    Austenitic Stainless Steel: Detailed Process Understanding Using Multiphysics Simulation and Experimentation. Metals 2021,
    11, 1076. [CrossRef]
  13. Ur Rehman, A.; Tingting, L.; Liao, W. 4D Printing; Printing Ceramics from Metals with Selective Oxidation. Patent No.
    W0/2019/052128, 21 March 2019.
  14. Ullah, A.; Wu, H.; Ur Rehman, A.; Zhu, Y.; Liu, T.; Zhang, K. Influence of laser parameters and Ti content on the surface
    morphology of L-PBF fabricated Titania. Rapid Prototyp. J. 2021, 27, 71–80. [CrossRef]
  15. Ur Rehman, A. Additive Manufacturing of Ceramic Materials and Combinations with New Laser Strategies. Master’s Thesis,
    Nanjing University of Science and Technology, Nanjing, China, 2017.
  16. Wong, K.V.; Hernandez, A. A Review of Additive Manufacturing. ISRN Mech. Eng. 2012, 2012, 1–10. [CrossRef]
  17. Körner, C. Additive manufacturing of metallic components by selective electron beam melting—A review. Int. Mater. Rev. 2016,
    61, 361–377. [CrossRef]
  18. Fayazfar, H.; Salarian, M.; Rogalsky, A.; Sarker, D.; Russo, P.; Paserin, V.; Toyserkani, E. A critical review of powder-based additive
    manufacturing of ferrous alloys: Process parameters, microstructure and mechanical properties. Mater. Des. 2018, 144, 98–128.
    [CrossRef]
  19. Everton, S.K.; Hirsch, M.; Stavroulakis, P.I.; Leach, R.K.; Clare, A.T. Review of in-situ process monitoring and in-situ metrology
    for metal additive manufacturing. Mater. Des. 2016, 95, 431–445. [CrossRef]
  20. Sing, S.L.; An, J.; Yeong, W.Y.; Wiria, F.E. Laser and electron-beam powder-bed additive manufacturing of metallic implants: A
    review on processes, materials and designs. J. Orthop. Res. 2016, 34, 369–385. [CrossRef] [PubMed]
  21. Olakanmi, E.O.; Cochrane, R.F.; Dalgarno, K.W. A review on selective laser sintering/melting (SLS/SLM) of aluminium alloy
    powders: Processing, microstructure, and properties. Prog. Mater. Sci. 2015, 74, 401–477. [CrossRef]
  22. Mahmood, M.A.; Popescu, A.C.; Hapenciuc, C.L.; Ristoscu, C.; Visan, A.I.; Oane, M.; Mihailescu, I.N. Estimation of clad geometry
    and corresponding residual stress distribution in laser melting deposition: Analytical modeling and experimental correlations.
    Int. J. Adv. Manuf. Technol. 2020, 111, 77–91. [CrossRef]
  23. Mahmood, M.A.; Popescu, A.C.; Oane, M.; Ristoscu, C.; Chioibasu, D.; Mihai, S.; Mihailescu, I.N. Three-jet powder flow
    and laser–powder interaction in laser melting deposition: Modelling versus experimental correlations. Metals 2020, 10, 1113.
    [CrossRef]
  24. King, W.; Anderson, A.T.; Ferencz, R.M.; Hodge, N.E.; Kamath, C.; Khairallah, S.A. Overview of modelling and simulation of
    metal powder bed fusion process at Lawrence Livermore National Laboratory. Mater. Sci. Technol. 2015, 31, 957–968. [CrossRef]
  1. Gong, H.; Rafi, K.; Gu, H.; Starr, T.; Stucker, B. Analysis of defect generation in Ti-6Al-4V parts made using powder bed fusion
    additive manufacturing processes. Addit. Manuf. 2014, 1, 87–98. [CrossRef]
  2. Frazier, W.E. Metal additive manufacturing: A review. J. Mater. Eng. Perform. 2014, 23, 1917–1928. [CrossRef]
  3. Panwisawas, C.; Qiu, C.L.; Sovani, Y.; Brooks, J.W.; Attallah, M.M.; Basoalto, H.C. On the role of thermal fluid dynamics into the
    evolution of porosity during selective laser melting. Scr. Mater. 2015, 105, 14–17. [CrossRef]
  4. Yan, W.; Ge, W.; Qian, Y.; Lin, S.; Zhou, B.; Liu, W.K.; Lin, F.; Wagner, G.J. Multi-physics modeling of single/multiple-track defect
    mechanisms in electron beam selective melting. Acta Mater. 2017, 134, 324–333. [CrossRef]
  5. Qian, Y.; Yan, W.; Lin, F. Parametric study and surface morphology analysis of electron beam selective melting. Rapid Prototyp. J.
    2018, 24, 1586–1598. [CrossRef]
  6. Panwisawas, C.; Perumal, B.; Ward, R.M.; Turner, N.; Turner, R.P.; Brooks, J.W.; Basoalto, H.C. Keyhole formation and thermal
    fluid flow-induced porosity during laser fusion welding in titanium alloys: Experimental and modelling. Acta Mater. 2017, 126,
    251–263. [CrossRef]
  7. King, W.E.; Barth, H.D.; Castillo, V.M.; Gallegos, G.F.; Gibbs, J.W.; Hahn, D.E.; Kamath, C.; Rubenchik, A.M. Observation of
    keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. J. Mater. Process. Technol. 2014, 214, 2915–2925.
    [CrossRef]
  8. Panwisawas, C.; Sovani, Y.; Turner, R.P.; Brooks, J.W.; Basoalto, H.C.; Choquet, I. Modelling of thermal fluid dynamics for fusion
    welding. J. Mater. Process. Technol. 2018, 252, 176–182. [CrossRef]
  9. Martin, A.A.; Calta, N.P.; Hammons, J.A.; Khairallah, S.A.; Nielsen, M.H.; Shuttlesworth, R.M.; Sinclair, N.; Matthews, M.J.;
    Jeffries, J.R.; Willey, T.M.; et al. Ultrafast dynamics of laser-metal interactions in additive manufacturing alloys captured by in situ
    X-ray imaging. Mater. Today Adv. 2019, 1, 100002. [CrossRef]
  10. Cunningham, R.; Zhao, C.; Parab, N.; Kantzos, C.; Pauza, J.; Fezzaa, K.; Sun, T.; Rollett, A.D. Keyhole threshold and morphology
    in laser melting revealed by ultrahigh-speed x-ray imaging. Science 2019, 363, 849–852. [CrossRef] [PubMed]
  11. Tang, C.; Tan, J.L.; Wong, C.H. A numerical investigation on the physical mechanisms of single track defects in selective laser
    melting. Int. J. Heat Mass Transf. 2018, 126, 957–968. [CrossRef]
  12. Mirkoohi, E.; Ning, J.; Bocchini, P.; Fergani, O.; Chiang, K.-N.; Liang, S. Thermal Modeling of Temperature Distribution in Metal
    Additive Manufacturing Considering Effects of Build Layers, Latent Heat, and Temperature-Sensitivity of Material Properties. J.
    Manuf. Mater. Process. 2018, 2, 63. [CrossRef]
  13. Oane, M.; Sporea, D. Temperature profiles modeling in IR optical components during high power laser irradiation. Infrared Phys.
    Technol. 2001, 42, 31–40. [CrossRef]
  14. Cleary, P.W.; Sawley, M.L. DEM modelling of industrial granular flows: 3D case studies and the effect of particle shape on hopper
    discharge. Appl. Math. Model. 2002, 26, 89–111. [CrossRef]
  15. Parteli, E.J.R.; Pöschel, T. Particle-based simulation of powder application in additive manufacturing. Powder Technol. 2016, 288,
    96–102. [CrossRef]
  16. Cao, L. Numerical simulation of the impact of laying powder on selective laser melting single-pass formation. Int. J. Heat Mass
    Transf. 2019, 141, 1036–1048. [CrossRef]
  17. Tian, Y.; Yang, L.; Zhao, D.; Huang, Y.; Pan, J. Numerical analysis of powder bed generation and single track forming for selective
    laser melting of SS316L stainless steel. J. Manuf. Process. 2020, 58, 964–974. [CrossRef]
  18. Lee, Y.S.; Zhang, W. Modeling of heat transfer, fluid flow and solidification microstructure of nickel-base superalloy fabricated by
    laser powder bed fusion. Addit. Manuf. 2016, 12, 178–188. [CrossRef]
  19. Tang, M.; Pistorius, P.C.; Beuth, J.L. Prediction of lack-of-fusion porosity for powder bed fusion. Addit. Manuf. 2017, 14, 39–48.
    [CrossRef]
  20. Promoppatum, P.; Yao, S.C.; Pistorius, P.C.; Rollett, A.D. A Comprehensive Comparison of the Analytical and Numerical
    Prediction of the Thermal History and Solidification Microstructure of Inconel 718 Products Made by Laser Powder-Bed Fusion.
    Engineering 2017, 3, 685–694. [CrossRef]
  21. Rosenthal, D. Mathematical Theory of Heat Distribution During Welding and Cutting. Weld. J. 1941, 20, 220–234.
  22. Chen, Q.; Zhao, Y.Y.; Strayer, S.; Zhao, Y.Y.; Aoyagi, K.; Koizumi, Y.; Chiba, A.; Xiong, W.; To, A.C. Elucidating the Effect
    of Preheating Temperature on Melt Pool Morphology Variation in Inconel 718 Laser Powder Bed Fusion via Simulation and
    Experiment. Available online: https://www.sciencedirect.com/science/article/pii/S2214860420310149#bb8 (accessed on 30
    April 2021).
  23. Ur Rehman, A.; Pitir, F.; Salamci, M.U. Laser Powder Bed Fusion (LPBF) of In718 and the Impact of Pre-Heating at 500 and
    1000 ◦C: Operando Study. Materials 2021, 14, 6683. [CrossRef] [PubMed]
  24. Ur Rehman, A.; Pitir, F.; Salamci, M.U. Full-Field Mapping and Flow Quantification of Melt Pool Dynamics in Laser Powder Bed
    Fusion of SS316L. Materials 2021, 14, 6264. [CrossRef] [PubMed]
  25. Gong, H.; Gu, H.; Zeng, K.; Dilip, J.J.S.; Pal, D.; Stucker, B.; Christiansen, D.; Beuth, J.; Lewandowski, J.J. Melt Pool Characterization
    for Selective Laser Melting of Ti-6Al-4V Pre-alloyed Powder. In Proceedings of the International Solid Freeform Fabrication
    Symposium, Austin, TX, USA, 10–12 August 2014; 2014; pp. 256–267.
  26. Song, B.; Dong, S.; Liao, H.; Coddet, C. Process parameter selection for selective laser melting of Ti6Al4V based on temperature
    distribution simulation and experimental sintering. Int. J. Adv. Manuf. Technol. 2012, 61, 967–974. [CrossRef]
  27. Guo, Q.; Zhao, C.; Qu, M.; Xiong, L.; Hojjatzadeh, S.M.H.; Escano, L.I.; Parab, N.D.; Fezzaa, K.; Sun, T.; Chen, L. In-situ full-field
  28. mapping of melt flow dynamics in laser metal additive manufacturing. Addit. Manuf. 2020, 31, 100939. [CrossRef]
  29. Messler, J.R.W. Principles of Welding: Processes, Physics, Chemistry, and Metallurgy; John Wiley & Sons: New York, NY, USA, 2008;
  30. ISBN 9783527617494.
  31. Khairallah, S.A.; Anderson, A.T.; Rubenchik, A.M.; King, W.E. Laser powder-bed fusion additive manufacturing: Physics of
  32. complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater. 2016, 108, 36–45. [CrossRef]
  33. Ur Rehman, A.; Mahmood, M.A.; Pitir, F.; Salamci, M.U.; Popescu, A.C.; Mihailescu, I.N. Mesoscopic Computational Fluid
  34. Dynamics Modelling for the Laser-Melting Deposition of AISI 304 Stainless Steel Single Tracks with Experimental Correlation: A
  35. Novel Study. Metals 2021, 11, 1569. [CrossRef]
  36. Paul, A.; Debroy, T. Free surface flow and heat transfer in conduction mode laser welding. Metall. Trans. B 1988, 19, 851–858.
  37. [CrossRef]
  38. Aucott, L.; Dong, H.; Mirihanage, W.; Atwood, R.; Kidess, A.; Gao, S.; Wen, S.; Marsden, J.; Feng, S.; Tong, M.; et al. Revealing
  39. internal flow behaviour in arc welding and additive manufacturing of metals. Nat. Commun. 2018, 9, 5414. [CrossRef]
  40. Abderrazak, K.; Bannour, S.; Mhiri, H.; Lepalec, G.; Autric, M. Numerical and experimental study of molten pool formation
  41. during continuous laser welding of AZ91 magnesium alloy. Comput. Mater. Sci. 2009, 44, 858–866. [CrossRef]
  42. Bayat, M.; Thanki, A.; Mohanty, S.; Witvrouw, A.; Yang, S.; Thorborg, J.; Tiedje, N.S.; Hattel, J.H. Keyhole-induced porosities in
  43. Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation. Addit. Manuf. 2019,
  44. 30, 100835. [CrossRef]
Experimental and Numerical Investigation of Hydrodynamic Performance of a Sloping Floating Breakwater with and Without Chain-Net

Chain-Net이 있거나 없는 경사 부유식 방파제의 유체역학적 성능에 대한 실험 및 수치적 조사

Experimental and Numerical Investigation of Hydrodynamic Performance of a Sloping Floating Breakwater with and Without Chain-Net

Keywords

  • Sloping floating breakwater
  • Chain net
  • Anchorage system
  • Hydrodynamic performance

Abstract

두 개의 부유체 사이에 간격이 있는 경사진 부유식 방파제(FB)에 대한 새로운 연구가 제안되었습니다. 구조물의 기울기는 파동 에너지 소산을 유발할 수 있습니다. 경사진 구조물의 문제는 파도가 넘친다는 것입니다. 이 문제를 해결하기 위해 두 플로터 사이의 간격을 고려합니다. 

오버 토핑이 발생하면 마루를 통과하는 물이 두 플로터 사이의 틈으로 쏟아지며 결과적으로 파도 에너지가 감쇠됩니다. 체인 네트가 모델에 추가되고 전송 계수에 대한 영향이 연구됩니다. 또한, 구조물의 유체역학적 성능에 대한 자유도의 영향을 조사하기 위해 말뚝으로 고정된(1 자유도) 계류 라인으로 고정된(3도의 자유도) 두 가지 고정 시스템에서 자유 모델을 연구했습니다.

게다가, 실험은 5개의 다른 파도 주기와 4개의 다른 파도 높이를 가진 규칙파에서 수행됩니다. 실험 결과, 경사형 부유식 방파제가 직사각형 상자형보다 최대 15% 성능이 우수한 것으로 나타났다. 말뚝에 의해 고정된 FB에 대한 투과계수는 단파에서 케이블에 의해 고정된 FB보다 최대값으로 약 14% 낮고 장파에서 약 4-10% 더 높다. 흘수가 증가함에 따라 전송 계수는 감소하지만 건현은 허용 비율의 초과를 제한하기 위한 최소 요구 사항을 충족해야 합니다. 

체인 그물이 있는 모델은 없는 모델에 비해 전달 계수가 최대 14% 감소하여 더 나은 성능을 나타냅니다. 실험 결과, 경사형 부유식 방파제가 직사각형 상자형보다 최대 15% 성능이 우수한 것으로 나타났다. 말뚝에 의해 고정된 FB에 대한 투과계수는 단파에서 케이블에 의해 고정된 FB보다 최대값으로 약 14% 낮고 장파에서 약 4-10% 더 높다. 흘수가 증가함에 따라 전송 계수는 감소하지만 건현은 허용 비율의 초과를 제한하기 위한 최소 요구 사항을 충족해야 합니다. 

체인 그물이 있는 모델은 없는 모델에 비해 전달 계수가 최대 14% 감소하여 더 나은 성능을 나타냅니다. 실험 결과, 경사형 부유식 방파제가 직사각형 상자형보다 최대 15% 성능이 우수한 것으로 나타났다. 말뚝에 의해 고정된 FB에 대한 투과계수는 단파에서 케이블에 의해 고정된 FB보다 최대값으로 약 14% 낮고 장파에서 약 4-10% 더 높다. 흘수가 증가함에 따라 전송 계수는 감소하지만 건현은 허용 비율의 초과를 제한하기 위한 최소 요구 사항을 충족해야 합니다.

체인 그물이 있는 모델은 없는 모델에 비해 전달 계수가 최대 14% 감소하여 더 나은 성능을 나타냅니다. 말뚝에 의해 고정된 FB에 대한 투과계수는 단파에서 케이블에 의해 고정된 FB보다 최대값으로 약 14% 낮고 장파에서 약 4-10% 더 높다. 흘수가 증가함에 따라 전송 계수는 감소하지만 건현은 허용 비율의 초과를 제한하기 위한 최소 요구 사항을 충족해야 합니다. 

체인 그물이 있는 모델은 없는 모델에 비해 전달 계수가 최대 14% 감소하여 더 나은 성능을 나타냅니다. 말뚝에 의해 고정된 FB에 대한 투과계수는 단파에서 케이블에 의해 고정된 FB보다 최대값으로 약 14% 낮고 장파에서 약 4-10% 더 높다. 

흘수가 증가함에 따라 전송 계수는 감소하지만 건현은 허용 비율의 초과를 제한하기 위한 최소 요구 사항을 충족해야 합니다. 체인 그물이 있는 모델은 없는 모델에 비해 전달 계수가 최대 14% 감소하여 더 나은 성능을 나타냅니다.

A novel study of sloping floating breakwater (FB) that has a gap between two floaters is proposed. The slope of a structure can cause wave energy dissipation. A problem with sloping structures is wave overtopping. To solve this problem, a gap is considered between the two floaters. If overtopping occurs, water passing the crest will pour into the gap between the two floaters, as a result wave energy will be attenuated. A chain net is added to the model and its effect on the transmission coefficient is studied. Furthermore, in order to investigate the effects of the degree of freedom on the hydrodynamic performance of the structure, the model is studied in the two anchorage systems which are anchored by pile (1 degree of freedom) and anchored by mooring lines (3 degree of freedom). Moreover, the experiments are performed under regular waves with five different wave periods and four different wave heights. The results of the experiments show a sloping floating breakwater that has a better performance than that of rectangular box type by 15% as maximum value. The transmission coefficients for the FB anchored by pile are lower about 14% as maximum value than that of the FB anchored by cable in shorter waves and are higher about 4–10% in longer waves. With increasing the draft, the transmission coefficient decreases but the freeboard should meet the minimum requirements to restrict overtopping in the allowable rate. The model with a chain net exhibits a better performance as compared with the model without it by a maximum 14% reduction in the transmission coefficients.

  • Fig. 1extended data figure 1
  • Fig. 2extended data figure 2
  • Fig. 3extended data figure 3
  • Fig. 4extended data figure 4
  • Fig. 5extended data figure 5
  • Fig. 6extended data figure 6
  • Fig. 7extended data figure 7
  • Fig. 8extended data figure 8
  • Fig. 9extended data figure 9
  • Fig. 10extended data figure 10
  • Fig. 11extended data figure 11
  • Fig. 12extended data figure 12
  • Fig. 13extended data figure 13
  • Fig. 14extended data figure 14
  • Fig. 15extended data figure 15
  • Fig. 16extended data figure 16
  • Fig. 17extended data figure 17
  • Fig. 18extended data figure 18
  • Fig. 19extended data figure 19
  • Fig. 20extended data figure 20
  • Fig. 21extended data figure 21
  • Fig. 22extended data figure 22
  • Fig. 23extended data figure 23
  • Fig.24extended data figure 24
  • Fig. 25extended data figure 25
  • Fig. 26extended data figure 26
  • Fig. 27extended data figure 27

References

  1. Abul-Azm AG, Gesraha MR (2000) Approximation to the hydrodynamics of floating pontoons under oblique waves. Ocean Eng 27:365–384Article Google Scholar 
  2. Biesheuvel AC (2013) Effectiveness of floating breakwaters. Delf University of Technology, DissertaionGoogle Scholar 
  3. Chen Zh, Wang Y, Dong H, Zheng B (2012) Time-domain hydrodynamic analysis of pontoon-plate floating breakwater. J Water Sci Eng 5(3):291–303Google Scholar 
  4. Daneshfaraz R, Kaya B (2008) solution of the propagation of the waves in open channels by the transfer matrix method. J Ocean Eng 35:1075–1079Article Google Scholar 
  5. Daneshfaraz R, Sadeghfam S, Tahni A (2020) exprimental investigation of screen as energy dissipators in the movable-Bed channel. Iran J Sci Technol Trans Civil Eng 44:1237–1246Article Google Scholar 
  6. Deng Zh, Wang L, Zhao X, Huang Zh (2019) Hydrodynamic performance of a T-shaped floating breakwater. J Appl Ocean Res 82:325–336Article Google Scholar 
  7. Dong GH, Zheng YN, Li YC, Teng B, Guan CT, Lin DF (2008) Experiments on wave transmission coefficients of floating breakwaters. Ocean Eng 35:931–938Article Google Scholar 
  8. Duan WY, Xu SP, Xu QL et al (2017) Performance of an F-type floating break water: a numerical and experimental study. Proc I MechE Part M 231(2):583–599Google Scholar 
  9. Gesraha MR (2006) Analysis of π shaped floating breakwater in oblique waves: I. Impervious rigid wave boards. Appl Ocean Res 28:327–338Article Google Scholar 
  10. He F, Huang Zh, Wing-Keung Law A (2013) An experimental study of a floating breakwater with asymmetric pneumatic chambers for wave energy extraction. J Appl Energy 106:222–231Article Google Scholar 
  11. Ikeno M, Shimoda N, Iwata K (1988) A new type of breakwater utilizing air compressibility. In: Proceedings of the 21st Coastal Engineering Conference, ASCE. pp 2426–2339
  12. Ji Ch, Cheng Y, Cui J, Yuan Zh, Gaidai O (2018) Hydrodynamic performance of floating breakwaters in long wave regime: an experimental study. J Ocean Eng 152:154–166Article Google Scholar 
  13. Koutandos E, Prinos P, Gironella X (2005) Floating breakwaters under regular and irregular wave forcing: reflection and transmission characteristics. J Hydraul Res 43(2):174–188Article Google Scholar 
  14. Liu Zh, Wang Y, Wang W, Hua X (2019) Numerical modeling and optimization of a winged box-type floating breakwater by Smoothed Particle Hydrodynamics. J Ocean Eng 188:106246Article Google Scholar 
  15. LotfollahiYaghin MA, Mojtahedi A, Aminfar MH (2012) Physical model studies and system identification of hydrodynamics around a vertical square-section cylinder in irregular sea waves. J Ocean Eng 55:10–22Article Google Scholar 
  16. Mansard E, Funke E (1980) The measurement of the incident and reflected spectra using the least squares method. In: Proceedings of the 17th Coastal Engineering Conference ASCE, Sydney. pp 154–172
  17. Mojtahedi A, ShokatianBeiragh M, Farajpour I, Mohammadian M (2020) Investigation on hydrodynamic performance of an enviromentally friendly pile breakwater. J Ocean Eng 217:107942Article Google Scholar 
  18. Noroozi B, Bazargan J, Safarzadeh A (2021) Introducing the T-shaped weir: a new nonlinear weir. Water Supply. https://doi.org/10.2166/ws.2021.144Article Google Scholar 
  19. Pena E, Ferreras J, Sanchez-Tembleque F (2011) Experimental study on wave transmission coefficient, mooring lines and module connector forces with different designs of floating breakwaters. J Ocean Eng 38:1150–1160Article Google Scholar 
  20. Safarzadeh A, Zaji AH, Bonakdari H (2017) Comparative Assessment of the Hybrid Genetic Algorithm-Artificial neural network and genetic programming methods for the predicition of longitudinal velocity field around a single straight groyne. Appl Soft Comput 60:213–228Article Google Scholar 
  21. Tang HJ, Huang CC, Chen WM (2011) Dynamics of dual pontoon floating structure for cage aquaculture in a two-dimensional numerical wave tank. J Fluid Struct 27:918–936Article Google Scholar 
  22. U.S. Army coastal engineering research center (1984) Shore protection manual. U.S. Government Printing Office, WashingtonGoogle Scholar 
  23. Williams AN, Lee HS, Huang Z (2000) Floating pontoon breakwaters. Ocean Eng 27:221–240Article Google Scholar 
  24. Yang Zh, Xie M, Gao Zh, Xu T, Guo W, Ji X, Yuan Ch (2018) Experimental investigation on hydrodynamic effectiveness of a water ballast type floating breakwater. J Ocean Eng 167:77–94Article Google Scholar 
  25. Zhang X, Ma Sh, Duan W (2018) A new L type floating breakwater derived from vortex dissipation simulation. J Ocean Eng 164:455–464Article Google Scholar 
Figure 1 | Original Compound Broad Crested Weir Model (PVC cast).

복합 광대보의 방류계수 예측을 위한 실험적 해석과 CFD 해석의 비교연구

Comparative study of experimental and CFD analysis for predicting discharge coefficient of compound broad crested weir

ABSTRACT

Present study highlights the behavior of weir crest head and width parameter on the discharge coefficient of compound broad crested (CBC) weir. Computational fluid dynamics model (CFD) is validated with laboratory experimental investigations.

In the discharge analysis through broad crested weirs, the upstream head over the weir crest (h) is crucial, where the result is mainly dependent upon the weir crest length (L) in transverse direction to flow, water depth from channel bed. Currently, minimal investigations are known for CFD validations on compound broad crested weirs.

The hydraulic research for measuring discharge numerically is carried out using FLOW 3D software. The model applies renormalized group (RNG) using volume of fluid (VOF) method for improved accuracy in free surface simulations. Structured hexagonal meshes of cubic elements define discretized meshing.

The comparative analysis of the numerical simulations and experimental observations confirm the performance of CBC weir for precise measurement of a wide range of discharges. Series of CFD model studies and experimental validation have led to constant range of discharg coefficients for various head over weir crest. The correlation coefficient of discharge predictions is 0.999 with mean error of 0.28%.

현재 연구에서는 CBC(compound broad crested) 위어의 배출 계수에 대한 위어 볏 머리 및 너비 매개변수의 거동을 강조합니다. 전산 유체 역학 모델(CFD)은 실험실 실험 조사를 통해 검증되었습니다.

넓은 볏이 있는 둑을 통한 유출 분석에서 둑 마루의 상류 수두(h)가 중요합니다. 여기서 결과는 주로 흐름에 대한 횡 방향의 둑 마루 길이(L), 수로 바닥에서 수심에 따라 달라집니다. . 현재 복합 넓은 볏 둑에 대한 CFD 검증에 대해 최소한의 조사가 알려져 있습니다.

수압 연구는 FLOW 3D 소프트웨어를 사용하여 수치적으로 측정합니다. 이 모델은 자유 표면 시뮬레이션의 정확도 향상을 위해 VOF(유체 체적) 방법을 사용하여 RNG(재정규화 그룹)를 적용합니다. 정육면체 요소의 구조화된 육각형 메쉬는 이산화된 메쉬를 정의합니다.

수치 시뮬레이션과 실험적 관찰의 비교 분석을 통해 광범위한 배출의 정확한 측정을 위한 CBC 둑의 성능을 확인했습니다. 일련의 CFD 모델 연구와 실험적 검증을 통해 다양한 head over weir crest에 대한 일정한 범위의 방전 계수가 나타났습니다. 방전 예측의 상관 계수는 0.999이고 평균 오차는 0.28%입니다.

Figure 1 | Original Compound Broad Crested Weir Model (PVC cast).
Figure 1 | Original Compound Broad Crested Weir Model (PVC cast).
Figure 4 | CFD Simulation for max discharge (y2 ¼ 13.557 cm, Qmax ¼ 10 lps) and min discharge (y2 ¼ 6.56 cm, Qmin ¼ 2 lps).
Figure 4 | CFD Simulation for max discharge (y2 ¼ 13.557 cm, Qmax ¼ 10 lps) and min discharge (y2 ¼ 6.56 cm, Qmin ¼ 2 lps).
Figure 5 | (a, b) Velocity profiles corresponding to max discharge (10 lps) and min discharge (2 lps).
Figure 5 | (a, b) Velocity profiles corresponding to max discharge (10 lps) and min discharge (2 lps).
Table 8 | Range of Froude number, Reynold number and Weber number
Table 8 | Range of Froude number, Reynold number and Weber number

Key words

compound weir, flow 3D, flow measurement, numerical technique, open channel

HIGHLIGHTS

• The Head-Discharge relation is established for discharge measurement using compound broad crested weir, experimentally and numerically.
• Assessment of head over weir crest for different step widths of proposed weir on discharge coefficient is executed.
• Experimental and CFD results of weir performance demonstrate good agreement between the theoretical discharges by traditional rectangular weir formulae keeping Cd constant.

CONCLUSION

  1. The head discharge relationship established for compound rectangular broad crested weir for various discharge ranges was validated by CFD technique. A three dimensional simulation software FLOW 3D was used for this purpose.
  2. Original theoretical compound weir model depicts the relative average error between discharge predictions with Flow 3D simulation as 4.96% which is found less than the predictions made by graphical interpolation technique which is 5.33%.
  3. The standard deviation in Cd parameter for CFD simulation model is less i.e. 0.0146 as compared to experimental output of 0.0502.
  4. The correlation coefficient for physical and CFD studies for modified compound weir model is high, around 0.999 with
    error in discharge predictions being 0.28% as compared to the accuracy limits of about +3–5% stated in literature so far.
  5. Discharge coefficient by experimental and CFD approach is maintained constant and equal to design input value of 0.6.
    Thus, the proposed CBC weir can be operated for various discharge ranges by maintaining constant discharge coefficients.
    Good agreement between the theoretical, experimental and CFD simulation results for obtaining discharge through compound broad crested weir ascertains the fact that CFD model can be used as an effective tool towards modeling flow through compound broad crested weir.

REFERENCES

Abd El-Hady Rady, R. M. 2011 2D 3D modeling of flow over sharp crested weirs. Journal of Applied Sciences Research 7 (12), 2495–2505.
ISSN 1819-544X.
Ackers, P., White, W. R. & Harrison, A. J. M. 1978 Weirs and Flumes for Flow Measurement. Wiley, New York.
Aydin, M. C. 2016 Investigation of a sill effect on rectangular side-weir flow by using CFD. Journal of Irrigation and Drainage Engineering
142 (2), 04015043.
Azimi, A. H. & Rajaratnam, N. 2009 Discharge characteristics of weirs of finite crest length. Journal of Hydraulic Engineering 135 (12),
1081–1085.
Bijankhan, M., Di Stefano, C., Ferro, V. & Kouchakzadeh, S. 2014 New stage discharge relationship for weirs of finite crest length. Journal of
Irrigation and Drainage Engineering 140 (3), 06013006.
Boiten, W. & Pitlo, H. R. 1982 The V- shaped broad-crested weir. Journal of Irrigation and Drainage Engineering 108 (2), 142–160.
Bos, M. G. 1989 Discharge Measurement Structures, 3rd edn. International Institute for Land Reclamation and Improvement, Publication 20,
Wageningen, The Netherlands.
Gogus, M., Defne, Z. & Ozkandemir, V. 2006 Broad-crested weirs with rectangular compound cross sections. Journal of Irrigation and
Drainage Engineering 132 (3), 272–280.

Gogus, M., Al-Khatib, I. A., Atalay, A. E. & Khatib, J. I. 2016 Discharge prediction in flow measurement flumes with different downstream
transition slopes. Flow Measurement and Instrumentation 47, 28–34.
Hager, W. H. & Schwalt, M. 1994 Broad – crested weir. Journal of Irrigation and Drainage Engineering 120 (1), 13–25.
Harrison, A. J. M. 1967 The streamlined broad-crested weir. Proceedings of the Institution of Civil Engineers 38, 657–678.
Hinge, G. A., Balkrishna, S. & Khare, K. C. 2010 Improved design of stilling basin for deficient tail water. Journal of Basic and Applied
Scientific Research 1 (1), 31–40.
Hinge, G. A., Balkrishna, S. & Khare, K. C. 2011 Experimental and numerical study of compound broad crested weir. International Journal of
Fluids Engineering 3 (2), 197–202.
Horton, R. E. 1907 Weir Experiments, Coefficients, and Formulas. Dept. of the Interior, U.S. Geological Survey, Water-Supply and Irrigation
Paper 200. Government Printing Office, Washington, DC.
Khan, L. A., Wicklein, E. A. & Teixeira, E. C. 2006 Validation of a three-dimensional computational fluid dynamics model of a contact tank.
Journal of Hydraulic Engineering 132 (7), 741–746.
Kindsvater, C. E. & Carter, R. W. 1959 Discharge characteristics of rectangular thin-plate weirs. Paper No. 3001, Transactions, American
Society of Civil Engineers 124.
Kulin, G. & Compton, P. R. 1975 A Guide to Methods and Standards for the Measurement of Water Flow. Special Publication 421, National
Bureau of Standards.
Kulkarni, K. H. & Hinge, G. A. 2017 Compound broad crested weir for measurement of discharge – a novel approach. In: Proceedings
International Conference Organized by Indian Society of Hydraulics – ISH HYDRO, 21–23 Dec 2017, India, pp. 678–687.
Kulkarni, K. H. & Hinge, G. A. 2020 Experimental study for measuring discharge through compound broad crested weir. Flow Measurement
Instrumentation 75, 101803. ISSN 0955-5986.
Man, C., Zhang, G., Hong, V., Zhou, S. & Feng, Y. 2019 Assessment of turbulence models on bridge-pier scour using flow-3D. World Journal
of Engineering and Technology 7, 241–255. ISSN Online: 2331-4249.
Omer, B., Cihan, A. M., Emin, E. M. & Miller, C. J. 2018 Experimental and CFD analysis of circular labyrinth weirs. Journal of Irrigation and
Drainage Engineering 144 (6), 04018007.
RangaRaju, K. G. 1981 Flow Through Open Channels. McGraw-Hill, New York.
Roushangar, K., Nouri, A., Shahnazi, S. & Azamathulla, H. M. 2021 Towards design of compound channels with minimum overall cost
through grey wolf optimization algorithm. IWA – Journal of Hydroinformatics (In – press).
Safarzadeh, A. & Mohajeri, S. H. 2018 Hydrodynamics of rectangular broad-crested porous weir. Journal of Irrigation and Drainage
Engineering 144 (10), 04018028.
Salmasi, F., Poorescandar, S., Dalir, A. H. & Zadeh, D. F. 2012 Discharge relations for rectangular broad crested weirs. Journal of
Agricultural Sciences 17, 324–336.
Samadi, A. & Arvanaghi, H. 2014 CFD simulation of flow over contracted compound arched rectangular sharp crested weirs. International
Journal of Optimization in Civil Engineering 4 (4), 549–560.
Savage, B. M. & Johnson, M. C. 2001 Flow over ogee spillway: physical and numerical model case study. Journal of Hydraulic Engineering
127 (8), 640–649.
Swamee, P. K. 1988 Generalized rectangular weir equations. Journal of Hydraulic Engineering 945–952. doi:10.1061/(ASCE),0733-9429
114:8(945).
The United States Bureau of Reclamation (USBR) 2001 Water Measurement Manual, Chapter 7 – Weirs. U.S. Government Printing Office,
Washington, DC, p. 20402. Available from: http://www/usbr.gov/pmts/hydraulics_lab/pubs/wmm.
Zahiri, A. & Azamathulla, H. M. 2014 Comparison between linear genetic programming and M5 tree models to predict flow discharge in
compound channels. Neural Computing and Application 24, 413–420.

Figure 15. Localized deformations on revetment due to run-down and sliding of armor from body laboratory model (left) and numerical modeling (right).

지속 가능한 해안 보호 구조로서 굴절식 콘크리트 블록 매트리스의 손상 메커니즘의 수치적 모델링

Numerical Modeling of Failure Mechanisms in Articulated Concrete Block Mattress as a Sustainable Coastal Protection Structure

Author

Ramin Safari Ghaleh(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

Omid Aminoroayaie Yamini(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

S. Hooman Mousavi(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

Mohammad Reza Kavianpour(Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran)

Abstract

해안선 보호는 전 세계적인 우선 순위로 남아 있습니다. 일반적으로 해안 지역은 석회암과 같은 단단하고 비자연적이며 지속 불가능한 재료로 보호됩니다. 시공 속도와 환경 친화성을 높이고 개별 콘크리트 블록 및 보강재의 중량을 줄이기 위해 콘크리트 블록을 ACB 매트(Articulated Concrete Block Mattress)로 설계 및 구현할 수 있습니다. 이 구조물은 필수적인 부분으로 작용하며 방파제 또는 해안선 보호의 둑으로 사용할 수 있습니다. 물리적 모델은 해안 구조물의 현상을 추정하고 조사하는 핵심 도구 중 하나입니다. 그러나 한계와 장애물이 있습니다. 결과적으로, 본 연구에서는 이러한 구조물에 대한 파도의 수치 모델링을 활용하여 방파제에서의 파도 전파를 시뮬레이션하고, VOF가 있는 Flow-3D 소프트웨어를 통해 ACB Mat의 불안정성에 영향을 미치는 요인으로는 파괴파동, 옹벽의 흔들림, 파손으로 인한 인양력으로 인한 장갑의 변위 등이 있다. 본 연구의 가장 중요한 목적은 수치 Flow-3D 모델이 연안 호안의 유체역학적 매개변수를 모사하는 능력을 조사하는 것입니다. 콘크리트 블록 장갑에 대한 파동의 상승 값은 파단 매개변수( 0.5 < ξ m – 1 , 0 < 3.3 )가 증가할 때까지(R u 2 % H m 0 = 1.6) ) 최대값에 도달합니다. 따라서 차단파라미터를 증가시키고 파괴파(ξ m − 1 , 0 > 3.3 ) 유형을 붕괴파/해일파로 변경함으로써 콘크리트 블록 호안의 상대파 상승 변화 경향이 점차 증가합니다. 파동(0.5 < ξ m − 1 , 0 < 3.3 )의 경우 차단기 지수(표면 유사성 매개변수)를 높이면 상대파 런다운의 낮은 값이 크게 감소합니다. 또한, 천이영역에서는 파단파동이 쇄도파에서 붕괴/서징으로의 변화( 3.3 < ξ m – 1 , 0 < 5.0 )에서 상대적 런다운 과정이 더 적은 강도로 발생합니다.

Shoreline protection remains a global priority. Typically, coastal areas are protected by armoring them with hard, non-native, and non-sustainable materials such as limestone. To increase the execution speed and environmental friendliness and reduce the weight of individual concrete blocks and reinforcements, concrete blocks can be designed and implemented as Articulated Concrete Block Mattress (ACB Mat). These structures act as an integral part and can be used as a revetment on the breakwater body or shoreline protection. Physical models are one of the key tools for estimating and investigating the phenomena in coastal structures. However, it does have limitations and obstacles; consequently, in this study, numerical modeling of waves on these structures has been utilized to simulate wave propagation on the breakwater, via Flow-3D software with VOF. Among the factors affecting the instability of ACB Mat are breaking waves as well as the shaking of the revetment and the displacement of the armor due to the uplift force resulting from the failure. The most important purpose of the present study is to investigate the ability of numerical Flow-3D model to simulate hydrodynamic parameters in coastal revetment. The run-up values of the waves on the concrete block armoring will multiply with increasing break parameter ( 0.5 < ξ m − 1 , 0 < 3.3 ) due to the existence of plunging waves until it ( R u 2 % H m 0 = 1.6 ) reaches maximum. Hence, by increasing the breaker parameter and changing breaking waves ( ξ m − 1 , 0 > 3.3 ) type to collapsing waves/surging waves, the trend of relative wave run-up changes on concrete block revetment increases gradually. By increasing the breaker index (surf similarity parameter) in the case of plunging waves ( 0.5 < ξ m − 1 , 0 < 3.3 ), the low values on the relative wave run-down are greatly reduced. Additionally, in the transition region, the change of breaking waves from plunging waves to collapsing/surging ( 3.3 < ξ m − 1 , 0 < 5.0 ), the relative run-down process occurs with less intensity.

Figure 1.  Armor  geometric  characteristics  and  drawing  three-dimensional  geometry  of  a  breakwater section  in SolidWorks software.
Figure 1. Armor geometric characteristics and drawing three-dimensional geometry of a breakwater section in SolidWorks software.
Figure  5.  Wave  overtopping on  concrete block  mattress in (a)  laboratory  and (b)  numerical  model.
Figure 5. Wave overtopping on concrete block mattress in (a) laboratory and (b) numerical model.
Figure  7.  Mesh  block  for  calibrated  numerical  model  with  686,625  cells  and  utilization  of  FAVOR  tab to assess figure geometry.
Figure 7. Mesh block for calibrated numerical model with 686,625 cells and utilization of FAVOR tab to assess figure geometry.
Figure  10.  How to place different layers  (core, filter,  and revetment)  of the structure on slope.
Figure 10. How to place different layers (core, filter, and revetment) of the structure on slope.

Suggested Citation

Figure 11. Wave run-up on ACB Mat blocks in (a) laboratory model and (b) numerical modeling.
Figure 11. Wave run-up on ACB Mat blocks in (a) laboratory model and (b) numerical modeling.
Figure  15.  Localized  deformations  on  revetment  due  to  run-down  and  sliding  of  armor  from  body  laboratory  model  (left) and  numerical  modeling (right).
Figure 15. Localized deformations on revetment due to run-down and sliding of armor from body laboratory model (left) and numerical modeling (right).

References

  1. Capobianco, V.; Robinson, K.; Kalsnes, B.; Ekeheien, C.; Høydal, Ø. Hydro-Mechanical Effects of Several Riparian Vegetation Combinations on the Streambank Stability—A Benchmark Case in Southeastern Norway. Sustainability 2021, 13, 4046. [CrossRef]
  2. MarCom Working Group 113. PIANC Report No 113: The Application of Geosynthetics in Waterfront Areas; PIANC: Brussels, Belgium, 2011; p. 113, ISBN 978-2-87223-188-1.
  3. Hunt, W.F.; Collins, K.A.; Hathaway, J.M. Hydrologic and Water Quality Evaluation of Four Permeable Pavements in North Carolina, USA. In Proceedings of the 9th International Conference on Concrete Block Paving, Buenos Aires, Argentina, 18–21 October 2009.
  4. Kirkpatrick, R.; Campbell, R.; Smyth, J.; Murtagh, J.; Knapton, J. Improvement of Water Quality by Coarse Graded Aggregates in Permeable Pavements. In Proceedings of the 9th International Conference on Concrete Block Paving, Buenos Aires, Argentina, 18–21 October 2009.
  5. Chinowsky, P.; Helman, J. Protecting Infrastructure and Public Buildings against Sea Level Rise and Storm Surge. Sustainability 2021, 13, 10538. [CrossRef]
  6. Breteler, M.K.; Pilarczyk, K.W.; Stoutjesdijk, T. Design of alternative revetments. Coast. Eng. 1998 1999, 1587–1600. [CrossRef]
  7. Pilarczyk, K.W. Design of Revetments; Dutch Public Works Department (Rws), Hydraulic Engineering Division: Delft, The Netherlands, 2003.
  8. Hughes, S.A. Combined Wave and Surge Overtopping of Levees: Flow Hydrodynamics and Articulated Concrete Mat Stability; Engineer Research and Development Center Vicksburg Ms Coastal and Hydraulics Lab: Vicksburg, MS, USA, 2008.
  9. Gier, F.; Schüttrumpf, H.; Mönnich, J.; Van Der Meer, J.; Kudella, M.; Rubin, H. Stability of Interlocked Pattern Placed Block Revetments. Coast. Eng. Proc. 2012, 1, Structures-46. [CrossRef]
  10. Najafi, J.A.; Monshizadeh, M. Laboratory Investigations on Wave Run-up and Transmission over Breakwaters Covered by Antifer Units; Scientia Iranica: Tehran, Iran, 2010.
  11. Oumeraci, H.; Staal, T.; Pförtner, S.; Ludwigs, G.; Kudella, M. Hydraulic Performance, Wave Loading and Response of Elastocoast Revetments and their Foundation—A Large Scale Model Study; Leichtweiß Institut für Wasserbau: Braunschweig, Germany, 2010.
  12. Tripathy, S.K. Significance of Traditional and Advanced Morphometry to Fishery Science. J. Hum. Earth Future 2020, 1, 153–166. [CrossRef]
  13. Nut, N.; Mihara, M.; Jeong, J.; Ngo, B.; Sigua, G.; Prasad, P.V.V.; Reyes, M.R. Land Use and Land Cover Changes and Its Impact on Soil Erosion in Stung Sangkae Catchment of Cambodia. Sustainability 2021, 13, 9276. [CrossRef]
  14. Xu, C.; Pu, L.; Kong, F.; Li, B. Spatio-Temporal Change of Land Use in a Coastal Reclamation Area: A Complex Network Approach. Sustainability 2021, 13, 8690. [CrossRef]
  15. Mousavi, S.; Kavianpour, H.M.R.; Yamini, O.A. Experimental analysis of breakwater stability with antifer concrete block. Mar. Georesour. Geotechnol. 2017, 35, 426–434. [CrossRef]
  16. Yamini, O.; Aminoroayaie, S.; Mousavi, H.; Kavianpour, M.R. Experimental Investigation of Using Geo-Textile Filter Layer In Articulated Concrete Block Mattress Revetment On Coastal Embankment. J. Ocean Eng. Mar. Energy 2019, 5, 119–133. [CrossRef]
  17. Ghasemi, A.; Far, M.S.; Panahi, R. Numerical Simulation of Wave Overtopping From Armour Breakwater by Considering Porous Effect. J. Mar. Eng. 2015, 11, 51–60. Available online: http://dorl.net/dor/20.1001.1.17357608.1394.11.22.8.4 (accessed on 21 October 2021).
  18. Nourani, O.; Askar, M.B. Comparison of the Effect of Tetrapod Block and Armor X block on Reducing Wave Overtopping in Breakwaters. Open J. Mar. Sci. 2017, 7, 472–484. [CrossRef]
  19. Aminoroaya, A.O.; Kavianpour, M.R.; Movahedi, A. Performance of Hydrodynamics Flow on Flip Buckets Spillway for Flood Control in Large Dam Reservoirs. J. Hum. Earth Future 2020, 1, 39–47.
  20. Milanian, F.; Niri, M.Z.; Najafi-Jilani, A. Effect of hydraulic and structural parameters on the wave run-up over the berm breakwaters. Int. J. Nav. Archit. Ocean Eng. 2017, 9, 282–291. [CrossRef]
  21. Yamini, O.A.; Kavianpour, M.R.; Mousavi, S.H. Experimental investigation of parameters affecting the stability of articulated concrete block mattress under wave attack. Appl. Ocean Res. 2017, 64, 184–202. [CrossRef]
  22. Yakhot, V.; Orszag, S.A.; Thangam, S.; Gatski, T.B.; Speziale, C.G. Development of turbulence models for shear flows by a double expansion technique. Phys. Fluids 1992, 4, 1510–1520. [CrossRef]
  23. Bayon, A.; Valero, D.; García-Bartual, R.; López-Jiménez, P.A. Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environ. Model. Softw. 2016, 80, 322–335. [CrossRef]
  24. Jin, J.; Meng, B. Computation of wave loads on the superstructures of coastal highway bridges. Ocean Eng. 2011, 38, 2185–2200. [CrossRef]
  25. Yang, S.; Yang, W.; Qin, S.; Li, Q.; Yang, B. Numerical study on characteristics of dam-break wave. Ocean Eng. 2018, 159, 358–371. [CrossRef]
  26. Ersoy, H.; Karahan, M.; Geli¸sli, K.; Akgün, A.; Anılan, T.; Sünnetci, M.O.; Yah¸si, B.K. Modelling of the landslide-induced impulse waves in the Artvin Dam reservoir by empirical approach and 3D numerical simulation. Eng. Geol. 2019, 249, 112–128. [CrossRef]
  27. Zhan, J.M.; Dong, Z.; Jiang, W.; Li, Y.S. Numerical simulation of wave transformation and runup incorporating porous media wave absorber and turbulence models. Ocean Eng. 2010, 37, 1261–1272. [CrossRef]
  28. Owen, M.W. The Hydroulic Design of Seawall Profiles, Proceedings Conference on Shoreline Protection; ICE: London, UK, 1980; pp. 185–192.
  29. Pilarczyk, K.W. Geosythetics and Geosystems in Hydraulic and Coastal Engineering; CRC Press: Balkema, FL, USA, 2000; p. 913, ISBN 90.5809.302.6.
  30. Van der Meer, J.W.; Allsop, N.W.H.; Bruce, T.; De Rouck, J.; Kortenhaus, A.; Pullen, T.; Schüttrumpf, H.; Troch, P.; Zanuttigh, B. (Eds.) Manual on Wave Overtopping of Sea Defences and Related Structures–Assessment Manual; EurOtop.: London, UK, 2016; Available online: www.Overtopping-manual.com (accessed on 21 October 2021).
  31. Battjes, J.A. Computation of Set-up, Longshore Currents, Run-up and Overtopping Due to Wind-Generated Waves; TU Delft Library: Delft, The Netherlands, 1974.
  32. Van der Meer, J.W. Rock Slopes and Gravel Beaches under Wave Attack; Delft Hydraulics: Delft, The Netherlands, 1988.
  33. Ten Oever, E. Theoretical and Experimental Study on the Placement of Xbloc; Delft Hydraulics: Delft, The Netherlands, 2006.
  34. Flow Science, Inc. FLOW-3D User Manual Version 9.3; Flow Science, Inc.: Santa Fe, NM, USA, 2008.
  35. Lebaron, J.W. Stability of A-Jacksarmored Rubble-Mound Break Waters Subjected to Breaking and Non-Breaking Waves with No Overtopping; Master of Science in Civil Engineering, Oregon State University: Corvallis, OR, USA, 1999.
  36. McLaren RW, G.; Chin, C.; Weber, J.; Binns, J.; McInerney, J.; Allen, M. Articulated Concrete Mattress block size stability comparison in omni-directional current. In Proceedings of the OCEANS 2016 MTS/IEEE Monterey, Monterey, CA, USA, 19–23 September 2016; pp. 1–6. [CrossRef]
Wave Loads Assessment on Coastal Structures at Inundation Risk Using CFD Modelling

CFD 모델링을 사용하여 침수 위험이 있는 해안 구조물에 대한 파랑 하중 평가

Wave Loads Assessment on Coastal Structures at Inundation Risk Using CFD Modellin

Ana GomesJosé Pinho

Conference paperFirst Online: 19 November 2021

지난 수십 년 동안 극한 현상은 심각성과 주민, 기반 시설 및 인류 활동에 대한 위험 증가로 인해 우려를 불러일으켰습니다. 오늘날 해안 구조물이 범람하고 해변 침식 및 기반 시설 파괴가 전 세계 해안에서 흔히 발생합니다. 

완화에 효율적으로 기여하고 효율적인 방어 조치를 채택하려면 이러한 영향을 예상하는 것이 매우 중요합니다. 대규모 물리적 모델을 기반으로 하는 이전 실험 작업에서 목조 교각 상단의 고가 해안 구조물의 공극과 그에 따른 수평 및 수직 파도력 사이의 관계가 다양한 파도 하중 조건에 대해 연구되었습니다. 

이러한 실험 결과는 CFD 도구를 사용하여 유체/구조 상호 작용을 시뮬레이션하기 위한 수치 모델에 대한 보정 데이터 역할을 합니다. 주어진 파도 조건에 대해 물과 구조물 베이스 레벨 사이의 공극 높이를 다르게 하여 세 가지 시나리오를 시뮬레이션했습니다. 

수치 결과를 물리적 모델 결과와 비교하면 수치적으로 구한 수평력과 수직력의 최대값은 각각 평균 ​​14.4%와 25.4%의 상대차로 만족할 만합니다. 또한 구조물을 지지하는 교각에 작용하는 압력과 전단응력을 시뮬레이션하기 위해 실제 수치모델을 적용하였으며, 서로 다른 공극의 높이를 고려하고 각각의 CPU 시뮬레이션 시간을 평가하였습니다. 

이러한 방식으로 CFD 모델의 운영 모델링 기능을 평가하여 조기 경보 시스템 내에서 최종 사용에 대한 예측 선행 시간 제한을 결정했습니다.

키워드

Coastal risk, Elevated coastal structure, Numerical simulation, Flow-3D® , 해안 위험, 높은 해안 구조, 수치 시뮬레이션

References

  1. 1.Neumann B, Vafeidis AT, Zimmermann J, Nicholls RJ (2015) Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment. PloS one, n. 10(3), p. X-XGoogle Scholar
  2. 2.Jones B, O’Neill BC (2016) Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environmental Research Letters, N. 11(8):1–10Google Scholar
  3. 3.Talbot J (2005) Repairing Florida’s Escambia Bay Bridge. Associated Construction Publications, available online at http://www.acppubs.com/article/CA511040
  4. 4.Kennedy A, Rogers S, Sallenger A, Gravois U, Zachry B, Dosa M, Zarama F (2011a) Building destruction from wave and surge on the bolivar peninsula during hurricane Ike. J. Waterw. Port, Coast. Ocean Eng. 137 (3), 132–141Google Scholar
  5. 5.Tomiczek T, Kennedy A, Rogers S (2014) Collapse limit state fragilities of woodframed residences from storm surge and waves during hurricane Ike. J. Waterw. Port, Coast. Ocean Eng. 140 (1), 43–55Google Scholar
  6. 6.Dentale F, Donnarumma G, Pugliese Carratelli E (2014a) Simulation of flow within armour blocks in a breakwater. J Coast Res 30(3):528–536CrossRefGoogle Scholar
  7. 7.Peregrine DH (2003) Water wave impact on walls. Annu Rev Fluid Mech 35:23–43CrossRefGoogle Scholar
  8. 8.Cuomo G, Piscopia R, Allsop W (2011) Evaluation of wave impact loads on caisson breakwaters based on joint probability of impact maxima and rise times. Coast Eng 58(1):9–27CrossRefGoogle Scholar
  9. 9.Faltinsen OM, Landrini M, Greco M (2004) Slamming in marine applications. J Eng Math 48(3–4):187–217CrossRefGoogle Scholar
  10. 10.Peregrine DH. et al (2005) Violent water wave impact on a wall. In: Proceedings of 14th Aha Huliko Winter Workshop, Honolulu, HawaiiGoogle Scholar
  11. 11.Cuomo G, Tirindelli M, Allsop W (2007) Wave in deck loads on exposed jetties. Coast Eng 54(9):657–679CrossRefGoogle Scholar
  12. 12.Azadbakht M, Yim SC (2015) Simulation and estimation of tsunami loads on bridge superstructures. J Waterw Port Coast Ocean Eng 141(2):20CrossRefGoogle Scholar
  13. 13.Wiebe DM, Park H, Cox DT (2014) Application of the Goda pressure formulae for horizontal wave loads on elevated structures. KSCE J. Civ. EngGoogle Scholar
  14. 14.Hayatdavoodi M, Seiffert B, Ertekin RC (2015) Experiments and calculations of cnoidal wave loads on a flat plate in shallow-water. J. Ocean Eng. Mar. Energy 1(1):77–99CrossRefGoogle Scholar
  15. 15.Wei Z, Dalrymple RA (2016) Numerical study on mitigating tsunami force on bridges by an SPH model. J. Ocean. Eng. Mar. Energy 2(365):365–380CrossRefGoogle Scholar
  16. 16.Bradner, C., Schumacher, T., Cox, D., Higgins, C.: Experimental Setup for a largescale bridge superstructure model subjected to waves. J. Waterw. Port, Coast. Ocean Eng. 137 (1), 3–11 (2011)Google Scholar
  17. 17.Xiao H, Huang W (2008) Numerical modeling of wave runup and forces on an idealized beachfront house. Ocean Eng 35(1):106–116CrossRefGoogle Scholar
  18. 18.Do T, van de Lindt JW, Cox D (2016) Performance-based design methodology for inundated elevated coastal structures subjected to wave load. Eng Struct 117:250–262CrossRefGoogle Scholar
  19. 19.Lara JL, Garcia N, Losada IJ (2006) RANS modeling applied to random wave interaction with submerged permeable structures. Coastal Eng 53(5–6):395–417CrossRefGoogle Scholar
  20. 20.Meringolo DD, Aristodemo F, Veltri P (2015) SPH numerical modeling of wave–perforated breakwater interaction. Coast Eng 101:48–68CrossRefGoogle Scholar
  21. 21.Al-Banaa K, Liu PLF (2007) Numerical study on the hydraulic performance of submerged porous breakwater under solitary wave attack. J Coast Res 50:201–205Google Scholar
  22. 22.Gomes, A., Pinho, J.L.S., Valente, T., Antunes do Carmo, J.S., V. Hegde, A.: Performance Assessment of a Semi-Circular Breakwater through CFD Modelling. J. Mar. Sci. Eng. 2020, 8, 226 (2020).Google Scholar
  23. 23.Flow Sciences Inc. Flow-3D User Manual, release 9.4, Santa Fe, NM, USA (2009).Google Scholar
  24. 24.Smith, H., Foster., D.L.: Modeling of flow around a cylinder over a scoured bed. J. Waterw., Port, Coastal, Ocean Eng.131(1),14–24 (2005).Google Scholar
  25. 25.Richardson JE, Panchang VG (1998) Three-dimensional simulation of scour-inducing flow at bridge piers. J Hydraul Eng 124(5):530–540CrossRefGoogle Scholar
  26. 26.Jin J, Meng B (2011) Computation of wave loads on the superstructures of coastal highway bridges. Ocean Eng 38(17–18):2185–2200CrossRefGoogle Scholar
  27. 27.Dentale F, Donnarumma G, Pugliese Carratelli E (2014b) Numerical wave interaction with tetrapods breakwater. Int. J. Nav. Arch. Ocean 6:13Google Scholar
  28. 28.Carratelli EP, Viccione G, Bovolin V (2016) Free surface flow impact on a vertical wall: a numerical assessment. Theor. Comput. Fluid Mech. 30(5):403–414CrossRefGoogle Scholar
  29. 29.Cavallaro, L., Dentale, F., Donnarumma, G., Foti, E., Musumeci, R.E., Pugliese Carratelli, E.: Rubble mound breakwater overtopping: estimation of the reliability of a 3D numerical simulation, In: ICCE 2012, Interntional Conference on Coastal Engineering, Santander, Spain (2012).Google Scholar
  30. 30.Vanneste, D., Suzuki, T., Altomare, C.: Comparison of numerical models for wave overtoping and impact on storm return walls. In: ICCE 2014, International Conference on Coastal Engineering, Seoul, Korea (2014).Google Scholar
  31. 31.Park H, Tomiczek T, Cox DT, van de Lindt JW, Lomonaco P (2017) Experimental modeling of horizontal and vertical wave forces on an elevated coastal structure. Coast Eng 128:58–74CrossRefGoogle Scholar
  32. 32.Isfahani AHG, Brethour JM (2009) On the Implementation of Two-Equation Turbulence Models in FLOW-3D; FSI-09-TN86; Flow Science: Santa Fe. NM, USAGoogle Scholar
  33. 33.Novais-Barbosa J (1985) Mecânica dos Fluidos e Hidráulica Geral Vol 1 e II Porto Editora, PortoGoogle Scholar
  34. 34.Le Méhauté B (1976) An Introduction to Hydrodynamics and Water Waves. Springer, Berlin/Heidelberg, GermanyCrossRefGoogle Scholar
Figure 1- The experimental model [17]

와류형 우수 저류지의 수치 모델링에 대한 난류 슈미트 수의 영향 조사

Investigation of the Turbulent Schmidt Number Effects On Numerical Modelling Of Vortex-Type Stormwater Retention Ponds

S. M. Yamini1; H. Shamloo2; S. H. Ghafari3
1M.Eng., Dep. of Civil Engineering K.N. Toosi University of Technology, Valiasr St., Tehran, Iran.
smyamini@alumni.kntu.ac.ir
2Associate Professor, Dep. of Civil Engineering K.N. Toosi University of Technology, Valiasr St., Tehran, Iran.
hshamloo@kntu.ac.ir
3Ph.D., Dep. of Civil Engineering Univ. of Tehran, Enqelab St., Tehran, Iran. sarvenazghafari@ut.ac.ir

Abstract

정확하고 신뢰할 수 있는 CFD 모델링 결과를 얻는 것은 이러한 시뮬레이션에서 입력의 중요성 때문에 종종 정밀 조사의 대상입니다.

난류 모델링이 RANS(Reynolds-Averaged Navier-Stokes) 방정식을 기반으로 하는 경우 난류 스칼라 전송을 추정하려면 난류 흐름에서 질량 1에 대한 운동량 확산의 비율로 정의되는 난류 슈미트 수(Sct)의 정의가 필요합니다.

그러나 이 매개변수는 난류 흐름의 속성이므로 보편적인 값이 허용되지 않았습니다. 우수 저류지의 수치 연구에서 적절한 Sct를 설정하는 실제 역할은 수력 효율의 평가가 추적자 테스트의 출력 질량 농도를 기반으로 하기 때문에 가장 중요합니다.

본 연구에서는 FLOW-3D를 사용하여 와류형 우수 저류지의 여러 수치 시뮬레이션을 체계적으로 수행했습니다. 다양한 난류 슈미트 수의 범위는 메쉬 감도를 조사하기 위해 다른 수의 계산 셀에 의해 수행된 수치 시뮬레이션에 도입되었습니다.

또한 사용자 정의 또는 자동 계산 값으로 최대 난류 혼합 길이의 영향을 평가했습니다. 이 연구의 결과는 실험 결과와 밀접한 일치를 제공하는 Sct= 0.625와 함께 수리학적 직경의 7%와 동일한 최대 난류 혼합 길이의 일정한 값을 갖는 확립된 수치 모델입니다.

특히 수치적 무차원 RDT 곡선의 피크 값은 극적으로 감소하여 실험 결과와 거의 일치했습니다. 이것은 FLOW-3D가 난류 유동의 와류형 물리학에서 질량 확산도를 적절하게 예측하는 상당한 능력을 가지고 있다는 결론을 내립니다.

– Achieving accurate and reliable CFD modelling results often is the subject of scrutiny because of the importance of the inputs in those simulations. If turbulence modelling is based on Reynolds-Averaged Navier-Stokes (RANS) equations, estimating the turbulent scalar transport requires the definition of the turbulent Schmidt number (Sct), defined as the ratio of momentum diffusivity to mass one in a turbulent flow. However, no universal value has been accepted for this parameter as it is a property of turbulent flows.

The practical role of establishing a suitable Sct in numerical studies of stormwater retention ponds is of the utmost importance because the assessment of the hydraulic efficiency of them is based on output mass concentration of tracer tests. In this study, several numerical simulations of a vortex-type stormwater retention pond were systematically carried out using FLOW-3D. A range of various turbulent Schmidt numbers were introduced in numerical simulations performed by different number of computational cells to investigate mesh sensitivity.

Moreover, the effects of maximum turbulent mixing length as a user-defined or automatically computed value were assessed. The outcome of this study is an established numerical model with a constant value of maximum turbulent mixing length equal to 7% of the hydraulic diameter along with Sct= 0.625 which provides a close agreement with experimental results.

Noticeably, the peak values of numerical dimensionless RDT curves are dramatically decreased, resulted in a close match with experimental results. This concludes that FLOW-3D has a considerable ability to appropriately predict mass diffusivity in vortex-type physics of turbulent flows.

Keywords:

turbulent Schmidt number – maximum turbulent mixing length – CFD – mesh sensitivity – vortex-type
stormwater retention pond – environmental fluid mechanics

Figure 1- The experimental model [17]
Figure 1- The experimental model [17]
Figure 2- Schematic of boundary conditions in the numerical model
Figure 2- Schematic of boundary conditions in the numerical model
Figure 3- Positioning of mesh blocks
Figure 3- Positioning of mesh blocks

References

[1] C. Gualtieri, A. Angeloudis, F. Bombardelli, S. Jha, and T. Stoesser, “On the Values for the Turbulent Schmidt Number
in Environmental Flows,” Fluids, vol. 2, p. 17, 2017.
[2] Å. Adamsson, L. Bergdahl, and S. Lyngfelt, “Measurement and three-dimensional simulation of flow in a rectangular
detention tank,” Urban Water Journal, vol. 2, no. 4, pp. 277-287, 2005/12/01 2005, doi: 10.1080/15730620500386545.
[3] C. Gualtieri, “Numerical simulation of flow and tracer transport in a disinfection contact tank,” 2006.
[4] S. Khan, B. Melville, and A. Shamseldin, Modeling the Layouts of Stormwater Retention Ponds using Residence Time.
2009, pp. 77-83.
[5] F. Martínez-Solano, P. L. I. Rey, C. Gualtieri, and P. López-Jiménez, “Modelling flow and concentration field in
rectangular water tanks,” 2010.
[6] W. B. Rauen, A. Angeloudis, and R. A. Falconer, “Appraisal of chlorine contact tank modelling practices,” Water
Research, vol. 46, no. 18, pp. 5834-5847, 2012/11/15/ 2012, doi: https://doi.org/10.1016/j.watres.2012.08.013.

[7] J. Zhang, A. Tejada-Martínez, and Q. Zhang, “Evaluation of LES and RANS for Determining Hydraulic Performance
of Disinfection Systems for Water Treatment,” Journal of Fluids Engineering, vol. 136, 05/15 2014, doi:
10.1115/1.4027652.
[8] J. Zhang, A. E. Tejada-Martínez, and Q. Zhang, “Developments in computational fluid dynamics-based modeling for
disinfection technologies over the last two decades: A review,” Environmental Modelling & Software, vol. 58, pp. 71-
85, 2014/08/01/ 2014, doi: https://doi.org/10.1016/j.envsoft.2014.04.003.
[9] C. Gualtieri and F. Salzano, “DIscussion on “The effect of baffle spacing on hydrodynamics and solute transport in
serpentine contact tanks”,” Journal of Hydraulic Research, vol. 52, pp. 152-154, 02/28 2014, doi:
10.1080/00221686.2013.877528.
[10] A. Angeloudis, T. Stoesser, R. A. Falconer, and D. Kim, “Flow, transport and disinfection performance in small- and
full-scale contact tanks,” Journal of Hydro-environment Research, vol. 9, no. 1, pp. 15-27, 2015/03/01/ 2015, doi:
https://doi.org/10.1016/j.jher.2014.07.001.
[11] A. Angeloudis, T. Stoesser, C. Gualtieri, and R. A. Falconer, “Contact Tank Design Impact on Process Performance,”
Environmental Modeling & Assessment, vol. 21, no. 5, pp. 563-576, 2016/10/01 2016, doi: 10.1007/s10666-016-9502-
x.
[12] D. Valero and D. B. Bung, “Sensitivity of turbulent Schmidt number and turbulence model to simulations of jets in
crossflow,” Environmental Modelling & Software, vol. 82, pp. 218-228, 2016/08/01/ 2016, doi:
https://doi.org/10.1016/j.envsoft.2016.04.030.
[13] F. Sonnenwald, I. Guymer, and V. Stovin, “Computational fluid dynamics modelling of residence times in vegetated
stormwater ponds,” Proceedings of the Institution of Civil Engineers – Water Management, vol. 171, pp. 1-11, 11/07
2017, doi: 10.1680/jwama.16.00117.
[14] F. Sonnenwald, I. Guymer, and V. Stovin, “A CFD-Based Mixing Model for Vegetated Flows,” Water Resources
Research, vol. 55, no. 3, pp. 2322-2347, 2019, doi: https://doi.org/10.1029/2018WR023628.
[15] S. B. Pope, Turbulent Flows. Cambridge, UK: Cambridge University Press, 2000.
[16] R. Rossi and G. Iaccarino, “Numerical simulation of scalar dispersion downstream of a square obstacle using gradienttransport type models,” Atmospheric Environment, vol. 43, no. 16, pp. 2518-2531, 2009/05/01/ 2009, doi:
https://doi.org/10.1016/j.atmosenv.2009.02.044.
[17] R. Chowdhury, M. Ahadi, K. A. Mazurek, G. Putz, D. Bergstrom, and C. Albers, “Physical Scale and Computational
Modeling in the Development of a Vortex-Type Stormwater Retention Pond,” in World Environmental and Water
Resources Congress 2016, 2016, pp. 388-397.
[18] V. Yakhot and L. M. Smith, “The renormalization group, the ɛ-expansion and derivation of turbulence models,” Journal
of Scientific Computing, vol. 7, no. 1, pp. 35-61, 1992/03/01 1992, doi: 10.1007/BF01060210.
[19] Flow Science, Inc., FLOW-3D User manual. Santa Fe, NM, USA. (2015).
[20] M. M. Bishop, J. M. Morgan, B. Cornwell, and D. K. Jamison, “Improving the Disinfection Detention Time of a Water
Plant Clearwell,” Journal AWWA, vol. 85, no. 3, pp. 68-75, 1993, doi: https://doi.org/10.1002/j.1551-
8833.1993.tb05958.x.
[21] F. L. Hart, “Improved Hydraulic Performance of Chlorine Contact Chambers.,” Jounal of Water Pollution Control
Federation, vol. 51(12), pp. 2868–2875, 1979.

Hydraulic Analysis of Submerged Spillway Flows and Performance Evaluation of Chute Aerator Using CFD Modeling: A Case Study of Mangla Dam Spillway

CFD 모델링을 이용한 침수 배수로 흐름의 수리학적 해석 및 슈트 폭기장치 성능 평가: Mangla Dam 배수로 사례 연구

Hydraulic Analysis of Submerged Spillway Flows and Performance Evaluation of Chute Aerator Using CFD Modeling: A Case Study of Mangla Dam Spillway

Muhammad Kaleem SarwarZohaib NisarGhulam NabiFaraz ul HaqIjaz AhmadMuhammad Masood & Noor Muhammad Khan 

Abstract

대용량 배출구가 있는 수중 여수로는 일반적으로 홍수 처리 및 침전물 세척의 이중 기능을 수행하기 위해 댐 정상 아래에 제공됩니다. 이 방수로를 통과하는 홍수 물은 난류 거동을 나타냅니다. 

게다가 이러한 난류의 수력학적 분석은 어려운 작업입니다. 

따라서 본 연구는 파키스탄 Mangla Dam에 건설된 수중 여수로의 수리학적 거동을 수치해석을 통해 조사하는 것을 목적으로 한다. 또한 다양