316-L 스테인리스강의 레이저 분말 베드 융합 중 콜드 스패터 형성의 충실도 높은 수치 모델링
W.E. ALPHONSO1*, M. BAYAT1 and J.H. HATTEL1 *Corresponding author 1Technical University of Denmark (DTU), 2800, Kgs, Lyngby, Denmark
L-PBF(Laser Powder Bed Fusion)는 금속 적층 제조(MAM) 기술로, 기존 제조 공정에 비해 부품 설계 자유도, 조립품 통합, 부품 맞춤화 및 낮은 툴링 비용과 같은 여러 이점을 산업에 제공합니다.
전기 코일 및 열 관리 장치는 일반적으로 높은 전기 및 열 전도성 특성으로 인해 순수 구리로 제조됩니다. 따라서 순동의 L-PBF가 가능하다면 기하학적으로 최적화된 방열판과 자유형 전자코일을 제작할 수 있습니다.
그러나 L-PBF로 조밀한 순동 부품을 생산하는 것은 적외선에 대한 낮은 광 흡수율과 높은 열전도율로 인해 어렵습니다. 기존의 L-PBF 시스템에서 조밀한 구리 부품을 생산하려면 적외선 레이저의 출력을 500W 이상으로 높이거나 구리의 광흡수율이 높은 녹색 레이저를 사용해야 합니다.
적외선 레이저 출력을 높이면 후면 반사로 인해 레이저 시스템의 광학 구성 요소가 손상되고 렌즈의 열 광학 현상으로 인해 공정이 불안정해질 수 있습니다. 이 작업에서 FVM(Finite Volume Method)에 기반한 다중 물리학 중간 규모 수치 모델은 Flow-3D에서 개발되어 용융 풀 역학과 궁극적으로 부품 품질을 제어하는 물리적 현상 상호 작용을 조사합니다.
녹색 레이저 열원과 적외선 레이저 열원은 기판 위의 순수 구리 분말 베드에 단일 트랙 증착을 생성하기 위해 개별적으로 사용됩니다.
용융 풀 역학에 대한 레이저 열원의 유사하지 않은 광학 흡수 특성의 영향이 탐구됩니다. 수치 모델을 검증하기 위해 단일 트랙이 구리 분말 베드에 증착되고 시뮬레이션된 용융 풀 모양과 크기가 비교되는 실험이 수행되었습니다.
녹색 레이저는 광흡수율이 높아 전도 및 키홀 모드 용융이 가능하고 적외선 레이저는 흡수율이 낮아 키홀 모드 용융만 가능하다. 레이저 파장에 대한 용융 모드의 변화는 궁극적으로 기계적, 전기적 및 열적 특성에 영향을 미치는 열 구배 및 냉각 속도에 대한 결과를 가져옵니다.
Laser Powder Bed Fusion (L-PBF) is a Metal Additive Manufacturing (MAM) technology which offers several advantages to industries such as part design freedom, consolidation of assemblies, part customization and low tooling cost over conventional manufacturing processes. Electric coils and thermal management devices are generally manufactured from pure copper due to its high electrical and thermal conductivity properties. Therefore, if L-PBF of pure copper is feasible, geometrically optimized heat sinks and free-form electromagnetic coils can be manufactured. However, producing dense pure copper parts by L-PBF is difficult due to low optical absorptivity to infrared radiation and high thermal conductivity. To produce dense copper parts in a conventional L-PBF system either the power of the infrared laser must be increased above 500W, or a green laser should be used for which copper has a high optical absorptivity. Increasing the infrared laser power can damage the optical components of the laser systems due to back reflections and create instabilities in the process due to thermal-optical phenomenon of the lenses. In this work, a multi-physics meso-scale numerical model based on Finite Volume Method (FVM) is developed in Flow-3D to investigate the physical phenomena interaction which governs the melt pool dynamics and ultimately the part quality. A green laser heat source and an infrared laser heat source are used individually to create single track deposition on pure copper powder bed above a substrate. The effect of the dissimilar optical absorptivity property of laser heat sources on the melt pool dynamics is explored. To validate the numerical model, experiments were conducted wherein single tracks are deposited on a copper powder bed and the simulated melt pool shape and size are compared. As the green laser has a high optical absorptivity, a conduction and keyhole mode melting is possible while for the infrared laser only keyhole mode melting is possible due to low absorptivity. The variation in melting modes with respect to the laser wavelength has an outcome on thermal gradient and cooling rates which ultimately affect the mechanical, electrical, and thermal properties.
Pure Copper, Laser Powder Bed Fusion, Finite Volume Method, multi-physics
 L. Jyothish Kumar, P. M. Pandey, and D. I. Wimpenny, 3D printing and additive manufacturing technologies. Springer Singapore, 2018. doi: 10.1007/978-981-13-0305-0.  T. DebRoy et al., “Additive manufacturing of metallic components – Process, structure and properties,” Progress in Materials Science, vol. 92, pp. 112–224, 2018, doi: 10.1016/j.pmatsci.2017.10.001.  C. S. Lefky, B. Zucker, D. Wright, A. R. Nassar, T. W. Simpson, and O. J. Hildreth, “Dissolvable Supports in Powder Bed Fusion-Printed Stainless Steel,” 3D Printing and Additive Manufacturing, vol. 4, no. 1, pp. 3–11, 2017, doi: 10.1089/3dp.2016.0043.  J. L. Bartlett and X. Li, “An overview of residual stresses in metal powder bed fusion,” Additive Manufacturing, vol. 27, no. January, pp. 131–149, 2019, doi: 10.1016/j.addma.2019.02.020.  I. H. Ahn, “Determination of a process window with consideration of effective layer thickness in SLM process,” International Journal of Advanced Manufacturing Technology, vol. 105, no. 10, pp. 4181–4191, 2019, doi: 10.1007/s00170-019-04402-w.
 R. McCann et al., “In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review,” Additive Manufacturing, vol. 45, no. May, 2021, doi: 10.1016/j.addma.2021.102058.  M. Bayat et al., “Keyhole-induced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation,” Additive Manufacturing, vol. 30, no. August, p. 100835, 2019, doi: 10.1016/j.addma.2019.100835.  M. Bayat, S. Mohanty, and J. H. Hattel, “Multiphysics modelling of lack-of-fusion voids formation and evolution in IN718 made by multi-track/multi-layer L-PBF,” International Journal of Heat and Mass Transfer, vol. 139, pp. 95–114, 2019, doi: 10.1016/j.ijheatmasstransfer.2019.05.003.  S. D. Jadhav, L. R. Goossens, Y. Kinds, B. van Hooreweder, and K. Vanmeensel, “Laserbased powder bed fusion additive manufacturing of pure copper,” Additive Manufacturing, vol. 42, no. March, 2021, doi: 10.1016/j.addma.2021.101990.  S. D. Jadhav, S. Dadbakhsh, L. Goossens, J. P. Kruth, J. van Humbeeck, and K. Vanmeensel, “Influence of selective laser melting process parameters on texture evolution in pure copper,” Journal of Materials Processing Technology, vol. 270, no. January, pp. 47–58, 2019, doi: 10.1016/j.jmatprotec.2019.02.022.  H. Siva Prasad, F. Brueckner, J. Volpp, and A. F. H. Kaplan, “Laser metal deposition of copper on diverse metals using green laser sources,” International Journal of Advanced Manufacturing Technology, vol. 107, no. 3–4, pp. 1559–1568, 2020, doi: 10.1007/s00170- 020-05117-z.  L. R. Goossens, Y. Kinds, J. P. Kruth, and B. van Hooreweder, “On the influence of thermal lensing during selective laser melting,” Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, SFF 2018, no. December, pp. 2267–2274, 2020.  M. Bayat, V. K. Nadimpalli, D. B. Pedersen, and J. H. Hattel, “A fundamental investigation of thermo-capillarity in laser powder bed fusion of metals and alloys,” International Journal of Heat and Mass Transfer, vol. 166, p. 120766, 2021, doi: 10.1016/j.ijheatmasstransfer.2020.120766.  H. Chen, Q. Wei, Y. Zhang, F. Chen, Y. Shi, and W. Yan, “Powder-spreading mechanisms in powder-bed-based additive manufacturing: Experiments and computational modeling,” Acta Materialia, vol. 179, pp. 158–171, 2019, doi: 10.1016/j.actamat.2019.08.030.  S. K. Nayak, S. K. Mishra, C. P. Paul, A. N. Jinoop, and K. S. Bindra, “Effect of energy density on laser powder bed fusion built single tracks and thin wall structures with 100 µm preplaced powder layer thickness,” Optics and Laser Technology, vol. 125, May 2020, doi: 10.1016/j.optlastec.2019.106016.  G. Nordet et al., “Absorptivity measurements during laser powder bed fusion of pure copper with a 1 kW cw green laser,” Optics & Laser Technology, vol. 147, no. April 2021, p. 107612, 2022, doi: 10.1016/j.optlastec.2021.107612.  M. Hummel, C. Schöler, A. Häusler, A. Gillner, and R. Poprawe, “New approaches on laser micro welding of copper by using a laser beam source with a wavelength of 450 nm,” Journal of Advanced Joining Processes, vol. 1, no. February, p. 100012, 2020, doi: 10.1016/j.jajp.2020.100012.  M. Hummel, M. Külkens, C. Schöler, W. Schulz, and A. Gillner, “In situ X-ray tomography investigations on laser welding of copper with 515 and 1030 nm laser beam sources,” Journal of Manufacturing Processes, vol. 67, no. April, pp. 170–176, 2021, doi: 10.1016/j.jmapro.2021.04.063.  L. Gargalis et al., “Determining processing behaviour of pure Cu in laser powder bed fusion using direct micro-calorimetry,” Journal of Materials Processing Technology, vol. 294, no. March, p. 117130, 2021, doi: 10.1016/j.jmatprotec.2021.117130.  A. Mondal, D. Agrawal, and A. Upadhyaya, “Microwave heating of pure copper powder with varying particle size and porosity,” Journal of Microwave Power and Electromagnetic Energy, vol. 43, no. 1, pp. 4315–43110, 2009, doi: 10.1080/08327823.2008.11688599.
316-L 스테인리스강의 레이저 분말 베드 융합 중 콜드 스패터 형성의 충실도 높은 수치 모델링
M. BAYAT1,* , AND J. H. HATTEL1
Corresponding author 1 Technical University of Denmark (DTU), Building 425, Kgs. 2800 Lyngby, Denmark
Spatter and denudation are two very well-known phenomena occurring mainly during the laser powder bed fusion process and are defined as ejection and displacement of powder particles, respectively. The main driver of this phenomenon is the formation of a vapor plume jet that is caused by the vaporization of the melt pool which is subjected to the laser beam. In this work, a 3-dimensional transient turbulent computational fluid dynamics model coupled with a discrete element model is developed in the finite volume-based commercial software package Flow-3D AM to simulate the spatter phenomenon. The numerical results show that a localized low-pressure zone forms at the bottom side of the plume jet and this leads to a pseudo-Bernoulli effect that drags nearby powder particles into the area of influence of the vapor plume jet. As a result, the vapor plume acts like a momentum sink and therefore all nearby particles point are dragged towards this region. Furthermore, it is noted that due to the jet’s attenuation, powder particles start diverging from the central core region of the vapor plume as they move vertically upwards. It is moreover observed that only particles which are in the very central core region of the plume jet get sufficiently accelerated to depart the computational domain, while the rest of the dragged particles, especially those which undergo an early divergence from the jet axis, get stalled pretty fast as they come in contact with the resting fluid. In the last part of the work, two simulations with two different scanning speeds are carried out, where it is clearly observed that the angle between the departing powder particles and the vertical axis of the plume jet increases with increasing scanning speed.
스패터와 denudation은 주로 레이저 분말 베드 융합 과정에서 발생하는 매우 잘 알려진 두 가지 현상으로 각각 분말 입자의 배출 및 변위로 정의됩니다.
이 현상의 주요 동인은 레이저 빔을 받는 용융 풀의 기화로 인해 발생하는 증기 기둥 제트의 형성입니다. 이 작업에서 이산 요소 모델과 결합된 3차원 과도 난류 전산 유체 역학 모델은 스패터 현상을 시뮬레이션하기 위해 유한 체적 기반 상용 소프트웨어 패키지 Flow-3D AM에서 개발되었습니다.
수치적 결과는 플룸 제트의 바닥면에 국부적인 저압 영역이 형성되고, 이는 근처의 분말 입자를 증기 플룸 제트의 영향 영역으로 끌어들이는 의사-베르누이 효과로 이어진다는 것을 보여줍니다.
결과적으로 증기 기둥은 운동량 흡수원처럼 작용하므로 근처의 모든 입자 지점이 이 영역으로 끌립니다. 또한 제트의 감쇠로 인해 분말 입자가 수직으로 위쪽으로 이동할 때 증기 기둥의 중심 코어 영역에서 발산하기 시작합니다.
더욱이 플룸 제트의 가장 중심 코어 영역에 있는 입자만 계산 영역을 벗어날 만큼 충분히 가속되는 반면, 드래그된 나머지 입자, 특히 제트 축에서 초기 발산을 겪는 입자는 정체되는 것으로 관찰됩니다. 그들은 휴식 유체와 접촉하기 때문에 꽤 빠릅니다.
작업의 마지막 부분에서 두 가지 다른 스캔 속도를 가진 두 가지 시뮬레이션이 수행되었으며, 여기서 출발하는 분말 입자와 연기 제트의 수직 축 사이의 각도가 스캔 속도가 증가함에 따라 증가하는 것이 명확하게 관찰되었습니다.
 T. DebRoy et al., “Additive manufacturing of metallic components – Process, structure and properties,” Prog. Mater. Sci., vol. 92, pp. 112–224, 2018, doi: 10.1016/j.pmatsci.2017.10.001.  M. Markl and C. Körner, “Multiscale Modeling of Powder Bed–Based Additive Manufacturing,” Annu. Rev. Mater. Res., vol. 46, no. 1, pp. 93–123, 2016, doi: 10.1146/annurev-matsci-070115-032158.  A. Zinoviev, O. Zinovieva, V. Ploshikhin, V. Romanova, and R. Balokhonov, “Evolution of grain structure during laser additive manufacturing. Simulation by a cellular automata method,” Mater. Des., vol. 106, pp. 321–329, 2016, doi: 10.1016/j.matdes.2016.05.125.  Y. Zhang and J. Zhang, “Modeling of solidification microstructure evolution in laser powder bed fusion fabricated 316L stainless steel using combined computational fluid dynamics and cellular automata,” Addit. Manuf., vol. 28, no. July 2018, pp. 750–765, 2019, doi: 10.1016/j.addma.2019.06.024.  A. A. Martin et al., “Ultrafast dynamics of laser-metal interactions in additive manufacturing alloys captured by in situ X-ray imaging,” Mater. Today Adv., vol. 1, p. 100002, 2019, doi: 10.1016/j.mtadv.2019.01.001.  Y. C. Wu et al., “Numerical modeling of melt-pool behavior in selective laser melting with random powder distribution and experimental validation,” J. Mater. Process. Technol., vol. 254, no. July 2017, pp. 72–78, 2018, doi: 10.1016/j.jmatprotec.2017.11.032.  W. Gao, S. Zhao, Y. Wang, Z. Zhang, F. Liu, and X. Lin, “Numerical simulation of thermal field and Fe-based coating doped Ti,” Int. J. Heat Mass Transf., vol. 92, pp. 83– 90, 2016, doi: 10.1016/j.ijheatmasstransfer.2015.08.082.  A. Charles, M. Bayat, A. Elkaseer, L. Thijs, J. H. Hattel, and S. Scholz, “Elucidation of dross formation in laser powder bed fusion at down-facing surfaces: Phenomenonoriented multiphysics simulation and experimental validation,” Addit. Manuf., vol. 50, 2022, doi: 10.1016/j.addma.2021.102551.  C. Meier, R. W. Penny, Y. Zou, J. S. Gibbs, and A. J. Hart, “Thermophysical phenomena in metal additive manufacturing by selective laser melting: Fundamentals, modeling, simulation and experimentation,” arXiv, 2017, doi: 10.1615/annualrevheattransfer.2018019042.  W. King, A. T. Anderson, R. M. Ferencz, N. E. Hodge, C. Kamath, and S. A. Khairallah, “Overview of modelling and simulation of metal powder bed fusion process at Lawrence Livermore National Laboratory,” Mater. Sci. Technol. (United Kingdom), vol. 31, no. 8, pp. 957–968, 2015, doi: 10.1179/1743284714Y.0000000728.
In this study a gating system including sprue, runner and overflows for semi-solid rheocasting of aluminum alloy was designed by means of numerical simulations with a commercial software. The effects of pouring temperature, mold temperature and injection speed on the filling process performance of semi-solid die casting were studied. Based on orthogonal test analysis, the optimal die casting process parameters were selected, which were metal pouring temperature 590 °C, mold temperature 260 °C and injection velocity 0.5 m/s. Semi-solid slurry preparation process of Swirled Enthalpy Equilibration Device (SEED) was used for die casting production experiment. Aluminum alloy semi-solid bracket components were successfully produced with the key die casting process parameters selected, which was consistent with the simulation result. The design of semi-solid gating system was further verified by observing and analyzing the microstructure of different zones of the casting. The characteristic parameters, particle size and shape factor of microstructure of the produced semi-solid casting showed that the semi-solid aluminum alloy components are of good quality.
이 연구에서 알루미늄 합금의 반고체 레오캐스팅을 위한 스프루, 러너 및 오버플로를 포함하는 게이팅 시스템은 상용 소프트웨어를 사용한 수치 시뮬레이션을 통해 설계되었습니다. 주입 온도, 금형 온도 및 사출 속도가 반고체 다이캐스팅의 충전 공정 성능에 미치는 영향을 연구했습니다. 직교 테스트 분석을 기반으로 금속 주입 온도 590°C, 금형 온도 260°C 및 사출 속도 0.5m/s인 최적의 다이 캐스팅 공정 매개변수가 선택되었습니다. Swirled Enthalpy Equilibration Device(SEED)의 반고체 슬러리 제조 공정을 다이캐스팅 생산 실험에 사용하였다. 알루미늄 합금 반고체 브래킷 구성 요소는 시뮬레이션 결과와 일치하는 주요 다이 캐스팅 공정 매개변수를 선택하여 성공적으로 생산되었습니다. 반고체 게이팅 시스템의 설계는 주조의 다른 영역의 미세 구조를 관찰하고 분석하여 추가로 검증되었습니다. 생산된 반고체 주조물의 특성 매개변수, 입자 크기 및 미세 구조의 형상 계수는 반고체 알루미늄 합금 부품의 품질이 양호함을 보여주었습니다.
G. Li, H. Lu, X. Hu et al., Current progress in rheoforming of wrought aluminum alloys: a review. Met. Open Access Metall. J. 10(2), 238 (2020)CASGoogle Scholar
H.V. Atkinson, Alloys for semi-solid processing. Solid State Phenom. 192–193, 16–27 (2013)Google Scholar
L. Rogal, Critical assessment: opportunities in developing semi-solid processing: aluminium, magnesium, and high-temperature alloys. Mater. Sci. Technol. Mst A Publ. Inst. Met. 33, 759–764 (2017)CASArticleGoogle Scholar
H. Guo, Rheo-diecasting process for semi-solid aluminum alloys. J. Wuhan Univ. Technol. Mater. Sci. Ed. 22(004), 590–595 (2007)CASArticleGoogle Scholar
T. Chucheep, J. Wannasin, R. Canyook, T. Rattanochaikul, S. Janudom, S. Wisutmethangoon, M.C. Flemings, Characterization of flow behavior of semi-solid slurries with low solid fractions. Metall. Mater. Trans. A 44(10), 4754–4763 (2013)CASArticleGoogle Scholar
M. Li, Y.D. Li, W.L. Yang et al., Effects of forming processes on microstructures and mechanical properties of A356 aluminum alloy prepared by self-inoculation method. Mater. Res. 22(3) (2019)
P. Côté, M.E. Larouche, X.G. Chen et al., New developments with the SEED technology. Solid State Phenom. 192(3), 373–378 (2012)ArticleGoogle Scholar
Z.Y. Liu, W.M. Mao, W.P. Wang et al., Investigation of rheo-diecasting mold filling of semi-solid A380 aluminum alloy slurry. Int. J. Miner. Metall. Mater. 24(006), 691–700 (2017)CASArticleGoogle Scholar
M. Arif, M.Z. Omar, N. Muhamad et al., Microstructural evolution of solid-solution-treated Zn–22Al in the semisolid state. J. Mater. Sci. Technol. 29(008), 765–774 (2013)CASArticleGoogle Scholar
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
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.
carbon monoxide, exhaust system, CFD simulation.
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.
1Tecnológico Nacional de México/ITS de Los Reyes. Carretera Los Reyes-Jacona, Col. Libertad. 60300. Los Reyes de Salgado, Michoacán. México.
firstname.lastname@example.org – 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.
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.
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–
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
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
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.
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.
aUniversity of Padova, Department of Management and Engineering, Vicenza, Italy bUniversity of Bayburt, Mechanical Engineering, Bayburt, Turkey cAtilim University, Metallurgical and Materials Engineering, Ankara, Turkey dIstanbul Technical University, Metallurgical and Materials Engineering, Istanbul, Turkey eCenter for Critical and Functional Materials, ITU, Istanbul, Turkey
The demand for lighter weight decreased thickness and higher strength has become the focal point in the automotive industry. In order to meet such requirements, the addition of several alloying elements has been started to be investigated. In this work, the additions of V, B, and Sr on feedability and tensile properties of A360 has been studied. A mold design that consisted of test bars has been produced. Initially, a simulation was carried out to optimize the runners, filling, and solidification parameters. Following the tests, it was found that V addition revealed the highest UTS but low elongation at fracture, while B addition exhibited visa verse. On the other hand, impact energy was higher with B additions.
더 가벼운 무게의 감소된 두께와 더 높은 강도에 대한 요구는 자동차 산업의 초점이 되었습니다. 이러한 요구 사항을 충족하기 위해 여러 합금 원소의 추가가 조사되기 시작했습니다. 이 연구에서는 A360의 이송성 및 인장 특성에 대한 V, B 및 Sr의 첨가가 연구되었습니다. 시험봉으로 구성된 금형 설계가 제작되었습니다. 처음에는 러너, 충전 및 응고 매개변수를 최적화하기 위해 시뮬레이션이 수행되었습니다. 시험 결과, V 첨가는 UTS가 가장 높지만 파단 연신율은 낮았고, B 첨가는 visa verse를 나타냈다. 반면에 충격 에너지는 B 첨가에서 더 높았다.
 A. Johanson, Effect of Vanadium on Grain Refinement of Aluminium, Institutt for materialteknologi, 2013.  D.G. McCartney, Grain refining of aluminium and its alloys using inoculants, Int. Mater. Rev. 34 (1) (1989) 247–260.  M.T. Di Giovanni, The Influence of Ni and V Trace Elements on the High Temperature Tensile Properties of A356 Aluminium Foundry Alloy, Institutt for materialteknologi, 2014.  D. Casari, T.H. Ludwig, M. Merlin, L. Arnberg, G.L. Garagnani, The effect of Ni and V trace elements on the mechanical properties of A356 aluminium foundry alloy in as-cast and T6 heat treated conditions, Mater. Sci. Eng., A 610 (2014) 414–426.  D. Casari, T.H. Ludwig, M. Merlin, L. Arnberg, G.L. Garagnani, Impact behavior of A356 foundry alloys in the presence of trace elements Ni and V, J. Mater. Eng. Perform. 24 (2) (2015) 894–908.  T.H. Ludwig, P.L. Schaffer, L. Arnberg, Influence of some trace elements on solidification path and microstructure of Al-Si foundry alloys, Metall. Mater. Trans. 44 (8) (2013) 3783–3796.  H.A. Elhadari, H.A. Patel, D.L. Chen, W. Kasprzak, Tensile and fatigue properties of a cast aluminum alloy with Ti, Zr and V additions, Mater. Sci. Eng., A 528 (28) (2011) 8128–8138.  Y. Wu, H. Liao, K. Zhou, “Effect of minor addition of vanadium on mechanical properties and microstructures of as-extruded near eutectic Al–Si–Mg alloy, Mater. Sci. Eng., A 602 (2014) 41–48.  E.S. Dæhlen, The Effect of Vanadium on AlFeSi-Intermetallic Phases in a Hypoeutectic Al-Si Foundry Alloy, Institutt for materialteknologi, 2013.  B. Lin, H. Li, R. Xu, H. Xiao, W. Zhang, S. Li, Effects of vanadium on modification of iron-rich intermetallics and mechanical properties in A356 cast alloys with 1.5 wt. % Fe, J. Mater. Eng. Perform. 28 (1) (2019) 475–484.  P.A. Tøndel, G. Halvorsen, L. Arnberg, Grain refinement of hypoeutectic Al-Si foundry alloys by addition of boron containing silicon metal, Light Met. (1993) 783.  Z. Chen, et al., Grain refinement of hypoeutectic Al-Si alloys with B, Acta Mater. 120 (2016) 168–178.  T. Wang, Z. Chen, H. Fu, J. Xu, Y. Fu, T. Li, “Grain refining potency of Al–B master alloy on pure aluminum, Scripta Mater. 64 (12) (2011) 1121–1124.  M. Gorny, ´ G. Sikora, M. Kawalec, Effect of titanium and boron on the stability of grain refinement of Al-Cu alloy, Arch. Foundry Eng. 16 (2016).  O. ¨ Gürsoy, E. Erzi, D. Dıs¸pınar, Ti grain refinement myth and cleanliness of A356 melt, in: Shape Casting, Springer, 2019, pp. 125–130.  D. Dispinar, A. Nordmark, J. Voje, L. Arnberg, Influence of hydrogen content and bi-film index on feeding behaviour of Al-7Si, in: 138th TMS Annual Meeting, Shape Casting, 3rd International Symposium, San Francisco, California, USA, 2009, pp. 63–70. February 2009.  M. Uludag, ˘ R. Çetin, D. Dıs¸pınar, Observation of hot tearing in Sr-B modified A356 alloy, Arch. Foundry Eng. 17 (2017).  X.L. Cui, Y.Y. Wu, T. Gao, X.F. Liu, “Preparation of a novel Al–3B–5Sr master alloy and its modification and refinement performance on A356 alloy, J. Alloys Compd. 615 (2014) 906–911.  F. Wang, Z. Liu, D. Qiu, J.A. Taylor, M.A. Easton, M.-X. Zhang, Revisiting the role of peritectics in grain refinement of Al alloys, Acta Mater. 61 (1) (2013) 360–370.  M. Akhtar, A. Khajuria, Effects of prior austenite grain size on impression creep and microstructure in simulated heat affected zones of boron modified P91 steels, Mater. Chem. Phys. 249 (2020) 122847.  M. Akhtar, A. Khajuria, Probing true creep-hardening interaction in weld simulated heat affected zone of P91 steels, J. Manuf. Process. 46 (2019) 345–356.  E.M. Schulson, T.P. Weihs, I. Baker, H.J. Frost, J.A. Horton, Grain boundary accommodation of slip in Ni3Al containing boron, Acta Metall. 34 (7) (1986) 1395–1399.  I. Baker, E.M. Schulson, J.R. Michael, The effect of boron on the chemistry of grain boundaries in stoichiometric Ni3Al, Philos. Mag. A B 57 (3) (Mar. 1988) 379–385.  S. Zhu, et al., Influences of nickel and vanadium impurities on microstructure of aluminum alloys, JOM (J. Occup. Med.) 65 (5) (2013) 584–592.  D.J. Beerntsen, Effect of vanadium and zirconium on the formation of CrAI 7 primary crystals in 7075 aluminum alloy, Metall. Mater. Trans. B 8 (3) (1977) 687–688.  G. Timelli, A. Fabrizi, S. Capuzzi, F. Bonollo, S. Ferraro, The role of Cr additions and Fe-rich compounds on microstructural features and impact toughness of AlSi9Cu3 (Fe) diecasting alloys, Mater. Sci. Eng., A 603 (2014) 58–68.  S. Kirtay, D. Dispinar, Effect of ranking selection on the Weibull modulus estimation, Gazi Univ. J. Sci. 25 (1) (2012) 175–187.  J. Rakhmonov, G. Timelli, F. Bonollo, “The effect of transition elements on hightemperature mechanical properties of Al–Si foundry alloys–A review, Adv. Eng. Mater. 18 (7) (2016) 1096–1105.
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
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.
 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.  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.  K.M. Becker, C.H. Ling, S. Hedberg, G. Strand, An experimental investigation of post dryout heat transfer, R. Inst. Technol. (1983).  K.M. Becker, A Burnout Correlation for Flow of Boiling Water in Vertical Rod Bundles, AB Atomenergi, 1967.  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.  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.  S.S. Paranjape, Two-Phase Flow Interfacial Structures in a Rod Bundle Geometry, Purdue University, 2009.  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.  M. Thurgood, J. Kelly, T. Guidotti, R. Kohrt, K. Crowell, Tech. rep., Pacific Northwest National Laboratory, 1983.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.
 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.  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.  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.  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.  I. Tanasawa, Advances in condensation heat transfer, Ad. Heat Transf. 21 (1991) 55–139 Vol, doi:10.1016/S0065-2717(08)70334-4.  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.  B. Matzner, G.M. Latter, Reduced pressure drop space for boiling water reactor fuel bundles, US Patent US5375154A, (1993)  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.  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.  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.  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.  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.  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).  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.  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.  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.  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.  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.  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.  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.  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.  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.
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.
Spatial dam breach; FLOW-3D; Overtopping erosion; Computational fluid dynamics (CFD)
There are many purposes for dam construction, such as protection from flood disasters, water storage, and power generation. Embankment 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 . 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 . Alhasan et al.  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  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.  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.  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 .
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 , , but Y. Yang et al.  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.
Zhang et al.  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.  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.  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.  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.  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  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.  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.  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.  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.  and Wu et al.  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.  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 . 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. Therefore, 3D models are preferred. Larocque et al.  and Yang et al.  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.
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 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.  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 (Ax, Ay, Az) 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 . 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 . 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  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 , 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. , 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
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  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.
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
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.
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.
 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.  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.  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.  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.  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.  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.  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.
M. Bayat* , V. K. Nadimpalli, J. H. Hattel 1Department of Mechanical Engineering, Technical University of Denmark (DTU), Produktionstorvet 425, Kgs. 2800, Lyngby, Denmark
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.
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.
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.
 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.  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.  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.  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.  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.  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.  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.  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).  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.  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.  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.  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.
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.
 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.  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.  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.  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.  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.  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.  Y. Wu, Numerical Simulation and Experiment Study of Flower Plate Pouring System for Solid Propellant, Chin. J. Expl. Propell. 2017, 41, 506–511.  T. M. G. Chu, J. W. Halloran, High-Temperature Flow Behavior of Ceramic Suspensions, J. Am. Ceram. Soc. 2004, 83, 2189– 2195.  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.  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.  F. Soltani, Ü. Yilmazer, Slip Velocity and Slip Layer Thickness in Flow of Concentrated Suspensions, J. Appl. Polym. Sci. 1998, 70, 515–522.
 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.  J. Mewis, N. J. Wagner, Colloidal Suspension Rheology, Cambridge University Press, 2011.  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.  H. Ohshima, Effective Viscosity of a Concentrated Suspension of Uncharged Spherical Soft Particles, Langmuir 2010, 26, 6287–6294.
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.
 I. Astm, ASTM52900-15 Standard Terminology for Additive Manufacturing—General Principles—Terminology, ASTM International, West Conshohocken, PA 3(4) (2015) 5.  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.  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.  K. Dai, L. Shaw, Thermal and stress modeling of multi-material laser processing, Acta Materialia 49(20) (2001) 4171-4181.  K. Dai, L. Shaw, Distortion minimization of laser-processed components through control of laser scanning patterns, Rapid Prototyping Journal 8(5) (2002) 270-276.  S.S. Bo Cheng, Kevin Chou, Stress and deformation evaluations of scanning strategy effect in selective laser melting, Additive Manufacturing (2017).  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.  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.  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.  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).  E.R. Denlinger, J. Irwin, P. Michaleris, Thermomechanical Modeling of Additive Manufacturing Large Parts, Journal of Manufacturing Science and Engineering 136(6) (2014) 061007.  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.  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.  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.  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.  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.  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.  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.  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.  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).  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.  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.  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).  S. Shrestha, B. Cheng, K. Chou, An Investigation into Melt Pool Effective Thermal Conductivity for Thermal Modeling of Powder-Bed Electron Beam Additive Manufacturing.  D. Rosenthal, Mathematical theory of heat distribution during welding and cutting, Welding journal 20 (1941) 220-234.  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.  M. Tang, P.C. Pistorius, J.L. Beuth, Prediction of lack-of-fusion porosity for powder bed fusion, Additive Manufacturing 14 (2017) 39-48.  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.  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.  B. Cheng, S. Shrestha, K. Chou, Stress and deformation evaluations of scanning strategy effect in selective laser melting, Additive Manufacturing 12 (2016) 240-251.  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.  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).  M.F. Zaeh, G. Branner, Investigations on residual stresses and deformations in selective laser melting, Production Engineering 4(1) (2010) 35-45.  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.  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.  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.  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.  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.  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.  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.  C. Wang, X. Tan, S. Tor, C. Lim, Machine learning in additive manufacturing: State-of-theart and perspectives, Additive Manufacturing (2020) 101538.  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.  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.  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.  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.  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.  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.  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.  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.  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).  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.  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.  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.  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.  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.  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.  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.  D.E. Smith, R. Hoglund, Continuous fiber angle topology optimization for polymer fused fillament fabrication, Annu. Int. Solid Free. Fabr. Symp. Austin, TX, 2016.  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.  Q. Xia, T. Shi, Optimization of composite structures with continuous spatial variation of fiber angle through Shepard interpolation, Composite Structures 182 (2017) 273-282.  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.  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.  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.  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.  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.  K.C. Mills, Recommended values of thermophysical properties for selected commercial alloys, Woodhead Publishing2002.  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.  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.  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.  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.  T. Mukherjee, W. Zhang, T. DebRoy, An improved prediction of residual stresses and distortion in additive manufacturing, Computational Materials Science 126 (2017) 360-372.  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.  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.  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.  J. Irwin, P. Michaleris, A line heat input model for additive manufacturing, Journal of Manufacturing Science and Engineering 138(11) (2016) 111004.  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).  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.  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.  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.  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.  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.  P. Mercelis, J.-P. Kruth, Residual stresses in selective laser sintering and selective laser melting, Rapid Prototyping Journal 12(5) (2006) 254-265.  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.  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.  C. Li, J. liu, Y. Guo, Efficient predictive model of part distortion and residual stress in selective laser melting, Solid Freeform Fabrication 2016, 2017.  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.  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.  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).  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.  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.  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.  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.  R. Fabbro, K. Chouf, Keyhole modeling during laser welding, Journal of applied Physics 87(9) (2000) 4075-4083.  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.  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.  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.  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.  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.  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.  M. Bugatti, Q. Semeraro, Limitations of the Inherent Strain Method in Simulating Powder Bed Fusion Processes, Additive Manufacturing 23 (2018) 329-346.  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).  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.  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.  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.  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.  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.  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.  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.  J. Liu, Q. Chen, Y. Zheng, R. Ahmad, J. Tang, Y. Ma, Level set-based heterogeneous object modeling and optimization, Computer-Aided Design (2019).  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.  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.  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.  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.  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.  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.  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.  S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9(8) (1997) 1735-1780.  A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems 25 (2012) 1097-1105.  K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014).  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.  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.  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.  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.  D.G. Duffy, Green’s functions with applications, Chapman and Hall/CRC2015.  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.  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.  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.  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).
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 Heexceeds 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.
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.
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
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.
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 . 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) . 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 .
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 .
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 . 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 . One of the major problems of solar collectors is their low efficiency . 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 . A number of researchers investigated Solar System performance in accordance with energy and exergy analyses. Zhai et al.  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.  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.  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) [2, 12–15].
Wang et al.  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% . Various optimization methods have been used to improve the cogeneration system, minimum system size, and performance, such as genetic algorithm [18, 19].
Hirasawa et al.  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 . In the experimental study done by Jouybari et al. , 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.  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.  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.  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 .
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 1The 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 1Schematic shape of the cogeneration cycle.Table 2Temperature 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 :
Total energy received by the collector is given by 
In the last step based on thermodynamic second law, exergy efficiency has been calculated from the following equation and the above-mentioned calculated loads :
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 2Copper foam with a porosity of 95%.Table 3Thermophysical 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 . 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 3Simulation 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 4Properties of the nanoparticles .
System constant parameters for input in the software are shown in Table 5.Table 5System 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 :where ρ is density and ϕ is the nanoparticles volume fraction.
The specific heat capacity is calculated from the following equation :
The thermal conductivity of the nanofluid is calculated from the following equation :
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 :
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.  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 4Verification charts of energy analysis results.
Figure 5Verification charts of exergy analysis results.
We may also investigate the application of machine learning paradigms [31–41] and various hybrid, advanced optimization approaches that are enhanced in terms of exploration and intensification [42–55], and intelligent model studies [56–61] as well, for example, methods such as particle swarm optimizer (PSO) [60, 62], differential search (DS) , ant colony optimizer (ACO) [61, 64, 65], Harris hawks optimizer (HHO) , grey wolf optimizer (GWO) [53, 67], differential evolution (DE) [68, 69], and other fusion and boosted systems [41, 46, 48, 50, 54, 55, 70, 71].
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 [9, 22–26, 30, 72]. 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 6Collectors 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 6Energy and exergy efficiencies of the PTC with porous media and nanofluid.
Figure 7Energy 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 . 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 . 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%.
Heat transfer augmentation coefficient
Solar collector area
Specific heat capacity of the nanofluid
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 energy receives
Auxiliary boiler heat
Screw expander work
Cooling load, in kilowatts
Heating load, in kilowatts
Solar radiation energy on collector, in Joule
Sanitary hot water load
Heat exchanger efficiency
Natural gas exergy
Steam mass flow rate
Hot water mass flow rate
Specific heat capacity of water
Power output form by the screw expander
Average ambient temperature
Density of the mixture.
Nanoparticles volume fraction
Ratio of the nanolayer thickness.
Combined cooling, heating, and power
Engineering equation solver.
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
M. Mirmozaffari, “Machine learning algorithms based on an optimization model,” 2020.View at: Google Scholar
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
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
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
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
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
C. Yu, “SGOA: annealing-behaved grasshopper optimizer for global tasks,” Engineering with Computers, vol. 1, pp. 1–28, 2021.View at: Google Scholar
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
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
Y. Zhang, “Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis,” Neurocomputing, vol. 1, 2020.View at: Google Scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
M. Eisazadeh and J. Rezapour, “Multi-objective optimization of the composite sheets using PSO algorithm,” 2017.View at: Google Scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
 Center TU. Annual report on China building energy efficiency. China Construction Industry Press (In Chinese). 2016.
 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.
 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.
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 Kleinstreuer C, Chiang H. Analysis of a porous-medium solar collector. Heat Transfer Engineering 1990;11:45-55. https://doi.org/10.1080/01457639008939728
 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
 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
 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
 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
 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
 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
 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
 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.  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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
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.
‘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.
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.
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
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
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
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
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]
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]
Guo, N.; Leu, M.C. Additive manufacturing: Technology, applications and research needs. Front. Mech. Eng. 2013, 8, 215–243. [CrossRef]
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]
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]
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]
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]
Ur Rehman, A.; Sglavo, V.M. 3D printing of geopolymer-based concrete for building applications. Rapid Prototyp. J. 2020, 26, 1783–1788. [CrossRef]
Ur Rehman, A.; Sglavo, V.M. 3D printing of Portland cement-containing bodies. Rapid Prototyp. J. 2021. ahead of print. [CrossRef]
Popovich, A.; Sufiiarov, V. Metal Powder Additive Manufacturing. In New Trends in 3D Printing; InTech: Rijeka, Croatia, 2016.
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]
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]
Ur Rehman, A.; Tingting, L.; Liao, W. 4D Printing; Printing Ceramics from Metals with Selective Oxidation. Patent No. W0/2019/052128, 21 March 2019.
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]
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.
Wong, K.V.; Hernandez, A. A Review of Additive Manufacturing. ISRN Mech. Eng. 2012, 2012, 1–10. [CrossRef]
Körner, C. Additive manufacturing of metallic components by selective electron beam melting—A review. Int. Mater. Rev. 2016, 61, 361–377. [CrossRef]
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]
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]
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]
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]
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]
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]
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]
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]
Frazier, W.E. Metal additive manufacturing: A review. J. Mater. Eng. Perform. 2014, 23, 1917–1928. [CrossRef]
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]
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]
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]
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]
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]
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]
Oane, M.; Sporea, D. Temperature profiles modeling in IR optical components during high power laser irradiation. Infrared Phys. Technol. 2001, 42, 31–40. [CrossRef]
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]
Parteli, E.J.R.; Pöschel, T. Particle-based simulation of powder application in additive manufacturing. Powder Technol. 2016, 288, 96–102. [CrossRef]
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]
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]
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]
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]
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]
Rosenthal, D. Mathematical Theory of Heat Distribution During Welding and Cutting. Weld. J. 1941, 20, 220–234.
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).
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]
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]
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.
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]
선택적 레이저 용융 동안 CP-Ti의 열 거동을 연구하기 위해 무작위 패킹 분말 베드 모델을 기반으로 하는 메조스코프 시뮬레이션이 설정되었습니다. 레이저와 분말의 상호 작용에 따른 용융 풀의 특성과 레이저 출력이 용융 풀의 열 거동, 유체 역학 및 표면 형태 변화에 미치는 영향을 연구했습니다.
결과는 레이저 출력이 증가함에 따라 최대 온도, 온도 변화율, 용융 풀의 수명 및 크기가 크게 향상되었음을 보여줍니다. 또한 본 연구에서는 이중궤도 하의 용융지의 특성과 열거동을 주로 연구하였다.
두 번째 트랙의 용융 풀의 최대 온도, 수명 및 길이와 너비는 첫 번째 트랙보다 더 높고 레이저 출력이 증가함에 따라 용융 풀에서 두 번째 트랙의 길이 너비 비율이 증가함을 알 수 있습니다. 더 커집니다.
A mesoscopic simulation based on random packing powder bed model was established to study the heat behavior of CP-Ti during selective laser melting. The characteristics of the molten pool under the interaction of laser and powder, and the influence of laser power on the thermal behavior, hydrodynamics and surface morphology evolution of the molten pool were studied. The results show that with the increase of laser power, the maximum temperature, temperature change rate, lifetime of molten pool and size are greatly improved. In addition, the characteristics and heat behavior of the molten pool under the double track are mainly studied in this study. It is found that the maximum temperature, lifetime, and the length and width of the molten pool of the second track are higher than those in the first, and with the increase of laser power, the length width ratio of the second track in molten pool becomes larger.
Selective laser melting
1.D.K. Pattanayak, A. Fukuda, T. Matsushita, M. Takemoto, S. Fujibayashi, K. Sasaki, N. Nishida, T. Nakamura, T. Kokubo, Acta Biomater. 7, 1398 (2011)CASArticleGoogle Scholar
6.E. Santos, K. Osakada, M. Shiomi, M. Morita, F. Abe, Fabrication of titanium dental implants by selective laser melting. in Proceedings of the 5th International Symposium on Laser Precision Microfabrication, Nara, 11–14 May 2004
28.Y.S. Lee, W. Zhang, Mesoscopic simulation of heat transfer and fluid flow in laser powder bed additive manufacturing. in Proceedings of 26th Solid Freeform Fabrication Symposium, Austin, 10-12 August 2015
47.Y.-C. Wu, C.-H. San, C.-H. Chang, H.-J. Lin, R. Marwan, S. Baba, W.-S. Hwang, J. Mater. Process. Tech. 254, 72 (2018)ArticleGoogle Scholar
48.B. Cheng, X. Li, C. Tuile, A. Ilin, H. Willeck, U. Hartel, Multi-physics modeling of single-track scanning in selective laser melting: powder compaction effect. in Proceedings of 29th Annual International Solid Freeform Fabrication Symposium-An Additive Manufacturing Conference, Austin, 13–15 August 2018
레이저 사인파 진동(사인) 용접 및 레이저 용접(SLW)에서 1.5mm 6061/5182 알루미늄 합금 박판 랩 조인트의 수치 모델이 온도 분포와 용융 흐름을 시뮬레이션하기 위해 개발되었습니다.
SLW의 일반적인 에너지 분포와 달리 레이저 빔의 사인파 진동은 에너지 분포를 크게 균질화하고 에너지 피크를 줄였습니다. 에너지 피크는 사인 용접의 양쪽에 위치하여 톱니 모양의 단면이 형성되었습니다. 이 논문은 시뮬레이션을 통해 응고 미세구조에 대한 온도 구배(G)와 응고 속도(R)의 영향을 설명했습니다.
결과는 사인 용접의 중심이 낮은 G/R로 더 넓은 영역을 가짐으로써 더 넓은 등축 결정립 영역의 형성을 촉진하고 더 큰 GR로 인해 주상 결정립이 더 가늘다는 것을 나타냅니다. 다공성 및 비관통 용접은 레이저 사인파 진동에 의해 얻어졌습니다.
그 이유는 용융 풀의 부피가 확대되고 열쇠 구멍의 부피 비율이 감소하며 용융 풀의 난류가 완만해졌기 때문이며, 이는 용융 흐름의 고속 이미징 및 시뮬레이션 결과에서 관찰되었습니다. 두 용접부의 인장시험에서 융착선을 따라 인장파괴 형태를 보였고 사인 용접부의 인장강도가 SLW 용접부보다 유의하게 우수하였습니다.
이는 등축 결정립 영역이 넓을수록 균열 경향이 감소하고 파단 위치에 근접한 입자 크기가 미세하기 때문입니다. 결함이 없고 우수한 용접은 신에너지 자동차 산업에 매우 중요합니다.
A numerical model of 1.5 mm 6061/5182 aluminum alloys thin sheets lap joints under laser sinusoidal oscillation (sine) welding and laser welding (SLW) weld was developed to simulate temperature distribution and melt flow. Unlike the common energy distribution of SLW, the sinusoidal oscillation of laser beam greatly homogenized the energy distribution and reduced the energy peak. The energy peaks were located at both sides of the sine weld, resulting in the tooth-shaped sectional formation. This paper illustrated the effect of the temperature gradient (G) and solidification rate (R) on the solidification microstructure by simulation. Results indicated that the center of the sine weld had a wider area with low G/R, promoting the formation of a wider equiaxed grain zone, and the columnar grains were slenderer because of greater GR. The porosity-free and non-penetration welds were obtained by the laser sinusoidal oscillation. The reasons were that the molten pool volume was enlarged, the volume proportion of keyhole was reduced and the turbulence in the molten pool was gentled, which was observed by the high-speed imaging and simulation results of melt flow. The tensile test of both welds showed a tensile fracture form along the fusion line, and the tensile strength of sine weld was significantly better than that of the SLW weld. This was because that the wider equiaxed grain area reduced the tendency of cracks and the finer grain size close to the fracture location. Defect-free and excellent welds are of great significance to the new energy vehicles industry.
Laser weldingSinusoidal oscillatingEnergy distributionNumerical simulationMolten pool flowGrain structure
적층 제조(AM) 기술은 복잡한 형상의 3D 부품을 쉽게 만들고 미세 구조 제어를 통해 재료 특성을 크게 제어할 수 있기 때문에 많은 관심을 받았습니다. PBF(Powderbed fusion) 방식의 AM 공정에서는 금속 분말을 레이저나 전자빔으로 녹이고 응고시키는 과정을 반복하여 3D 부품을 제작합니다.
일반적으로 응고 미세구조는 Hunt[Mater. 과학. 영어 65, 75(1984)]. 그러나 CET 이론이 일반 316L 스테인리스강에서도 높은 G와 R로 인해 PBF형 AM 공정에 적용될 수 있을지는 불확실하다.
본 연구에서는 미세구조와 응고 조건 간의 관계를 밝히기 위해 전자빔 조사에 의해 유도된 316L 강의 응고 미세구조를 분석하고 CtFD(Computational Thermal-Fluid Dynamics) 방법을 사용하여 고체/액체 계면에서의 응고 조건을 평가했습니다.
CET 이론과 반대로 높은 G 조건에서 등축 결정립이 종종 형성되는 것으로 밝혀졌다. CtFD 시뮬레이션은 약 400 mm s-1의 속도까지 유체 흐름이 있음을 보여 주며 수상 돌기의 파편 및 이동의 영향으로 등축 결정립이 형성됨을 시사했습니다.
Additive manufacturing（AM）technologies have attracted much attention because it enables us to build 3D parts with complicated geometry easily and control material properties significantly via the control of microstructures. In the powderbed fusion（PBF）type AM process, 3D parts are fabricated by repeating a process of melting and solidifying metal powders by laser or electron beams. In general, the solidification microstructures can be predicted from solidification conditions defined by the combination of temperature gradient G and solidification rate R on the basis of columnar-equiaxed transition（CET）theory proposed by Hunt [Mater. Sci. Eng. 65, 75（1984）]. However, it is unclear whether the CET theory can be applied to the PBF type AM process because of the high G and R, even for general 316L stainless steel. In this study, to reveal relationships between microstructures and solidification conditions, we have analyzed solidification microstructures of 316L steel induced by electronbeam irradiation and evaluated solidification conditions at the solid/liquid interface using a computational thermal-fluid dynamics （CtFD）method. It was found that equiaxed grains were often formed under high G conditions contrary to the CET theory. CtFD simulation revealed that there is a fluid flow up to a velocity of about 400 mm s-1, and suggested that equiaxed grains are formed owing to the effect of fragmentations and migrations of dendrites.
Additive Manufacturing, 316L Stainless Steel, Powder Bed Fusion, Electron Beam Melting, Computational Thermal Fluid Dynamics Simulation
1） M.C. Sow, T. De Terris, O. Castelnau, Z. Hamouche, F. Coste, R. Fabbro and P. Peyre: “Influence of beam diameter on Laser Powder
Bed Fusion（L-PBF）process”, Addit. Manuf. 36（2020）, 101532. 2） J.C. Simmons, X. Chen, A. Azizi, M.A. Daeumer, P.Y. Zavalij, G. Zhou and S.N. Schiffres: “Influence of processing and microstructure on the local and bulk thermal conductivity of selective laser melted 316L stainless steel”, Addit. Manuf. 32（2020）, 100996. 3） S. Dryepondt, P. Nandwana, P. Fernandez-Zelaia and F. List: “Microstructure and High Temperature Tensile properties of 316L Fabricated by Laser Powder-Bed Fusion”, Addit. Manuf. 37（2020）, 101723. 4） S.H. Sun, T. Ishimoto, K. Hagihara, Y. Tsutsumi, T. Hanawa and T. Nakano: “Excellent mechanical and corrosion properties of austenitic stainless steel with a unique crystallographic lamellar microstructure via selective laser melting”, Scr. Mater. 159（2019）, 89-93. 5） T. Ishimoto, S. Wu, Y. Ito, S.H. Sun, H. Amano and T. Nakano: “Crystallographic orientation control of 316L austenitic stainless steel via selective laser melting”, ISIJ Int. 60（2020）, 1758-1764. 6） T. Ishimoto, K. Hagihara, K. Hisamoto, S.H. Sun and T. Nakano: “Crystallographic texture control of beta-type Ti-15Mo-5Zr3Al alloy by selective laser melting for the development of novel implants with a biocompatible low Young’s modulus”, Scr. Mater. 132（2017）, 34-38. 7） X. Ding, Y. Koizumi, D. Wei and A. Chiba: “Effect of process parameters on melt pool geometry and microstructure development for electron beam melting of IN718: A systematic single bead analysis study”, Addit. Manuf. 26（2019）, 215-226. 8） K. Karayagiz, L. Johnson, R. Seede, V. Attari, B. Zhang, X. Huang, S. Ghosh, T. Duong, I. Karaman, A. Elwany and R. Arróyave: “Finite interface dissipation phase field modeling of Ni-Nb under additive manufacturing conditions”, Acta Mater. 185（2020）, 320-339. 9） M.M. Kirka, Y. Lee, D.A. Greeley, A. Okello, M.J. Goin, M.T. Pearce and R.R. Dehoff: “Strategy for Texture Management in Metals Additive Manufacturing”, JOM, 69（2017）, 523-531. 10） S.S. Babu, N. Raghavan, J. Raplee, S.J. Foster, C. Frederick, M. Haines, R. Dinwiddie, M.K. Kirka, A. Plotkowski, Y. Lee and R.R. Dehoff: “Additive Manufacturing of Nickel Superalloys: Opportunities for Innovation and Challenges Related to Qualification”, Metall. Mater. Trans. A. 49（2018）, 3764-3780. 11） M.R. Gotterbarm, A.M. Rausch and C. Körner: “Fabrication of Single Crystals through a μ-Helix Grain Selection Process during Electron Beam Metal Additive Manufacturing”, Metals, 10（2020）, 313. 12） J.D.D. Hunt: “Steady state columnar and equiaxed growth of dendrites and eutectic”, Mater. Sci. Eng. 65（1984）, 75-83. 13） S. Bontha, N.W. Klingbeil, P.A. Kobryn and H.L. Fraser: “Effects of process variables and size-scale on solidification microstructure in beam-based fabrication of bulky 3D structures”, Mater. Sci. Eng. A. 513-514（2009）, 311-318. 14） J. Gockel and J. Beuth: “Understanding Ti-6Al-4V microstructure control in additive manufacturing via process maps”, 24th Int. SFF Symp. – An Addit. Manuf. Conf. SFF 2013.（2013）, 666-674. 15） B. Schoinochoritis, D. Chantzis and K. Salonitis: “Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review”, Proc. of Instit. Mech. Eng., Part B: J. Eng. Manuf. 231（2017）, 96-117. 16）小泉雄一郎: “計算機シミュレーションを用いたAdditive Manufacturing プロセス最適化予測”, スマートプロセス学会誌， 8-4（2019）, 132-138. 17） Y. Zhao, Y. Koizumi, K. Aoyagi, D. Wei, K. Yamanaka and 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”, Addit. Manuf. 26（2019）, 202-214. 18） C. Tang, J.L. Tan and C.H. Wong: “A numerical investigation on the physical mechanisms of single track defects in selective laser melting”, Int. J. Heat Mass Transf. 126（2018）, 957-968. 19） Technical data for Iron, [Online]. Available: http://periodictable.com/ Elements/026/data.html. [Accessed: 8-Feb-2021]. 20） N. Raghavan, R. Dehoff, S. Pannala, S. Simunovic, M. Kirka, J. Turner, N. Carl-son and S.S. Babu: “Numerical modeling of heattransfer and the influence of process parameters on tailoring the grain morphology of IN718 in electron beam additive manufacturing”, Acta Mater. 112（2016）, 303-314. 21） S. Morita, Y. Miki and K. Toishi: “Introduction of Dendrite Fragmentation to Microstructure Calculation by Cellular Automaton Method”, Tetsu-to-Hagane. 104（2018）, 559-566. 22） H. Esaka and M. Tamura: “Model Experiment Using Succinonitrile on the Formation of Equiaxed Grains caused by Forced Convection”, Tetsu-to-Hagane. 86（2000）, 252-258.
17-4 PH 스테인리스강의 레이저 분말 베드 융합: 열처리가 미세조직의 진화 및 기계적 특성에 미치는 영향에 대한 비교 연구
panelS.Saboonia, A.Chaboka, S.Fenga,e, H.Blaauwb, T.C.Pijperb,c, H.J.Yangd, Y.T.Peia aDepartment of Advanced Production Engineering, Engineering and Technology Institute Groningen, University of Groningen, Nijenborgh 4, 9747 AG, Groningen, The Netherlands bPhilips Personal Care, Oliemolenstraat 5, 9203 ZN, Drachten, The Netherlands cInnovation Cluster Drachten, Nipkowlaan 5, 9207 JA, Drachten, The Netherlands dShi-changxu Innovation Center for Advanced Materials, Institute of Metal Research, Chinese Academy of Sciences, 72 Wenhua Road, Shenyang 110016, P. R. China eSchool of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, P.R. China
17-4 PH (precipitation hardening) stainless steel is commonly used for the fabrication of complicated molds with conformal cooling channels using laser powder bed fusion process (L-PBF). However, their microstructure in the as-printed condition varies notably with the chemical composition of the feedstock powder, resulting in different age-hardening behavior. In the present investigation, 17-4 PH stainless steel components were fabricated by L-PBF from two different feedstock powders, and subsequently subjected to different combinations of post-process heat treatments. It was observed that the microstructure in as-printed conditions could be almost fully martensitic or ferritic, depending on the ratio of Creq/Nieq of the feedstock powder. Aging treatment at 480 °C improved the yield and ultimate tensile strengths of the as-printed components. However, specimens with martensitic structures exhibited accelerated age-hardening response compared with the ferritic specimens due to the higher lattice distortion and dislocation accumulation, resulting in the “dislocation pipe diffusion mechanism”. It was also found that the martensitic structures were highly susceptible to the formation of reverted austenite during direct aging treatment, where 19.5% of austenite phase appeared in the microstructure after 15 h of direct aging. Higher fractions of reverted austenite activates the transformation induced plasticity and improves the ductility of heat treated specimens. The results of the present study can be used to tailor the microstructure of the L-PBF printed 17-4 PH stainless steel by post-process heat treatments to achieve a good combination of mechanical properties.
17-4 PH(석출 경화) 스테인리스강은 레이저 분말 베드 융합 공정(L-PBF)을 사용하여 등각 냉각 채널이 있는 복잡한 금형 제작에 일반적으로 사용됩니다. 그러나 인쇄된 상태의 미세 구조는 공급원료 분말의 화학적 조성에 따라 크게 달라지므로 시효 경화 거동이 다릅니다.
현재 조사에서 17-4 PH 스테인리스강 구성요소는 L-PBF에 의해 두 가지 다른 공급원료 분말로 제조되었으며, 이후에 다양한 조합의 후처리 열처리를 거쳤습니다. 인쇄된 상태의 미세구조는 공급원료 분말의 Creq/Nieq 비율에 따라 거의 완전히 마르텐사이트 또는 페라이트인 것으로 관찰되었습니다.
480 °C에서 노화 처리는 인쇄된 구성 요소의 수율과 극한 인장 강도를 개선했습니다. 그러나 마텐자이트 구조의 시편은 격자 변형 및 전위 축적이 높아 페라이트 시편에 비해 시효 경화 반응이 가속화되어 “전위 파이프 확산 메커니즘”이 발생합니다.
또한 마르텐사이트 구조는 직접 시효 처리 중에 복귀된 오스테나이트의 형성에 매우 민감한 것으로 밝혀졌으며, 여기서 15시간의 직접 시효 후 미세 조직에 19.5%의 오스테나이트 상이 나타났습니다.
복귀된 오스테나이트의 비율이 높을수록 변형 유도 가소성이 활성화되고 열처리된 시편의 연성이 향상됩니다. 본 연구의 결과는 기계적 특성의 우수한 조합을 달성하기 위해 후처리 열처리를 통해 L-PBF로 인쇄된 17-4 PH 스테인리스강의 미세 구조를 조정하는 데 사용할 수 있습니다.
Electromagnetic metal casting (EMC) is a casting technique that uses electromagnetic energy to heat metal powders. It is a faster, cleaner, and less time-consuming operation. Solid metals create issues in electromagnetics since they reflect the electromagnetic radiation rather than consume it—electromagnetic energy processing results in sounded pieces with higher-ranking material properties and a more excellent microstructure solution. For the physical production of the electromagnetic casting process, knowledge of electromagnetic material interaction is critical. Even where the heated material is an excellent electromagnetic absorber, the total heating quality is sometimes insufficient. Numerical modelling works on finding the proper coupled effects between properties to bring out the most effective operation. The main parameters influencing the quality of output of the EMC process are: power dissipated per unit volume into the material, penetration depth of electromagnetics, complex magnetic permeability and complex dielectric permittivity. The contact mechanism and interference pattern also, in turn, determines the quality of the process. Only a few parameters, such as the environment’s temperature, the interference pattern, and the rate of metal solidification, can be controlled by AI models. Neural networks are used to achieve exact outcomes by stimulating the neurons in the human brain. Additive manufacturing (AM) is used to design mold and cores for metal casting. The models outperformed the traditional DFA optimization approach, which is susceptible to local minima. The system works only offline, so real-time analysis and corrections are not yet possible.
전자기 금속 주조 (EMC)는 전자기 에너지를 사용하여 금속 분말을 가열하는 주조 기술입니다. 더 빠르고 깨끗하며 시간이 덜 소요되는 작업입니다.
고체 금속은 전자기 복사를 소비하는 대신 반사하기 때문에 전자기학에서 문제를 일으킵니다. 전자기 에너지 처리는 더 높은 등급의 재료 특성과 더 우수한 미세 구조 솔루션을 가진 사운드 조각을 만듭니다.
전자기 주조 공정의 물리적 생산을 위해서는 전자기 물질 상호 작용에 대한 지식이 중요합니다. 가열된 물질이 우수한 전자기 흡수재인 경우에도 전체 가열 품질이 때때로 불충분합니다. 수치 모델링은 가장 효과적인 작업을 이끌어 내기 위해 속성 간의 적절한 결합 효과를 찾는데 사용됩니다.
EMC 공정의 출력 품질에 영향을 미치는 주요 매개 변수는 단위 부피당 재료로 분산되는 전력, 전자기의 침투 깊이, 복합 자기 투과성 및 복합 유전율입니다. 접촉 메커니즘과 간섭 패턴 또한 공정의 품질을 결정합니다. 환경 온도, 간섭 패턴 및 금속 응고 속도와 같은 몇 가지 매개 변수 만 AI 모델로 제어 할 수 있습니다.
신경망은 인간 뇌의 뉴런을 자극하여 정확한 결과를 얻기 위해 사용됩니다. 적층 제조 (AM)는 금속 주조용 몰드 및 코어를 설계하는 데 사용됩니다. 모델은 로컬 최소값에 영향을 받기 쉬운 기존 DFA 최적화 접근 방식을 능가했습니다. 이 시스템은 오프라인에서만 작동하므로 실시간 분석 및 수정은 아직 불가능합니다.
2.E. Ghasali, A. Fazili, M. Alizadeh, K. Shirvanimoghaddam, T. Ebadzadeh, Evaluation of microstructure and mechanical properties of Al-TiC metal matrix composite prepared by conventional, electromagnetic and spark plasma sintering methods. Materials 10(11), 1255 (2017). https://doi.org/10.3390/ma10111255ADSArticleGoogle Scholar
19.V. Gangwar, S. Kumar, V. Singh, H. Singh, Effect of process parameters on hardness of AA-6063 in-situ electromagnetic casting by using taguchi method, in IOP Conference Series: Materials Science and Engineering, vol. 804(1) (IOP Publishing, 2020), p. 012019
22.K.L. Glass, D.M. Ashby, U.S. Patent No. 9050656. (U.S. Patent and Trademark Office, Washington, DC, 2015)
23.S. Verma, P. Gupta, S. Srivastava, S. Kumar, A. Anand, An overview: casting/melting of non ferrous metallic materials using domestic electromagnetic oven. J. Mater. Sci. Mech. Eng. 4(4), (2017). p-ISSN: 2393-9095; e-ISSN: 2393-9109
29.E.B. Ripley, J.A. Oberhaus, WWWeb search power page-melting and heat treating metals using electromagnetic heating-the potential of electromagnetic metal processing techniques for a wide variety of metals and alloys is. Ind. Heat. 72(5), 65–70 (2005)Google Scholar
30.J. Campbell, Complete Casting Handbook: Metal Casting Processes, Metallurgy, Techniques and Design (Butterworth-Heinemann, 2015)Google Scholar
31.B. Ravi, Metal Casting: Computer-Aided Design and Analysis, 1st edn. (PHI Learning Ltd, 2005)Google Scholar
34.J. Ha, P. Cleary, V. Alguine, T. Nguyen, Simulation of die filling in gravity die casting using SPH and MAGMAsoft, in Proceedings of 2nd International Conference on CFD in Minerals & Process Industries (1999) pp. 423–428
35.M. Sirviö, M. Woś, Casting directly from a computer model by using advanced simulation software FLOW-3D Cast Ž. Arch. Foundry Eng. 9(1), 79–82 (2009)Google Scholar
38.EKK, Inc. Metal Casting Simulation Software and Consulting Services, CAPCAST Brochure
39.P. Muenprasertdee, Solidification modeling of iron castings using SOLIDCast (2007)
40.CasCAE, CT-CasTest Inc. Oy, Kerava
41.E. Dominguez-Tortajada, J. Monzo-Cabrera, A. Diaz-Morcillo, Uniform electric field distribution in electromagnetic heating applicators by means of genetic algorithms optimization of dielectric multilayer structures. IEEE Trans. Electromagn. Theory Tech. 55(1), 85–91 (2007). https://doi.org/10.1109/tmtt.2006.886913ADSArticleGoogle Scholar
42.B. Warren, M.H. Awida, A.E. Fathy, Electromagnetic heating of metals. IET Electromagn. Antennas Propag. 6(2), 196–205 (2012)ArticleGoogle Scholar
51.H. Sekiguchi, Y. Mori, Steam plasma reforming using Electromagnetic discharge. Thin Solid Films 435, 44–48 (2003)ADSArticleGoogle Scholar
52.J. Sun, W. Wang, C. Zhao, Y. Zhang, C. Ma, Q. Yue, Study on the coupled effect of wave absorption and metal discharge generation under electromagnetic irradiation. Ind. Eng. Chem. Res. 53, 2042–2051 (2014)ArticleGoogle Scholar
65.M. Ferguson, R. Ak, Y.T. Lee, K.H. Law, Automatic localization of casting defects with convolutional neural networks, in 2017 IEEE International Conference on Big Data (Big Data) (Boston, MA, USA, 2017), pp. 1726–1735. https://doi.org/10.1109/BigData.2017.8258115.
69.E. Mares, J. Sokolowski, Artificial intelligence-based control system for the analysis of metal casting properties. J. Achiev. Mater. Manuf. Eng. 40, 149–154 (2010)Google Scholar
70.K.S. Senthil, S. Muthukumaran, C. Chandrasekhar Reddy, Suitability of friction welding of tube to tube plate using an external tool process for different tube diameters—a study. Exp. Tech. 37(6), 8–14 (2013)ArticleGoogle Scholar
74.C.V. Kumar, S. Muthukumaran, A. Pradeep, S.S. Kumaran, Optimizational study of friction welding of steel tube to aluminum tube plate using an external tool process. Int. J. Mech. Mater. Eng. 6(2), 300–306 (2011)Google Scholar
84.M.P. Reddy, R.A. Shakoor, G. Parande, V. Manakari, F. Ubaid, A.M.A. Mohamed, M. Gupta, Enhanced performance of nano-sized SiC reinforced Al metal matrix nanocomposites synthesized through electromagnetic sintering and hot extrusion techniques. Prog. Nat. Sci. Mater. Int. 27(5), 606–614 (2017). https://doi.org/10.1016/j.pnsc.2017.08.015ArticleGoogle Scholar
Surface roughness of laser powder bed fusion (L-PBF) printed overhang regions is a major contributor to deteriorated shape accuracy/surface quality. This study investigates the mechanisms behind the evolution of surface roughness (Ra) in overhang regions. The evolution of surface morphology is the result of a combination of border track contour, powder adhesion, warp deformation, and dross formation, which is strongly related to the overhang angle (θ). When 0° ≤ θ ≤ 15°, the overhang angle does not affect Ra significantly since only a small area of the melt pool boundaries contacts the powder bed resulting in slight powder adhesion. When 15° < θ ≤ 50°, powder adhesion is enhanced by the melt pool sinking and the increased contact area between the melt pool boundary and powder bed. When θ > 50°, large waviness of the overhang contour, adhesion of powder clusters, severe warp deformation and dross formation increase Ra sharply.
레이저 파우더 베드 퓨전 (L-PBF) 프린팅 오버행 영역의 표면 거칠기는 형상 정확도 / 표면 품질 저하의 주요 원인입니다. 이 연구 는 오버행 영역에서 표면 거칠기 (Ra ) 의 진화 뒤에 있는 메커니즘을 조사합니다 . 표면 형태의 진화는 오버행 각도 ( θ ) 와 밀접한 관련이있는 경계 트랙 윤곽, 분말 접착, 뒤틀림 변형 및 드로스 형성의 조합의 결과입니다 . 0° ≤ θ ≤ 15° 인 경우 , 용융풀 경계의 작은 영역 만 분말 베드와 접촉하여 약간의 분말 접착이 발생하기 때문에 오버행 각도가 R a에 큰 영향을 주지 않습니다 . 15° < θ 일 때 ≤ 50°, 용융 풀 싱킹 및 용융 풀 경계와 분말 베드 사이의 증가된 접촉 면적으로 분말 접착력이 향상됩니다. θ > 50° 일 때 오버행 윤곽의 큰 파형, 분말 클러스터의 접착, 심한 휨 변형 및 드 로스 형성이 Ra 급격히 증가 합니다.
KEYWORDS: Laser powder bed fusion (L-PBF), melt pool dynamics, overhang region, shape deviation, surface roughness
레이저 분말 베드 융합 (L-PBF)은 첨단 적층 제조 (AM) 기술로, 집중된 레이저 빔을 사용하여 금속 분말을 선택적으로 융합하여 슬라이스 된 3D 컴퓨터 지원에 따라 층별로 3 차원 (3D) 금속 부품을 구축합니다. 설계 (CAD) 모델 (Chatham, Long 및 Williams 2019 ; Tan, Zhu 및 Zhou 2020 ). 재료가 인쇄 층 아래에 존재하는지 여부에 따라 인쇄 영역은 각각 솔리드 영역 또는 돌출 영역으로 분류 될 수 있습니다. 따라서 오버행 영역은 고체 기판이 아니라 분말 베드 바로 위에 건설되는 특수 구조입니다 (Patterson, Messimer 및 Farrington 2017). 오버행 영역은지지 구조를 포함하거나 포함하지 않고 구축 할 수 있으며, 지지대가있는 돌출 영역의 L-PBF는 지지체가 더 낮은 밀도로 구축된다는 점을 제외 하고 (Wang and Chou 2018 ) 고체 기판의 공정과 유사합니다 (따라서 기계적 강도가 낮기 때문에 L-PBF 공정 후 기계적으로 쉽게 제거 할 수 있습니다. 따라서지지 구조로 인쇄 된 오버행 영역은 L-PBF 공정 후 지지물 제거, 연삭 및 연마와 같은 추가 후 처리 단계가 필요합니다.
수평 내부 채널의 제작과 같은 일부 특정 경우에는 공정 후 지지대를 제거하기가 어려우므로 채널 상단 절반의 돌출부 영역을 지지대없이 건설해야합니다 (Hopkinson and Dickens 2000 ). 수평 내부 채널에 사용할 수없는지지 구조 외에도 내부 표면, 특히 등각 냉각 채널 (Feng, Kamat 및 Pei 2021 ) 에서 발생하는 복잡한 3D 채널 네트워크의 경우 표면 마감 프로세스를 구현하는 것도 어렵습니다 . 결과적으로 오버행 영역은 (i) 잔류 응력에 의한 변형, (ii) 계단 효과 (Kuo et al. 2020 ; Li et al. 2020 )로 인해 설계된 모양에서 벗어날 수 있습니다 .) 및 (iii) 원하지 않는 분말 소결로 인한 향상된 표면 거칠기; 여기서, 앞의 두 요소는 일반적으로 mm 길이 스케일에서 ‘매크로’편차로 분류되고 후자는 일반적으로 µm 길이 스케일에서 ‘마이크로’편차로 인식됩니다.
열 응력에 의한 변형은 오버행 영역에서 발생하는 중요한 문제입니다 (Patterson, Messimer 및 Farrington 2017 ). 국부적 인 용융 / 냉각은 용융 풀 내부 및 주변에서 큰 온도 구배를 유도하여 응고 된 층에 집중적 인 열 응력을 유발합니다. 열 응력에 의한 뒤틀림은 고체 영역을 현저하게 변형하지 않습니다. 이러한 영역은 아래의 여러 레이어에 의해 제한되기 때문입니다. 반면에 오버행 영역은 구속되지 않고 공정 중 응력 완화로 인해 상당한 변형이 발생합니다 (Kamat 및 Pei 2019 ). 더욱이 용융 깊이는 레이어 두께보다 큽니다 (이전 레이어도 재용 해되어 빌드 된 레이어간에 충분한 결합을 보장하기 때문입니다 [Yadroitsev et al. 2013 ; Kamath et al.2014 ]),응고 된 두께가 설계된 두께보다 크기 때문에형태 편차 (예 : 드 로스 [Charles et al. 2020 ; Feng et al. 2020 ])가 발생합니다. 마이크로 스케일에서 인쇄 된 표면 (R a 및 S a ∼ 10 μm)은 기계적으로 가공 된 표면보다 거칠다 (Duval-Chaneac et al. 2018 ; Wen et al. 2018 ). 이 문제는고형화 된 용융 풀의 가장자리에 부착 된 용융되지 않은 분말의 결과로 표면 거칠기 (R a )가 일반적으로 약 20 μm인 오버행 영역에서 특히 심각합니다 (Mazur et al. 2016 ; Pakkanen et al. 2016 ).
오버행 각도 ( θ , 빌드 방향과 관련하여 측정)는 오버행 영역의 뒤틀림 편향과 표면 거칠기에 영향을 미치는 중요한 매개 변수입니다 (Kamat and Pei 2019 ; Mingear et al. 2019 ). θ ∼ 45 ° 의 오버행 각도 는 일반적으로지지 구조없이 오버행 영역을 인쇄 할 수있는 임계 값으로 합의됩니다 (Pakkanen et al. 2016 ; Kadirgama et al. 2018 ). θ 일 때이 임계 값보다 크면 오버행 영역을 허용 가능한 표면 품질로 인쇄 할 수 없습니다. 오버행 각도 외에도 레이저 매개 변수 (레이저 에너지 밀도와 관련된)는 용융 풀의 모양 / 크기 및 용융 풀 역학에 영향을줌으로써 오버행 영역의 표면 거칠기에 영향을줍니다 (Wang et al. 2013 ; Mingear et al . 2019 ).
용융 풀 역학은 고체 (Shrestha 및 Chou 2018 ) 및 오버행 (Le et al. 2020 ) 영역 모두에서 수행되는 L-PBF 공정을 포함한 레이저 재료 가공의 일반적인 물리적 현상입니다 . 용융 풀 모양, 크기 및 냉각 속도는 잔류 응력으로 인한 변형과 표면 거칠기에 모두 영향을 미치므로 처리 매개 변수와 표면 형태 / 품질 사이의 다리 역할을하며 용융 풀을 이해하기 위해 수치 시뮬레이션을 사용하여 추가 조사를 수행 할 수 있습니다. 거동과 표면 거칠기에 미치는 영향. 현재까지 고체 영역의 L-PBF 동안 용융 풀 동작을 시뮬레이션하기 위해 여러 연구가 수행되었습니다. 유한 요소 방법 (FEM)과 같은 시뮬레이션 기술 (Roberts et al. 2009 ; Du et al.2019 ), 유한 차분 법 (FDM) (Wu et al. 2018 ), 전산 유체 역학 (CFD) (Lee and Zhang 2016 ), 임의의 Lagrangian-Eulerian 방법 (ALE) (Khairallah and Anderson 2014 )을 사용하여 증발 반동 압력 (Hu et al. 2018 ) 및 Marangoni 대류 (Zhang et al. 2018 ) 현상을포함하는 열 전달 (온도 장) 및 물질 전달 (용융 흐름) 프로세스. 또한 이산 요소법 (DEM)을 사용하여 무작위 분산 분말 베드를 생성했습니다 (Lee and Zhang 2016 ; Wu et al. 2018 ). 이 모델은 분말 규모의 L-PBF 공정을 시뮬레이션했습니다 (Khairallah et al. 2016) 메조 스케일 (Khairallah 및 Anderson 2014 ), 단일 트랙 (Leitz et al. 2017 )에서 다중 트랙 (Foroozmehr et al. 2016 ) 및 다중 레이어 (Huang, Khamesee 및 Toyserkani 2019 )로.
그러나 결과적인 표면 거칠기를 결정하는 오버행 영역의 용융 풀 역학은 문헌에서 거의 관심을받지 못했습니다. 솔리드 영역의 L-PBF에 대한 기존 시뮬레이션 모델이 어느 정도 참조가 될 수 있지만 오버행 영역과 솔리드 영역 간의 용융 풀 역학에는 상당한 차이가 있습니다. 오버행 영역에서 용융 금속은 분말 입자 사이의 틈새로 아래로 흘러 용융 풀이 다공성 분말 베드가 제공하는 약한 지지체 아래로 가라 앉습니다. 이것은 중력과 표면 장력의 영향이 용융 풀의 결과적인 모양 / 크기를 결정하는 데 중요하며, 결과적으로 오버행 영역의 마이크로 스케일 형태의 진화에 중요합니다. 또한 분말 입자 사이의 공극, 열 조건 (예 : 에너지 흡수,2019 ; Karimi et al. 2020 ; 노래와 영 2020 ). 표면 거칠기는 (마이크로) 형상 편차를 증가시킬뿐만 아니라 주기적 하중 동안 미세 균열의 시작 지점 역할을함으로써 기계적 강도를 저하시킵니다 (Günther et al. 2018 ). 오버행 영역의 높은 표면 거칠기는 (마이크로) 정확도 / 품질에 대한 엄격한 요구 사항이있는 부품 제조에서 L-PBF의 적용을 제한합니다.
본 연구는 실험 및 시뮬레이션 연구를 사용하여 오버행 영역 (지지물없이 제작)의 미세 형상 편차 형성 메커니즘과 표면 거칠기의 기원을 체계적이고 포괄적으로 조사합니다. 결합 된 DEM-CFD 시뮬레이션 모델은 경계 트랙 윤곽, 분말 접착 및 뒤틀림 변형의 효과를 고려하여 오버행 영역의 용융 풀 역학과 표면 형태의 형성 메커니즘을 나타 내기 위해 개발되었습니다. 표면 거칠기 R의 시뮬레이션 및 단일 요인 L-PBF 인쇄 실험을 사용하여 오버행 각도의 함수로 연구됩니다. 용융 풀의 침몰과 관련된 오버행 영역에서 분말 접착의 세 가지 메커니즘이 식별되고 자세히 설명됩니다. 마지막으로, 인쇄 된 오버행 영역에서 높은 표면 거칠기 문제를 완화 할 수 있는 잠재적 솔루션에 대해 간략하게 설명합니다.
Cai, Chao, Chrupcala Radoslaw, Jinliang Zhang, Qian Yan, Shifeng Wen, Bo Song, and Yusheng Shi. 2019. “In-Situ Preparation and Formation of TiB/Ti-6Al-4V Nanocomposite via Laser Additive Manufacturing: Microstructure Evolution and Tribological Behavior.” Powder Technology 342: 73–84. doi:10.1016/j.powtec.2018.09.088. [Crossref], [Web of Science ®], [Google Scholar]
Cai, Chao, Wei Shian Tey, Jiayao Chen, Wei Zhu, Xingjian Liu, Tong Liu, Lihua Zhao, and Kun Zhou. 2021. “Comparative Study on 3D Printing of Polyamide 12 by Selective Laser Sintering and Multi Jet Fusion.” Journal of Materials Processing Technology 288 (August 2020): 116882. doi:10.1016/j.jmatprotec.2020.116882. [Crossref], [Web of Science ®], [Google Scholar]
Cai, Chao, Xu Wu, Wan Liu, Wei Zhu, Hui Chen, Jasper Dong Qiu Chua, Chen Nan Sun, Jie Liu, Qingsong Wei, and Yusheng Shi. 2020. “Selective Laser Melting of Near-α Titanium Alloy Ti-6Al-2Zr-1Mo-1V: Parameter Optimization, Heat Treatment and Mechanical Performance.” Journal of Materials Science and Technology 57: 51–64. doi:10.1016/j.jmst.2020.05.004. [Crossref], [Web of Science ®], [Google Scholar]
Charles, Amal, Ahmed Elkaseer, Lore Thijs, and Steffen G. Scholz. 2020. “Dimensional Errors Due to Overhanging Features in Laser Powder Bed Fusion Parts Made of Ti-6Al-4V.” Applied Sciences 10 (7): 2416. doi:10.3390/app10072416. [Crossref], [Google Scholar]
Chatham, Camden A., Timothy E. Long, and Christopher B. Williams. 2019. “A Review of the Process Physics and Material Screening Methods for Polymer Powder Bed Fusion Additive Manufacturing.” Progress in Polymer Science 93: 68–95. doi:10.1016/j.progpolymsci.2019.03.003. [Crossref], [Web of Science ®], [Google Scholar]
Du, Yang, Xinyu You, Fengbin Qiao, Lijie Guo, and Zhengwu Liu. 2019. “A Model for Predicting the Temperature Field during Selective Laser Melting.” Results in Physics 12 (November 2018): 52–60. doi:10.1016/j.rinp.2018.11.031. [Crossref], [Web of Science ®], [Google Scholar]
Duval-Chaneac, M. S., S. Han, C. Claudin, F. Salvatore, J. Bajolet, and J. Rech. 2018. “Experimental Study on Finishing of Internal Laser Melting (SLM) Surface with Abrasive Flow Machining (AFM).” Precision Engineering 54 (July 2017): 1–6. doi:10.1016/j.precisioneng.2018.03.006. [Crossref], [Web of Science ®], [Google Scholar]
Feng, Shaochuan, Shijie Chen, Amar M. Kamat, Ru Zhang, Mingji Huang, and Liangcai Hu. 2020. “Investigation on Shape Deviation of Horizontal Interior Circular Channels Fabricated by Laser Powder Bed Fusion.” Additive Manufacturing 36 (December): 101585. doi:10.1016/j.addma.2020.101585. [Crossref], [Web of Science ®], [Google Scholar]
Feng, Shaochuan, Chuanzhen Huang, Jun Wang, Hongtao Zhu, Peng Yao, and Zhanqiang Liu. 2017. “An Analytical Model for the Prediction of Temperature Distribution and Evolution in Hybrid Laser-Waterjet Micro-Machining.” Precision Engineering 47: 33–45. doi:10.1016/j.precisioneng.2016.07.002. [Crossref], [Web of Science ®], [Google Scholar]
Feng, Shaochuan, Amar M. Kamat, and Yutao Pei. 2021. “Design and Fabrication of Conformal Cooling Channels in Molds: Review and Progress Updates.” International Journal of Heat and Mass Transfer. doi:10.1016/j.ijheatmasstransfer.2021.121082. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Flow-3D V11.2 Documentation. 2016. Flow Science, Inc. [Crossref], [Google Scholar]
Foroozmehr, Ali, Mohsen Badrossamay, Ehsan Foroozmehr, and Sa’id Golabi. 2016. “Finite Element Simulation of Selective Laser Melting Process Considering Optical Penetration Depth of Laser in Powder Bed.” Materials and Design 89: 255–263. doi:10.1016/j.matdes.2015.10.002. [Crossref], [Web of Science ®], [Google Scholar]
“Geometrical Product Specifications (GPS) — Surface Texture: Profile Method — Rules and Procedures for the Assessment of Surface Texture (ISO 4288).” 1996. International Organization for Standardization. https://www.iso.org/standard/2096.html. [Google Scholar]
Günther, Johannes, Stefan Leuders, Peter Koppa, Thomas Tröster, Sebastian Henkel, Horst Biermann, and Thomas Niendorf. 2018. “On the Effect of Internal Channels and Surface Roughness on the High-Cycle Fatigue Performance of Ti-6Al-4V Processed by SLM.” Materials & Design 143: 1–11. doi:10.1016/j.matdes.2018.01.042. [Crossref], [Web of Science ®], [Google Scholar]
Hopkinson, Neil, and Phill Dickens. 2000. “Conformal Cooling and Heating Channels Using Laser Sintered Tools.” In Solid Freeform Fabrication Conference, 490–497. Texas. doi:10.26153/tsw/3075. [Crossref], [Google Scholar]
Hu, Zhiheng, Haihong Zhu, Changchun Zhang, Hu Zhang, Ting Qi, and Xiaoyan Zeng. 2018. “Contact Angle Evolution during Selective Laser Melting.” Materials and Design 139: 304–313. doi:10.1016/j.matdes.2017.11.002. [Crossref], [Web of Science ®], [Google Scholar]
Hu, Cheng, Kejia Zhuang, Jian Weng, and Donglin Pu. 2019. “Three-Dimensional Analytical Modeling of Cutting Temperature for Round Insert Considering Semi-Infinite Boundary and Non-Uniform Heat Partition.” International Journal of Mechanical Sciences 155 (October 2018): 536–553. doi:10.1016/j.ijmecsci.2019.03.019. [Crossref], [Web of Science ®], [Google Scholar]
Huang, Yuze, Mir Behrad Khamesee, and Ehsan Toyserkani. 2019. “A New Physics-Based Model for Laser Directed Energy Deposition (Powder-Fed Additive Manufacturing): From Single-Track to Multi-Track and Multi-Layer.” Optics & Laser Technology 109 (August 2018): 584–599. doi:10.1016/j.optlastec.2018.08.015. [Crossref], [Web of Science ®], [Google Scholar]
Kadirgama, K., W. S. W. Harun, F. Tarlochan, M. Samykano, D. Ramasamy, Mohd Zaidi Azir, and H. Mehboob. 2018. “Statistical and Optimize of Lattice Structures with Selective Laser Melting (SLM) of Ti6AL4V Material.” International Journal of Advanced Manufacturing Technology 97 (1–4): 495–510. doi:10.1007/s00170-018-1913-1. [Crossref], [Web of Science ®], [Google Scholar]
Kamat, Amar M, and Yutao Pei. 2019. “An Analytical Method to Predict and Compensate for Residual Stress-Induced Deformation in Overhanging Regions of Internal Channels Fabricated Using Powder Bed Fusion.” Additive Manufacturing 29 (March): 100796. doi:10.1016/j.addma.2019.100796. [Crossref], [Web of Science ®], [Google Scholar]
Kamath, Chandrika, Bassem El-Dasher, Gilbert F. Gallegos, Wayne E. King, and Aaron Sisto. 2014. “Density of Additively-Manufactured, 316L SS Parts Using Laser Powder-Bed Fusion at Powers up to 400 W.” International Journal of Advanced Manufacturing Technology 74 (1–4): 65–78. doi:10.1007/s00170-014-5954-9. [Crossref], [Web of Science ®], [Google Scholar]
Karimi, J., C. Suryanarayana, I. Okulov, and K. G. Prashanth. 2020. “Selective Laser Melting of Ti6Al4V: Effect of Laser Re-Melting.” Materials Science and Engineering A (July): 140558. doi:10.1016/j.msea.2020.140558. [Crossref], [Web of Science ®], [Google Scholar]
Khairallah, Saad A., and Andy Anderson. 2014. “Mesoscopic Simulation Model of Selective Laser Melting of Stainless Steel Powder.” Journal of Materials Processing Technology 214 (11): 2627–2636. doi:10.1016/j.jmatprotec.2014.06.001. [Crossref], [Web of Science ®], [Google Scholar]
Khairallah, Saad A., Andrew T. Anderson, Alexander Rubenchik, and Wayne E. King. 2016. “Laser Powder-Bed Fusion Additive Manufacturing: Physics of Complex Melt Flow and Formation Mechanisms of Pores, Spatter, and Denudation Zones.” Edited by Adedeji B. Badiru, Vhance V. Valencia, and David Liu. Acta Materialia 108 (April): 36–45. doi:10.1016/j.actamat.2016.02.014. [Crossref], [Web of Science ®], [Google Scholar]
Kuo, C. N., C. K. Chua, P. C. Peng, Y. W. Chen, S. L. Sing, S. Huang, and Y. L. Su. 2020. “Microstructure Evolution and Mechanical Property Response via 3D Printing Parameter Development of Al–Sc Alloy.” Virtual and Physical Prototyping 15 (1): 120–129. doi:10.1080/17452759.2019.1698967. [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
Le, K. Q., C. H. Wong, K. H. G. Chua, C. Tang, and H. Du. 2020. “Discontinuity of Overhanging Melt Track in Selective Laser Melting Process.” International Journal of Heat and Mass Transfer 162 (December): 120284. doi:10.1016/j.ijheatmasstransfer.2020.120284. [Crossref], [Web of Science ®], [Google Scholar]
Lee, Y. S., and W. Zhang. 2016. “Modeling of Heat Transfer, Fluid Flow and Solidification Microstructure of Nickel-Base Superalloy Fabricated by Laser Powder Bed Fusion.” Additive Manufacturing 12: 178–188. doi:10.1016/j.addma.2016.05.003. [Crossref], [Web of Science ®], [Google Scholar]
Leitz, K. H., P. Singer, A. Plankensteiner, B. Tabernig, H. Kestler, and L. S. Sigl. 2017. “Multi-Physical Simulation of Selective Laser Melting.” Metal Powder Report 72 (5): 331–338. doi:10.1016/j.mprp.2016.04.004. [Crossref], [Google Scholar]
Li, Jian, Jing Hu, Yi Zhu, Xiaowen Yu, Mengfei Yu, and Huayong Yang. 2020. “Surface Roughness Control of Root Analogue Dental Implants Fabricated Using Selective Laser Melting.” Additive Manufacturing 34 (September 2019): 101283. doi:10.1016/j.addma.2020.101283. [Crossref], [Web of Science ®], [Google Scholar]
Li, Yingli, Kun Zhou, Pengfei Tan, Shu Beng Tor, Chee Kai Chua, and Kah Fai Leong. 2018. “Modeling Temperature and Residual Stress Fields in Selective Laser Melting.” International Journal of Mechanical Sciences 136 (February): 24–35. doi:10.1016/j.ijmecsci.2017.12.001. [Crossref], [Web of Science ®], [Google Scholar]
Mazur, MacIej, Martin Leary, Matthew McMillan, Joe Elambasseril, and Milan Brandt. 2016. “SLM Additive Manufacture of H13 Tool Steel with Conformal Cooling and Structural Lattices.” Rapid Prototyping Journal 22 (3): 504–518. doi:10.1108/RPJ-06-2014-0075. [Crossref], [Web of Science ®], [Google Scholar]
Mingear, Jacob, Bing Zhang, Darren Hartl, and Alaa Elwany. 2019. “Effect of Process Parameters and Electropolishing on the Surface Roughness of Interior Channels in Additively Manufactured Nickel-Titanium Shape Memory Alloy Actuators.” Additive Manufacturing 27 (October 2018): 565–575. doi:10.1016/j.addma.2019.03.027. [Crossref], [Web of Science ®], [Google Scholar]
Pakkanen, Jukka, Flaviana Calignano, Francesco Trevisan, Massimo Lorusso, Elisa Paola Ambrosio, Diego Manfredi, and Paolo Fino. 2016. “Study of Internal Channel Surface Roughnesses Manufactured by Selective Laser Melting in Aluminum and Titanium Alloys.” Metallurgical and Materials Transactions A 47 (8): 3837–3844. doi:10.1007/s11661-016-3478-7. [Crossref], [Web of Science ®], [Google Scholar]
Patterson, Albert E., Sherri L. Messimer, and Phillip A. Farrington. 2017. “Overhanging Features and the SLM/DMLS Residual Stresses Problem: Review and Future Research Need.” Technologies 5 (4): 15. doi:10.3390/technologies5020015. [Crossref], [Web of Science ®], [Google Scholar]
Roberts, I. A., C. J. Wang, R. Esterlein, M. Stanford, and D. J. Mynors. 2009. “A Three-Dimensional Finite Element Analysis of the Temperature Field during Laser Melting of Metal Powders in Additive Layer Manufacturing.” International Journal of Machine Tools and Manufacture 49 (12–13): 916–923. doi:10.1016/j.ijmachtools.2009.07.004. [Crossref], [Web of Science ®], [Google Scholar]
Shrestha, Subin, and Kevin Chou. 2018. “Computational Analysis of Thermo-Fluid Dynamics with Metallic Powder in SLM.” In CFD Modeling and Simulation in Materials Processing 2018, edited by Laurentiu Nastac, Koulis Pericleous, Adrian S. Sabau, Lifeng Zhang, and Brian G. Thomas, 85–95. Cham, Switzerland: Springer Nature. doi:10.1007/978-3-319-72059-3_9. [Crossref], [Google Scholar]
Sing, S. L., and W. Y. Yeong. 2020. “Laser Powder Bed Fusion for Metal Additive Manufacturing: Perspectives on Recent Developments.” Virtual and Physical Prototyping 15 (3): 359–370. doi:10.1080/17452759.2020.1779999. [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
Šmilauer, Václav, Emanuele Catalano, Bruno Chareyre, Sergei Dorofeenko, Jérôme Duriez, Nolan Dyck, Jan Eliáš, et al. 2015. Yade Documentation. 2nd ed. The Yade Project. doi:10.5281/zenodo.34073. [Crossref], [Google Scholar]
Tan, Pengfei, Fei Shen, Biao Li, and Kun Zhou. 2019. “A Thermo-Metallurgical-Mechanical Model for Selective Laser Melting of Ti6Al4V.” Materials & Design 168 (April): 107642. doi:10.1016/j.matdes.2019.107642. [Crossref], [Web of Science ®], [Google Scholar]
Tan, Lisa Jiaying, Wei Zhu, and Kun Zhou. 2020. “Recent Progress on Polymer Materials for Additive Manufacturing.” Advanced Functional Materials 30 (43): 1–54. doi:10.1002/adfm.202003062. [Crossref], [Web of Science ®], [Google Scholar]
Wang, Xiaoqing, and Kevin Chou. 2018. “Effect of Support Structures on Ti-6Al-4V Overhang Parts Fabricated by Powder Bed Fusion Electron Beam Additive Manufacturing.” Journal of Materials Processing Technology 257 (February): 65–78. doi:10.1016/j.jmatprotec.2018.02.038. [Crossref], [Web of Science ®], [Google Scholar]
Wang, Di, Yongqiang Yang, Ziheng Yi, and Xubin Su. 2013. “Research on the Fabricating Quality Optimization of the Overhanging Surface in SLM Process.” International Journal of Advanced Manufacturing Technology 65 (9–12): 1471–1484. doi:10.1007/s00170-012-4271-4. [Crossref], [Web of Science ®], [Google Scholar]
Wen, Peng, Maximilian Voshage, Lucas Jauer, Yanzhe Chen, Yu Qin, Reinhart Poprawe, and Johannes Henrich Schleifenbaum. 2018. “Laser Additive Manufacturing of Zn Metal Parts for Biodegradable Applications: Processing, Formation Quality and Mechanical Properties.” Materials and Design 155: 36–45. doi:10.1016/j.matdes.2018.05.057. [Crossref], [Web of Science ®], [Google Scholar]
Wu, Yu-che, Cheng-hung San, Chih-hsiang Chang, Huey-jiuan Lin, Raed Marwan, Shuhei Baba, and Weng-Sing Hwang. 2018. “Numerical Modeling of Melt-Pool Behavior in Selective Laser Melting with Random Powder Distribution and Experimental Validation.” Journal of Materials Processing Technology 254 (November 2017): 72–78. doi:10.1016/j.jmatprotec.2017.11.032. [Crossref], [Web of Science ®], [Google Scholar]
Yadroitsev, I., P. Krakhmalev, I. Yadroitsava, S. Johansson, and I. Smurov. 2013. “Energy Input Effect on Morphology and Microstructure of Selective Laser Melting Single Track from Metallic Powder.” Journal of Materials Processing Technology 213 (4): 606–613. doi:10.1016/j.jmatprotec.2012.11.014. [Crossref], [Web of Science ®], [Google Scholar]
Yu, Wenhui, Swee Leong Sing, Chee Kai Chua, and Xuelei Tian. 2019. “Influence of Re-Melting on Surface Roughness and Porosity of AlSi10Mg Parts Fabricated by Selective Laser Melting.” Journal of Alloys and Compounds 792: 574–581. doi:10.1016/j.jallcom.2019.04.017. [Crossref], [Web of Science ®], [Google Scholar]
Zhang, Dongyun, Pudan Zhang, Zhen Liu, Zhe Feng, Chengjie Wang, and Yanwu Guo. 2018. “Thermofluid Field of Molten Pool and Its Effects during Selective Laser Melting (SLM) of Inconel 718 Alloy.” Additive Manufacturing 21 (100): 567–578. doi:10.1016/j.addma.2018.03.031. [Crossref], [Web of Science ®], [Google Scholar]
The use of functionalized magnetic particles for the detection or separation of multiple chemicals and biomolecules from biofluids continues to attract significant attention. After their incubation with the targeted substances, the beads can be magnetically recovered to perform analysis or diagnostic tests. Particle recovery with permanent magnets in continuous-flow microdevices has gathered great attention in the last decade due to the multiple advantages of microfluidics. As such, great efforts have been made to determine the magnetic and fluidic conditions for achieving complete particle capture; however, less attention has been paid to the effect of the channel geometry on the system performance, although it is key for designing systems that simultaneously provide high particle recovery and flow rates. Herein, we address the optimization of Y-Y-shaped microchannels, where magnetic beads are separated from blood and collected into a buffer stream by applying an external magnetic field. The influence of several geometrical features (namely cross section shape, thickness, length, and volume) on both bead recovery and system throughput is studied. For that purpose, we employ an experimentally validated Computational Fluid Dynamics (CFD) numerical model that considers the dominant forces acting on the beads during separation. Our results indicate that rectangular, long devices display the best performance as they deliver high particle recovery and high throughput. Thus, this methodology could be applied to the rational design of lab-on-a-chip devices for any magnetically driven purification, enrichment or isolation.
생체 유체에서 여러 화학 물질과 생체 분자의 검출 또는 분리를위한 기능화 된 자성 입자의 사용은 계속해서 상당한 관심을 받고 있습니다. 표적 물질과 함께 배양 한 후 비드를 자기 적으로 회수하여 분석 또는 진단 테스트를 수행 할 수 있습니다. 연속 흐름 마이크로 장치에서 영구 자석을 사용한 입자 회수는 마이크로 유체의 여러 장점으로 인해 지난 10 년 동안 큰 관심을 모았습니다.
따라서 완전한 입자 포획을 달성하기 위한 자기 및 유체 조건을 결정하기 위해 많은 노력을 기울였습니다. 그러나 높은 입자 회수율과 유속을 동시에 제공하는 시스템을 설계하는 데있어 핵심이기는 하지만 시스템 성능에 대한 채널 형상의 영향에 대해서는 덜주의를 기울였습니다.
여기에서 우리는 자기 비드가 혈액에서 분리되고 외부 자기장을 적용하여 버퍼 스트림으로 수집되는 YY 모양의 마이크로 채널의 최적화를 다룹니다. 비드 회수 및 시스템 처리량에 대한 여러 기하학적 특징 (즉, 단면 형상, 두께, 길이 및 부피)의 영향을 연구합니다.
이를 위해 분리 중에 비드에 작용하는 지배적인 힘을 고려하는 실험적으로 검증 된 CFD (Computational Fluid Dynamics) 수치 모델을 사용합니다. 우리의 결과는 직사각형의 긴 장치가 높은 입자 회수율과 높은 처리량을 제공하기 때문에 최고의 성능을 보여줍니다.
따라서 이 방법론은 자기 구동 정제, 농축 또는 분리를 위한 랩온어 칩 장치의 합리적인 설계에 적용될 수 있습니다.
Gómez-Pastora, J.; Xue, X.; Karampelas, I.H.; Bringas, E.; Furlani, E.P.; Ortiz, I. Analysis of separators for magnetic beads recovery: From large systems to multifunctional microdevices. Sep. Purif. Technol.2017, 172, 16–31. [Google Scholar] [CrossRef]
Wise, N.; Grob, T.; Morten, K.; Thompson, I.; Sheard, S. Magnetophoretic velocities of superparamagnetic particles, agglomerates and complexes. J. Magn. Magn. Mater.2015, 384, 328–334. [Google Scholar] [CrossRef]
Khashan, S.A.; Elnajjar, E.; Haik, Y. CFD simulation of the magnetophoretic separation in a microchannel. J. Magn. Magn. Mater.2011, 323, 2960–2967. [Google Scholar] [CrossRef]
Khashan, S.A.; Furlani, E.P. Scalability analysis of magnetic bead separation in a microchannel with an array of soft magnetic elements in a uniform magnetic field. Sep. Purif. Technol.2014, 125, 311–318. [Google Scholar] [CrossRef]
Furlani, E.P. Magnetic biotransport: Analysis and applications. Materials2010, 3, 2412–2446. [Google Scholar] [CrossRef]
Gómez-Pastora, J.; Bringas, E.; Ortiz, I. Design of novel adsorption processes for the removal of arsenic from polluted groundwater employing functionalized magnetic nanoparticles. Chem. Eng. Trans.2016, 47, 241–246. [Google Scholar]
Gómez-Pastora, J.; Bringas, E.; Lázaro-Díez, M.; Ramos-Vivas, J.; Ortiz, I. The reverse of controlled release: Controlled sequestration of species and biotoxins into nanoparticles (NPs). In Drug Delivery Systems; Stroeve, P., Mahmoudi, M., Eds.; World Scientific: Hackensack, NJ, USA, 2017; pp. 207–244. ISBN 9789813201057. [Google Scholar]
Yáñez-Sedeño, P.; Campuzano, S.; Pingarrón, J.M. Magnetic particles coupled to disposable screen printed transducers for electrochemical biosensing. Sensors2016, 16, 1585. [Google Scholar] [CrossRef]
Schrittwieser, S.; Pelaz, B.; Parak, W.J.; Lentijo-Mozo, S.; Soulantica, K.; Dieckhoff, J.; Ludwig, F.; Guenther, A.; Tschöpe, A.; Schotter, J. Homogeneous biosensing based on magnetic particle labels. Sensors2016, 16, 828. [Google Scholar] [CrossRef]
He, J.; Huang, M.; Wang, D.; Zhang, Z.; Li, G. Magnetic separation techniques in sample preparation for biological analysis: A review. J. Pharm. Biomed. Anal.2014, 101, 84–101. [Google Scholar] [CrossRef]
Ha, Y.; Ko, S.; Kim, I.; Huang, Y.; Mohanty, K.; Huh, C.; Maynard, J.A. Recent advances incorporating superparamagnetic nanoparticles into immunoassays. ACS Appl. Nano Mater.2018, 1, 512–521. [Google Scholar] [CrossRef]
Gómez-Pastora, J.; González-Fernández, C.; Fallanza, M.; Bringas, E.; Ortiz, I. Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies. Chem. Eng. J.2018, 344, 487–497. [Google Scholar] [CrossRef]
Gale, B.K.; Jafek, A.R.; Lambert, C.J.; Goenner, B.L.; Moghimifam, H.; Nze, U.C.; Kamarapu, S.K. A review of current methods in microfluidic device fabrication and future commercialization prospects. Inventions2018, 3, 60. [Google Scholar] [CrossRef]
Nanobiotechnology; Concepts, Applications and Perspectives; Niemeyer, C.M.; Mirkin, C.A. (Eds.) Wiley-VCH: Weinheim, Germany, 2004; ISBN 3527305068. [Google Scholar]
Khashan, S.A.; Dagher, S.; Alazzam, A.; Mathew, B.; Hilal-Alnaqbi, A. Microdevice for continuous flow magnetic separation for bioengineering applications. J. Micromech. Microeng.2017, 27, 055016. [Google Scholar] [CrossRef]
Basauri, A.; Gomez-Pastora, J.; Fallanza, M.; Bringas, E.; Ortiz, I. Predictive model for the design of reactive micro-separations. Sep. Purif. Technol.2019, 209, 900–907. [Google Scholar] [CrossRef]
Abdollahi, P.; Karimi-Sabet, J.; Moosavian, M.A.; Amini, Y. Microfluidic solvent extraction of calcium: Modeling and optimization of the process variables. Sep. Purif. Technol.2020, 231, 115875. [Google Scholar] [CrossRef]
Khashan, S.A.; Alazzam, A.; Furlani, E. A novel design for a microfluidic magnetophoresis system: Computational study. In Proceedings of the 12th International Symposium on Fluid Control, Measurement and Visualization (FLUCOME2013), Nara, Japan, 18–23 November 2013. [Google Scholar]
Gómez-Pastora, J.; Amiri Roodan, V.; Karampelas, I.H.; Alorabi, A.Q.; Tarn, M.D.; Iles, A.; Bringas, E.; Paunov, V.N.; Pamme, N.; Furlani, E.P.; et al. Two-step numerical approach to predict ferrofluid droplet generation and manipulation inside multilaminar flow chambers. J. Phys. Chem. C2019, 123, 10065–10080. [Google Scholar] [CrossRef]
Gómez-Pastora, J.; Karampelas, I.H.; Bringas, E.; Furlani, E.P.; Ortiz, I. Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions. Sci. Rep.2019, 9, 7265. [Google Scholar] [CrossRef]
Tarn, M.D.; Pamme, N. On-Chip Magnetic Particle-Based Immunoassays Using Multilaminar Flow for Clinical Diagnostics. In Microchip Diagnostics Methods and Protocols; Taly, V., Viovy, J.L., Descroix, S., Eds.; Humana Press: New York, NY, USA, 2017; pp. 69–83. [Google Scholar]
Phurimsak, C.; Tarn, M.D.; Peyman, S.A.; Greenman, J.; Pamme, N. On-chip determination of c-reactive protein using magnetic particles in continuous flow. Anal. Chem.2014, 86, 10552–10559. [Google Scholar] [CrossRef]
Wu, X.; Wu, H.; Hu, Y. Enhancement of separation efficiency on continuous magnetophoresis by utilizing L/T-shaped microchannels. Microfluid. Nanofluid.2011, 11, 11–24. [Google Scholar] [CrossRef]
Vojtíšek, M.; Tarn, M.D.; Hirota, N.; Pamme, N. Microfluidic devices in superconducting magnets: On-chip free-flow diamagnetophoresis of polymer particles and bubbles. Microfluid. Nanofluid.2012, 13, 625–635. [Google Scholar] [CrossRef]
Gómez-Pastora, J.; González-Fernández, C.; Real, E.; Iles, A.; Bringas, E.; Furlani, E.P.; Ortiz, I. Computational modeling and fluorescence microscopy characterization of a two-phase magnetophoretic microsystem for continuous-flow blood detoxification. Lab Chip2018, 18, 1593–1606. [Google Scholar] [CrossRef] [PubMed]
Nandy, K.; Chaudhuri, S.; Ganguly, R.; Puri, I.K. Analytical model for the magnetophoretic capture of magnetic microspheres in microfluidic devices. J. Magn. Magn. Mater.2008, 320, 1398–1405. [Google Scholar] [CrossRef]
Hale, C.; Darabi, J. Magnetophoretic-based microfluidic device for DNA isolation. Biomicrofluidics2014, 8, 044118. [Google Scholar] [CrossRef] [PubMed]
Becker, H.; Gärtner, C. Polymer microfabrication methods for microfluidic analytical applications. Electrophoresis2000, 21, 12–26. [Google Scholar] [CrossRef]
Pekas, N.; Zhang, Q.; Nannini, M.; Juncker, D. Wet-etching of structures with straight facets and adjustable taper into glass substrates. Lab Chip2010, 10, 494–498. [Google Scholar] [CrossRef]
Wang, T.; Chen, J.; Zhou, T.; Song, L. Fabricating microstructures on glass for microfluidic chips by glass molding process. Micromachines2018, 9, 269. [Google Scholar] [CrossRef]
Castaño-Álvarez, M.; Pozo Ayuso, D.F.; García Granda, M.; Fernández-Abedul, M.T.; Rodríguez García, J.; Costa-García, A. Critical points in the fabrication of microfluidic devices on glass substrates. Sens. Actuators B Chem.2008, 130, 436–448. [Google Scholar] [CrossRef]
Prakash, S.; Kumar, S. Fabrication of microchannels: A review. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.2015, 229, 1273–1288. [Google Scholar] [CrossRef]
Leester-Schädel, M.; Lorenz, T.; Jürgens, F.; Ritcher, C. Fabrication of Microfluidic Devices. In Microsystems for Pharmatechnology: Manipulation of Fluids, Particles, Droplets, and Cells; Dietzel, A., Ed.; Springer: Basel, Switzerland, 2016; pp. 23–57. ISBN 9783319269207. [Google Scholar]
Bartlett, N.W.; Wood, R.J. Comparative analysis of fabrication methods for achieving rounded microchannels in PDMS. J. Micromech. Microeng.2016, 26, 115013. [Google Scholar] [CrossRef]
Ng, P.F.; Lee, K.I.; Yang, M.; Fei, B. Fabrication of 3D PDMS microchannels of adjustable cross-sections via versatile gel templates. Polymers2019, 11, 64. [Google Scholar] [CrossRef] [PubMed]
Furlani, E.P.; Sahoo, Y.; Ng, K.C.; Wortman, J.C.; Monk, T.E. A model for predicting magnetic particle capture in a microfluidic bioseparator. Biomed. Microdevices2007, 9, 451–463. [Google Scholar] [CrossRef]
Tarn, M.D.; Peyman, S.A.; Robert, D.; Iles, A.; Wilhelm, C.; Pamme, N. The importance of particle type selection and temperature control for on-chip free-flow magnetophoresis. J. Magn. Magn. Mater.2009, 321, 4115–4122. [Google Scholar] [CrossRef]
Furlani, E.P. Permanent Magnet and Electromechanical Devices; Materials, Analysis and Applications; Academic Press: Waltham, MA, USA, 2001. [Google Scholar]
White, F.M. Viscous Fluid Flow; McGraw-Hill: New York, NY, USA, 1974. [Google Scholar]
Mathew, B.; Alazzam, A.; El-Khasawneh, B.; Maalouf, M.; Destgeer, G.; Sung, H.J. Model for tracing the path of microparticles in continuous flow microfluidic devices for 2D focusing via standing acoustic waves. Sep. Purif. Technol.2015, 153, 99–107. [Google Scholar] [CrossRef]
Furlani, E.J.; Furlani, E.P. A model for predicting magnetic targeting of multifunctional particles in the microvasculature. J. Magn. Magn. Mater.2007, 312, 187–193. [Google Scholar] [CrossRef]
Furlani, E.P.; Ng, K.C. Analytical model of magnetic nanoparticle transport and capture in the microvasculature. Phys. Rev. E2006, 73, 061919. [Google Scholar] [CrossRef]
Pamme, N.; Eijkel, J.C.T.; Manz, A. On-chip free-flow magnetophoresis: Separation and detection of mixtures of magnetic particles in continuous flow. J. Magn. Magn. Mater.2006, 307, 237–244. [Google Scholar] [CrossRef]
Alorabi, A.Q.; Tarn, M.D.; Gómez-Pastora, J.; Bringas, E.; Ortiz, I.; Paunov, V.N.; Pamme, N. On-chip polyelectrolyte coating onto magnetic droplets-Towards continuous flow assembly of drug delivery capsules. Lab Chip2017, 17, 3785–3795. [Google Scholar] [CrossRef]
Zhang, H.; Guo, H.; Chen, Z.; Zhang, G.; Li, Z. Application of PECVD SiC in glass micromachining. J. Micromech. Microeng.2007, 17, 775–780. [Google Scholar] [CrossRef]
Mourzina, Y.; Steffen, A.; Offenhäusser, A. The evaporated metal masks for chemical glass etching for BioMEMS. Microsyst. Technol.2005, 11, 135–140. [Google Scholar] [CrossRef]
Mata, A.; Fleischman, A.J.; Roy, S. Fabrication of multi-layer SU-8 microstructures. J. Micromech. Microeng.2006, 16, 276–284. [Google Scholar] [CrossRef]
Su, N. 8 2000 Negative Tone Photoresist Formulations 2002–2025; MicroChem Corporation: Newton, MA, USA, 2002. [Google Scholar]
Su, N. 8 2000 Negative Tone Photoresist Formulations 2035–2100; MicroChem Corporation: Newton, MA, USA, 2002. [Google Scholar]
Fu, C.; Hung, C.; Huang, H. A novel and simple fabrication method of embedded SU-8 micro channels by direct UV lithography. J. Phys. Conf. Ser.2006, 34, 330–335. [Google Scholar] [CrossRef]
Kazoe, Y.; Yamashiro, I.; Mawatari, K.; Kitamori, T. High-pressure acceleration of nanoliter droplets in the gas phase in a microchannel. Micromachines2016, 7, 142. [Google Scholar] [CrossRef]
Sharp, K.V.; Adrian, R.J.; Santiago, J.G.; Molho, J.I. Liquid flows in microchannels. In MEMS: Introduction and Fundamentals; Gad-el-Hak, M., Ed.; CRC Press: Boca Raton, FL, USA, 2006; pp. 10-1–10-46. ISBN 9781420036572. [Google Scholar]
Oh, K.W.; Lee, K.; Ahn, B.; Furlani, E.P. Design of pressure-driven microfluidic networks using electric circuit analogy. Lab Chip2012, 12, 515–545. [Google Scholar] [CrossRef]
Bruus, H. Theoretical Microfluidics; Oxford University Press: New York, NY, USA, 2008; ISBN 9788578110796. [Google Scholar]
Beebe, D.J.; Mensing, G.A.; Walker, G.M. Physics and applications of microfluidics in biology. Annu. Rev. Biomed. Eng.2002, 4, 261–286. [Google Scholar] [CrossRef] [PubMed]
Yalikun, Y.; Tanaka, Y. Large-scale integration of all-glass valves on a microfluidic device. Micromachines2016, 7, 83. [Google Scholar] [CrossRef] [PubMed]
Van Heeren, H.; Verhoeven, D.; Atkins, T.; Tzannis, A.; Becker, H.; Beusink, W.; Chen, P. Design Guideline for Microfluidic Device and Component Interfaces (Part 2), Version 3; Available online: http://www.makefluidics.com/en/design-guideline?id=7 (accessed on 9 March 2020).
Scheuble, N.; Iles, A.; Wootton, R.C.R.; Windhab, E.J.; Fischer, P.; Elvira, K.S. Microfluidic technique for the simultaneous quantification of emulsion instabilities and lipid digestion kinetics. Anal. Chem.2017, 89, 9116–9123. [Google Scholar] [CrossRef] [PubMed]
Lynch, E.C. Red blood cell damage by shear stress. Biophys. J.1972, 12, 257–273. [Google Scholar]
Paul, R.; Apel, J.; Klaus, S.; Schügner, F.; Schwindke, P.; Reul, H. Shear stress related blood damage in laminar Couette flow. Artif. Organs2003, 27, 517–529. [Google Scholar] [CrossRef] [PubMed]
Gómez-Pastora, J.; Karampelas, I.H.; Xue, X.; Bringas, E.; Furlani, E.P.; Ortiz, I. Magnetic bead separation from flowing blood in a two-phase continuous-flow magnetophoretic microdevice: Theoretical analysis through computational fluid dynamics simulation. J. Phys. Chem. C2017, 121, 7466–7477. [Google Scholar] [CrossRef]
Lim, J.; Yeap, S.P.; Leow, C.H.; Toh, P.Y.; Low, S.C. Magnetophoresis of iron oxide nanoparticles at low field gradient: The role of shape anisotropy. J. Colloid Interface Sci.2014, 421, 170–177. [Google Scholar] [CrossRef] [PubMed]
Culbertson, C.T.; Sibbitts, J.; Sellens, K.; Jia, S. Fabrication of Glass Microfluidic Devices. In Microfluidic Electrophoresis: Methods and Protocols; Dutta, D., Ed.; Humana Press: New York, NY, USA, 2019; pp. 1–12. ISBN 978-1-4939-8963-8. [Google Scholar]
이 연구에서는 비드 운동과 유체 흐름에 미치는 영향에 대한 자세한 분석을 제공하기 위해 연속 흐름 마이크로 채널 내부의 비드 자기 영동에 대한 수치 흐름 중심 연구를 보고합니다.
수치 모델은 Lagrangian 접근 방식을 포함하며 영구 자석에 의해 생성 된 자기장의 적용에 의해 혈액에서 비드 분리 및 유동 버퍼로의 수집을 예측합니다.
다음 시나리오가 모델링됩니다. (i) 운동량이 유체에서 점 입자로 처리되는 비드로 전달되는 단방향 커플 링, (ii) 비드가 점 입자로 처리되고 운동량이 다음으로부터 전달되는 양방향 결합 비드를 유체로 또는 그 반대로, (iii) 유체 변위에서 비드 체적의 영향을 고려한 양방향 커플 링.
결과는 세 가지 시나리오에서 비드 궤적에 약간의 차이가 있지만 특히 높은 자기력이 비드에 적용될 때 유동장에 상당한 변화가 있음을 나타냅니다.
따라서 높은 자기력을 사용할 때 비드 운동과 유동장의 체적 효과를 고려한 정확한 전체 유동 중심 모델을 해결해야 합니다. 그럼에도 불구하고 비드가 중간 또는 낮은 자기력을 받을 때 계산적으로 저렴한 모델을 안전하게 사용하여 자기 영동을 모델링 할 수 있습니다.
1.Keshipour, S. & Khalteh, N. K. Oxidation of ethylbenzene to styrene oxide in the presence of cellulose-supported Pd magnetic nanoparticles. Appl. Organometal. Chem.30, 653–656 (2016).CASArticleGoogle Scholar
2.Neamtu, M. et al. Functionalized magnetic nanoparticles: synthesis, characterization, catalytic application and assessment of toxicity. Sci. Rep.8(1), 6278 (2018).ADSMathSciNetArticleGoogle Scholar
3.Gómez-Pastora, J., Bringas, E. & Ortiz, I. Recent progress and future challenges on the use of high performance magnetic nano-adsorbents in environmental applications. Chem. Eng. J.256, 187–204 (2014).ArticleGoogle Scholar
4.Gómez-Pastora, J., Bringas, E. & Ortiz, I. Design of novel adsorption processes for the removal of arsenic from polluted groundwater employing functionalized magnetic nanoparticles. Chem. Eng. Trans.47, 241–246 (2016).Google Scholar
5.Bagbi, Y., Sarswat, A., Mohan, D., Pandey, A. & Solanki, P. R. Lead and chromium adsorption from water using L-Cysteine functionalized magnetite (Fe3O4) nanoparticles. Sci. Rep.7(1), 7672 (2017).ADSArticleGoogle Scholar
6.Gómez-Pastora, J. et al. Review and perspectives on the use of magnetic nanophotocatalysts (MNPCs) in water treatment. Chem. Eng. J.310, 407–427 (2017).ArticleGoogle Scholar
7.Lee, H. Y. et al. A selective fluoroionophore based on BODIPY-functionalized magnetic silica nanoparticles: removal of Pb2+ from human blood. Angew. Chem. Int. Ed.48, 1239–1243 (2009).CASArticleGoogle Scholar
8.Buzea, C., Pacheco, I. I. & Robbie, K. Nanomaterials and nanoparticles: sources and toxicity. Biointerphases2, MR17–MR71 (2007).ArticleGoogle Scholar
9.Roux, S. et al. Multifunctional nanoparticles: from the detection of biomolecules to the therapy. Int. J. Nanotechnol.7, 781–801 (2010).ADSCASArticleGoogle Scholar
10.Gómez-Pastora, J., Bringas, E., Lázaro-Díez, M., Ramos-Vivas, J. & Ortiz, I. In Drug Delivery Systems (Stroeve, P. & Mahmoudi, M. ed) 207–244 (World Scientific, 2017).
11.Selmi, M., Gazzah, M. H. & Belmabrouk, H. Optimization of microfluidic biosensor efficiency by means of fluid flow engineering. Sci. Rep.7(1), 5721 (2017).ADSArticleGoogle Scholar
12.Gómez-Pastora, J., González-Fernández, C., Fallanza, M., Bringas, E. & Ortiz, I. Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies. Chem. Eng. J.344, 487–497 (2018).ArticleGoogle Scholar
14.Alorabi, A. Q. et al. On-chip polyelectrolyte coating onto magnetic droplets – towards continuous flow assembly of drug delivery capsules. Lab Chip17, 3785–3795 (2017).CASArticleGoogle Scholar
15.Gómez-Pastora, J. et al. Analysis of separators for magnetic beads recovery: from large systems to multifunctional microdevices. Sep. Purif. Technol.172, 16–31 (2017).ArticleGoogle Scholar
16.Tarn, M. D. & Pamme, N. On-chip magnetic particle-based immunoassays using multilaminar flow for clinical diagnosis. Methods Mol. Biol.1547, 69–83 (2017).CASArticleGoogle Scholar
17.Lv, C. et al. Integrated optofluidic-microfluidic twin channels: toward diverse application of lab-on-a-chip systems. Sci. Rep.6, 19801 (2016).ADSCASArticleGoogle Scholar
18.Gómez-Pastora, J. et al. Magnetic bead separation from flowing blood in a two-phase continuous-flow magnetophoretic microdevice: theoretical analysis through computational fluid dynamics simulation. J. Phys. Chem. C121, 7466–7477 (2017).ArticleGoogle Scholar
20.Khashan, S. A. & Furlani, E. P. Effects of particle–fluid coupling on particle transport and capture in a magnetophoretic microsystem. Microfluid. Nanofluid.12, 565–580 (2012).ArticleGoogle Scholar
21.Modak, N., Datta, A. & Ganguly, R. Cell separation in a microfluidic channel using magnetic microspheres. Microfluid. Nanofluid.6, 647–660 (2009).CASArticleGoogle Scholar
22.Furlani, E. P., Sahoo, Y., Ng, K. C., Wortman, J. C. & Monk, T. E. A model for predicting magnetic particle capture in a microfluidic bioseparator. Biomed. Microdevices9, 451–463 (2007).CASArticleGoogle Scholar
23.Furlani, E. P. & Sahoo, Y. Analytical model for the magnetic field and force in a magnetophoretic microsystem. J. Phys. D: Appl. Phys.39, 1724–1732 (2006).ADSCASArticleGoogle Scholar
24.Tarn, M. D. et al. The importance of particle type selection and temperature control for on-chip free-flow magnetophoresis. J. Magn. Magn. Mater.321, 4115–4122 (2009).ADSCASArticleGoogle Scholar
25.Fonnum, G., Johansson, C., Molteberg, A., Morup, S. & Aksnes, E. Characterisation of Dynabeads® by magnetization measurements and Mössbauer spectroscopy. J. Magn. Magn. Mater.293, 41–47 (2005).ADSCASArticleGoogle Scholar
26.Xue, W., Moore, L. R., Nakano, N., Chalmers, J. J. & Zborowski, M. Single cell magnetometry by magnetophoresis vs. bulk cell suspension magnetometry by SQUID-MPMS – A comparison. J. Magn. Magn. Mater.474, 152–160 (2019).ADSCASArticleGoogle Scholar
27.Moore, L. R. et al. Continuous, intrinsic magnetic depletion of erythrocytes from whole blood with a quadrupole magnet and annular flow channel; pilot scale study. Biotechnol. Bioeng.115, 1521–1530 (2018).CASArticleGoogle Scholar
28.Furlani, E. P. & Xue, X. Field, force and transport analysis for magnetic particle-based gene delivery. Microfluid Nanofluid.13, 589–602 (2012).CASArticleGoogle Scholar
29.Furlani, E. P. & Xue, X. A model for predicting field-directed particle transport in the magnetofection process. Pharm. Res.29, 1366–1379 (2012).CASArticleGoogle Scholar
30.Furlani, E. P. Permanent Magnet and Electromechanical Devices; Materials, Analysis and Applications, (Academic Press, 2001).
31.Balachandar, S. & Eaton, J. K. Turbulent dispersed multiphase flow. Annu. Rev. Fluid Mech.42, 111–133 (2010).ADSArticleGoogle Scholar
32.Wakaba, L. & Balachandar, S. On the added mass force at finite Reynolds and acceleration number. Theor. Comput. Fluid. Dyn.21, 147–153 (2007).ArticleGoogle Scholar
33.White, F. M. Viscous Fluid Flow, (McGraw-Hill, 1974).
34.Rietema, K. & Van Den Akker, H. E. A. On the momentum equations in dispersed two-phase systems. Int. J. Multiphase Flow9, 21–36 (1983).ArticleGoogle Scholar
35.Furlani, E. P. & Ng, K. C. Analytical model of magnetic nanoparticle transport and capture in the microvasculature. Phys. Rev. E73, 1–10 (2006).ArticleGoogle Scholar
36.Eibl, R., Eibl, D., Pörtner, R., Catapano, G. & Czermak, P. Cell and Tissue Reaction Engineering, (Springer, 2009).
37.Gómez-Pastora, J. et al. Computational modeling and fluorescence microscopy characterization of a two-phase magnetophoretic microsystem for continuous-flow blood detoxification. Lab Chip18, 1593–1606 (2018).ArticleGoogle Scholar
38.Khashan, S. A. & Furlani, E. P. Scalability analysis of magnetic bead separation in a microchannel with an array of soft magnetic elements in a uniform magnetic field. Sep. Purif. Technol.125, 311–318 (2014).CASArticleGoogle Scholar
39.Hirt, C. W. & Sicilian, J. M. A porosity technique for the definition of obstacles in rectangular cell meshes. Proc. Fourth International Conf. Ship Hydro., National Academic of Science, Washington, DC., (1985).
40.Crank, J. Free and Moving Boundary Problems, (Oxford University Press, 1984).
41.Bruus, H. Theoretical Microfluidics, (Oxford University Press, 2008).
42.Liang, L. & Xuan, X. Diamagnetic particle focusing using ferromicrofluidics with a single magnet. Microfluid. Nanofluid.13, 637–643 (2012).
Edward P. Furlani is deceased.
Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005, Santander, SpainJenifer Gómez-Pastora, Eugenio Bringas & Inmaculada Ortiz
Flow Science, Inc, Santa Fe, New Mexico, 87505, USAIoannis H. Karampelas
Department of Chemical and Biological Engineering, University at Buffalo (SUNY), Buffalo, New York, 14260, USAEdward P. Furlani
Department of Electrical Engineering, University at Buffalo (SUNY), Buffalo, New York, 14260, USAEdward P. Furlani
Junichi Saeki and Tsutomu Kono Production Engineering Research Laboratory, Hitachi Ltd. 292, Y shida-cho, Totsuka-ku, Yokohama, 244-0817 Japan
Thermosetting molding compounds are widely used for encapsulating semiconductor devices and electronic modules. In recent years, the number of electronic parts encapsulated in an electronic module has increased, in order to meet the requirements for high performance. As a result, the configuration of inserted parts during molding has become very complicated. Meanwhile, package thickness has been reduced in response to consumer demands for miniaturization. These trends have led to complicated flow patterns of molten compounds in a mold cavity, increasing the difficulty of predicting the occurrence of void formation or gold-wire deformation.
A method of three-dimensional (3-D) flow analysis of thermosetting compounds has been developed with the objective of minimizing the trial term before mass production and of enhancing the quality of molded products. A constitutive equation model was developed to describe isothermal viscosity changes as a function of time and temperature. This isothermal model was used for predicting non-isothermal viscosity changes. In addition, an empirical model was developed for calculating the amount of wire deformation as a function of viscosity, wire configuration, and other parameters. These models were integrated with FLOW-3D® software, which is used for multipurpose 3-D flow analysis.
The mold-filling dynamics of an epoxy compound were analyzed using the newly developed modeling software during transfer molding of an actual high performance electronic module. The changes in the 3-D distributions of parameters such as temperature, viscosity, velocity, and pressure were compared with the flow front patterns. The predicted results of cavity filling behavior corresponded well with actual short shot data. As well, the predicted amount of gold-wire deformation at each LSI chip with a substrate connection also corresponded well with observed data obtained by X-ray inspection of the molded product.
열경화성 몰딩 컴파운드는 반도체 장치 및 전자 모듈을 캡슐화하는 데 널리 사용됩니다. 최근에는 고성능에 대한 요구 사항을 충족시키기 위해 전자 모듈에 캡슐화되는 전자 부품의 수가 증가하고 있습니다.
그 결과 성형시 삽입 부품의 구성이 매우 복잡해졌습니다. 한편, 소비자의 소형화 요구에 부응하여 패키지 두께를 줄였다. 이러한 경향은 몰드 캐비티에서 용융된 화합물의 복잡한 흐름 패턴을 야기하여 보이드 형성 또는 금선 변형의 발생을 예측하기 어렵게합니다.
열경화성 화합물의 3 차원 (3-D) 유동 분석 방법은 대량 생산 전에 시험 기간을 최소화하고 성형 제품의 품질을 향상시킬 목적으로 개발되었습니다. 시간과 온도의 함수로서 등온 점도 변화를 설명하기 위해 구성 방정식 모델이 개발되었습니다. 이 등온 모델은 비등 온 점도 변화를 예측하는 데 사용되었습니다.
또한 점도, 와이어 구성 및 기타 매개 변수의 함수로 와이어 변형량을 계산하기위한 경험적 모델이 개발되었습니다. 이 모델은 다목적 3D 흐름 분석에 사용되는 FLOW-3D® 소프트웨어와 통합되었습니다.
실제 고성능 전자 모듈의 트랜스퍼 몰딩 과정에서 새로 개발 된 모델링 소프트웨어를 사용하여 에폭시 화합물의 몰드 충전 역학을 분석했습니다. 온도, 점도, 속도 및 압력과 같은 매개 변수의 3D 분포 변화를 유동 선단 패턴과 비교했습니다.
캐비티 충전 거동의 예측 결과는 실제 미 성형 데이터와 잘 일치했습니다. 또한, 기판 연결이 있는 각 LSI 칩에서 예상되는 금선 변형량은 성형품의 X-ray 검사에서 얻은 관찰 데이터와도 잘 일치했습니다.
1)J.Saeki et al. ,6th annual meeting of PPS, 12KN1(1990) 2)J.Saeki et al. , JSME International Journal Series Ⅱ, 33,486(1990) 3)J.Saeki et al.,SEIKEI KAKOU,12,67(2000) 4) J.Saeki et al.,SEIKEI KAKOU,12,788(2000) 5) J.Saeki et al.,SEIKEI KAKOU,13,49(2001)
Department of mechanical engineering, Technical University of Denmark (DTU), Building 425, 2800 Kgs., Lyngby, Denmark
Received 21 August 2020, Revised 18 November 2020, Accepted 25 November 2020, Available online 15 December 2020.
Several different interfacial forces affect the free surface of liquid metals during metal additive manufacturing processes. One of these is thermo-capillarity or the so-called Marangoni effect. In this work, a novel framework is introduced for unraveling the effects of thermo-capillarity on the melt pool morphology/size and its thermo-fluid conditions during the Laser Powder Bed Fusion (L-PBF) process. In this respect, a multi-physics numerical model is developed based on the commercial software package Flow-3D. The model is verified and validated via mesh-independency analysis and by comparison of the predicted melt pool profile with those from lab-scale single-track experiments. Two sets of parametric studies are carried out to find the role of both positive and inverse thermo-capillarity on the melt pool shape and its thermal and fluid dynamics conditions. The thermo-fluid conditions of the melt pool are further investigated using appropriate dimensionless numbers. The results show that for the higher Marangoni number cases, the melt pool temperature drops, and at the same time, the temperature field becomes more uniform. Also, it is shown that at higher Marangoni numbers, temperature gradients decrease, thus reducing the role of conduction in the heat transfer from the melt pool. Furthermore, for the first time, a novel methodology is introduced for the calculation of the melt pool’s average Nusselt number. The average Nusselt numbers calculated for the positive and inverse thermo-capillarity are then used for finding the effective liquid conductivity required for a computationally cheaper pure heat conduction simulation. The results show that the deviation between the average melt pool temperature, using the pure conduction model with effective conductivity, and the one obtained from the advanced fluid dynamics model is less than 2%.
Thermo-capillarity, Melt pool, Heat and fluid flow, Numerical model, L-PBF
금속 적층 제조 공정 중 액체 금속의 자유 표면에 여러 가지 다른 계면력이 영향을 미칩니다. 이들 중 하나는 열 모세관 또는 소위 Marangoni 효과입니다.
이 작업에서는 L-PBF (Laser Powder Bed Fusion) 공정 중 용융 풀 형태 / 크기 및 열 유동 조건에 대한 열 모세관의 영향을 밝히기 위한 새로운 프레임워크가 도입되었습니다.
이러한 점에서 상용 소프트웨어 패키지 Flow-3D를 기반으로 다중 물리 수치 모델이 개발되었습니다. 모델은 메쉬 독립 분석을 통해 그리고 예측 된 용융 풀 프로필을 실험실 규모의 단일 트랙 실험에서 얻은 프로필과 비교하여 검증 및 검증됩니다.
용융 풀 모양과 열 및 유체 역학 조건에 대한 양 및 역 열 모세관의 역할을 찾기 위해 두 세트의 매개 변수 연구가 수행됩니다. 용융 풀의 열 유동 조건은 적절한 무 차원 숫자를 사용하여 추가로 조사됩니다.
결과는 Marangoni 수가 더 높은 경우 용융 풀 온도가 떨어지고 동시에 온도 필드가 더 균일 해짐을 보여줍니다. 또한 Marangoni 수가 높을수록 온도 구배가 감소하여 용융 풀에서 열 전달에서 전도의 역할이 감소하는 것으로 나타났습니다.
또한 용융 풀의 평균 Nusselt 수를 계산하기위한 새로운 방법론이 처음으로 도입되었습니다. 그런 다음 양수 및 역 열 모세관에 대해 계산 된 평균 Nusselt 수는 계산적으로 더 저렴한 순수 열 전도 시뮬레이션에 필요한 효과적인 액체 전도도를 찾는 데 사용됩니다. 결과는 유효 전도도가 있는 순수 전도 모델을 사용한 평균 용융 풀 온도와 고급 유체 역학 모델에서 얻은 편차가 2 % 미만임을 보여줍니다.
Spillways are constructed to evacuate flood discharge safely so that a flood wave does not overtop the dam body. There are different types of spillways, with the ogee type being the conventional one. A stepped spillway is an example of a nonconventional spillway. The turbulent flow over a stepped spillway was studied numerically by using the Flow-3D package. Different fluid flow characteristics such as longitudinal flow velocity, temperature distribution, density and chemical concentration can be well simulated by Flow-3D. In this study, the influence of slope changes on flow characteristics such as air entrainment, velocity distribution and dynamic pressures distribution over a stepped spillway was modelled by Flow-3D. The results from the numerical model were compared with an experimental study done by others in the literature. Two models of a stepped spillway with different discharge for each model were simulated. The turbulent flow in the experimental model was simulated by the Renormalized Group (RNG) turbulence scheme in the numerical model. A good agreement was achieved between the numerical results and the observed ones, which are exhibited in terms of graphics and statistical tables.
배수로는 홍수가 댐 몸체 위로 넘치지 않도록 안전하게 홍수를 피할 수 있도록 건설되었습니다. 다른 유형의 배수로가 있으며, ogee 유형이 기존 유형입니다. 계단식 배수로는 비 전통적인 배수로의 예입니다. 계단식 배수로 위의 난류는 Flow-3D 패키지를 사용하여 수치적으로 연구되었습니다.
세로 유속, 온도 분포, 밀도 및 화학 농도와 같은 다양한 유체 흐름 특성은 Flow-3D로 잘 시뮬레이션 할 수 있습니다. 이 연구에서는 계단식 배수로에 대한 공기 혼입, 속도 분포 및 동적 압력 분포와 같은 유동 특성에 대한 경사 변화의 영향을 Flow-3D로 모델링 했습니다.
수치 모델의 결과는 문헌에서 다른 사람들이 수행한 실험 연구와 비교되었습니다. 각 모델에 대해 서로 다른 배출이 있는 계단식 배수로의 두 모델이 시뮬레이션되었습니다. 실험 모델의 난류 흐름은 수치 모델의 Renormalized Group (RNG) 난류 계획에 의해 시뮬레이션되었습니다. 수치 결과와 관찰 된 결과 사이에 좋은 일치가 이루어졌으며, 이는 그래픽 및 통계 테이블로 표시됩니다.
댐 구조는 물 보호가 생활의 핵심이기 때문에 물을 저장하거나 물을 운반하는 전 세계에서 가장 중요한 프로젝트입니다. 그리고 여수로는 댐의 가장 중요한 부분 중 하나로 분류됩니다. 홍수로 인한 파괴 나 피해로부터 댐을 보호하기 위해 여수로가 건설됩니다.
수력 발전, 항해, 레크리에이션 및 어업의 중요성을 감안할 때 댐 건설 및 홍수 통제는 전 세계적으로 매우 중요한 문제로 간주 될 수 있습니다. 많은 유형의 배수로가 있지만 가장 일반적인 유형은 다음과 같습니다 : ogee 배수로, 자유 낙하 배수로, 사이펀 배수로, 슈트 배수로, 측면 채널 배수로, 터널 배수로, 샤프트 배수로 및 계단식 배수로.
그리고 모든 여수로는 입구 채널, 제어 구조, 배출 캐리어 및 출구 채널의 네 가지 필수 구성 요소로 구성됩니다. 특히 롤러 압축 콘크리트 (RCC) 댐 건설 기술과 더 쉽고 빠르며 저렴한 건설 기술로 분류 된 계단식 배수로 건설과 관련하여 최근 수십 년 동안 많은 계단식 배수로가 건설되었습니다 (Chanson 2002; Felder & Chanson 2011).
계단식 배수로 구조는 캐비테이션 위험을 감소시키는 에너지 소산 속도를 증가시킵니다 (Boes & Hager 2003b). 계단식 배수로는 다양한 조건에서 더 매력적으로 만드는 장점이 있습니다.
계단식 배수로의 흐름 거동은 일반적으로 낮잠, 천이 및 스키밍 흐름 체제의 세 가지 다른 영역으로 분류됩니다 (Chanson 2002). 유속이 낮을 때 nappe 흐름 체제가 발생하고 자유 낙하하는 낮잠의 시퀀스로 특징 지워지는 반면, 스키밍 흐름 체제에서는 물이 외부 계단 가장자리 위의 유사 바닥에서 일관된 흐름으로 계단 위로 흐릅니다.
또한 주요 흐름에서 3 차원 재순환 소용돌이가 발생한다는 것도 분명합니다 (예 : Chanson 2002; Gonzalez & Chanson 2008). 계단 가장자리 근처의 의사 바닥에서 흐름의 방향은 가상 바닥과 가상으로 정렬됩니다. Takahashi & Ohtsu (2012)에 따르면, 스키밍 흐름 체제에서 주어진 유속에 대해 흐름은 계단 가장자리 근처의 수평 계단면에 영향을 미치고 슈트 경사가 감소하면 충돌 영역의 면적이 증가합니다. 전이 흐름 체제는 나페 흐름과 스키밍 흐름 체제 사이에서 발생합니다. 계단식 배수로를 설계 할 때 스키밍 흐름 체계를 고려해야합니다 (예 : Chanson 1994, Matos 2000, Chanson 2002, Boes & Hager 2003a).
CFD (Computational Fluid Dynamics), 즉 수력 공학의 수치 모델은 일반적으로 물리적 모델에 소요되는 총 비용과 시간을 줄여줍니다. 따라서 수치 모델은 실험 모델보다 빠르고 저렴한 것으로 분류되며 동시에 하나 이상의 목적으로 사용될 수도 있습니다. 사용 가능한 많은 CFD 소프트웨어 패키지가 있지만 가장 널리 사용되는 것은 FLOW-3D입니다. 이 연구에서는 Flow 3D 소프트웨어를 사용하여 유량이 서로 다른 두 모델에 대해 계단식 배수로에서 공기 농도, 속도 분포 및 동적 압력 분포를 시뮬레이션합니다.
Roshan et al. (2010)은 서로 다른 수의 계단 및 배출을 가진 계단식 배수로의 두 가지 물리적 모델에 대한 흐름 체제 및 에너지 소산 조사를 연구했습니다. 실험 모델의 기울기는 각각 19.2 %, 12 단계와 23 단계의 수입니다. 결과는 23 단계 물리적 모델에서 관찰 된 흐름 영역이 12 단계 모델보다 더 수용 가능한 것으로 간주되었음을 보여줍니다. 그러나 12 단계 모델의 에너지 손실은 23 단계 모델보다 더 많았습니다. 그리고 실험은 스키밍 흐름 체제에서 23 단계 모델의 에너지 소산이 12 단계 모델보다 약 12 % 더 적다는 것을 관찰했습니다.
Ghaderi et al. (2020a)는 계단 크기와 유속이 다른 정련 매개 변수의 영향을 조사하기 위해 계단식 배수로에 대한 실험 연구를 수행했습니다. 그 결과, 흐름 체계가 냅페 흐름 체계에서 발생하는 최소 scouring 깊이와 같은 scouring 구멍 치수에 영향을 미친다는 것을 보여주었습니다. 또한 테일 워터 깊이와 계단 크기는 최대 scouring깊이에 대한 실제 매개 변수입니다. 테일 워터의 깊이를 6.31cm에서 8.54 및 11.82cm로 늘림으로써 수세 깊이가 각각 18.56 % 및 11.42 % 증가했습니다. 또한 이 증가하는 테일 워터 깊이는 scouring 길이를 각각 31.43 % 및 16.55 % 감소 시킵니다. 또한 유속을 높이면 Froude 수가 증가하고 흐름의 운동량이 증가하면 scouring이 촉진됩니다. 또한 결과는 중간의 scouring이 횡단면의 측벽보다 적다는 것을 나타냅니다. 계단식 배수로 하류의 최대 scouring 깊이를 예측 한 후 실험 결과와 비교하기 위한 실험식이 제안 되었습니다. 그리고 비교 결과 제안 된 공식은 각각 3.86 %와 9.31 %의 상대 오차와 최대 오차 내에서 scouring 깊이를 예측할 수 있음을 보여주었습니다.
Ghaderi et al. (2020b)는 사다리꼴 미로 모양 (TLS) 단계의 수치 조사를 했습니다. 결과는 이러한 유형의 배수로가 확대 비율 LT / Wt (LT는 총 가장자리 길이, Wt는 배수로의 폭)를 증가시키기 때문에 더 나은 성능을 갖는 것으로 관찰되었습니다. 또한 사다리꼴 미로 모양의 계단식 배수로는 더 큰 마찰 계수와 더 낮은 잔류 수두를 가지고 있습니다. 마찰 계수는 다양한 배율에 대해 0.79에서 1.33까지 다르며 평평한 계단식 배수로의 경우 대략 0.66과 같습니다. 또한 TLS 계단식 배수로에서 잔류 수두의 비율 (Hres / dc)은 약 2.89이고 평평한 계단식 배수로의 경우 약 4.32와 같습니다.
Shahheydari et al. (2015)는 Flow-3D 소프트웨어, RNG k-ε 모델 및 VOF (Volume of Fluid) 방법을 사용하여 배출 계수 및 에너지 소산과 같은 자유 표면 흐름의 프로파일을 연구하여 스키밍 흐름 체제에서 계단식 배수로에 대한 흐름을 조사했습니다. 실험 결과와 비교했습니다. 결과는 에너지 소산 율과 방전 계수율의 관계가 역으로 실험 모델의 결과와 잘 일치 함을 보여 주었다.
Mohammad Rezapour Tabari & Tavakoli (2016)는 계단 높이 (h), 계단 길이 (L), 계단 수 (Ns) 및 단위 폭의 방전 (q)과 같은 다양한 매개 변수가 계단식 에너지 소산에 미치는 영향을 조사했습니다. 방수로. 그들은 해석에 FLOW-3D 소프트웨어를 사용하여 계단식 배수로에서 에너지 손실과 임계 흐름 깊이 사이의 관계를 평가했습니다. 또한 유동 난류에 사용되는 방정식과 표준 k-ɛ 모델을 풀기 위해 유한 체적 방법을 적용했습니다. 결과에 따르면 스텝 수가 증가하고 유량 배출량이 증가하면 에너지 손실이 감소합니다. 얻은 결과를 다른 연구와 비교하고 경험적, 수학적 조사를 수행하여 결국 합격 가능한 결과를 얻었습니다.
ListenReadSpeaker webReader: ListenFor all numerical models the basic principle is very similar: a set of partial differential equations (PDE) present the physical problems. The flow of fluids (gas and liquid) are governed by the conservation laws of mass, momentum and energy. For Computational Fluid Dynamics (CFD), the PDE system is substituted by a set of algebraic equations which can be worked out by using numerical methods (Versteeg & Malalasekera 2007). Flow-3D uses the finite volume approach to solve the Reynolds Averaged Navier-Stokes (RANS) equation, by applying the technique of Fractional Area/Volume Obstacle Representation (FAVOR) to define an obstacle (Flow Science Inc. 2012). Equations (1) and (2) are RANS and continuity equations with FAVOR variables that are applied for incompressible flows.
(2)where is the velocity in xi direction, t is the time, is the fractional area open to flow in the subscript directions, is the volume fraction of fluid in each cell, p is the hydrostatic pressure, is the density, is the gravitational force in subscript directions and is the Reynolds stresses.
Turbulence modelling is one of three key elements in CFD (Gunal 1996). There are many types of turbulence models, but the most common are Zero-equation models, One-equation models, Two-equation models, Reynolds Stress/Flux models and Algebraic Stress/Flux models. In FLOW-3D software, five turbulence models are available. The formulation used in the FLOW-3D software differs slightly from other formulations that includes the influence of the fractional areas/volumes of the FAVORTM method and generalizes the turbulence production (or decay) associated with buoyancy forces. The latter generalization, for example, includes buoyancy effects associated with non-inertial accelerations.
The available turbulence models in Flow-3D software are the Prandtl Mixing Length Model, the One-Equation Turbulent Energy Model, the Two-Equation Standard Model, the Two-Equation Renormalization-Group (RNG) Model and large Eddy Simulation Model (Flow Science Inc. 2012).In this research the RNG model was selected because this model is more commonly used than other models in dealing with particles; moreover, it is more accurate to work with air entrainment and other particles. In general, the RNG model is classified as a more widely-used application than the standard k-ɛ model. And in particular, the RNG model is more accurate in flows that have strong shear regions than the standard k-ɛ model and it is defined to describe low intensity turbulent flows. For the turbulent dissipation it solves an additional transport equation:
(3)where CDIS1, CDIS2, and CDIS3 are dimensionless parameters and the user can modify them. The diffusion of dissipation, Diff ɛ, is
(4)where u, v and w are the x, y and z coordinates of the fluid velocity; , , and , are FLOW-3D’s FAVORTM defined terms; and are turbulence due to shearing and buoyancy effects, respectively. R and are related to the cylindrical coordinate system. The default values of RMTKE, CDIS1 and CNU differ, being 1.39, 1.42 and 0.085 respectively. And CDIS2 is calculated from turbulent production () and turbulent kinetic energy ().The kinematic turbulent viscosity is the same in all turbulence transport models and is calculated from
(5)where : is the turbulent kinematic viscosity. is defined as the numerical challenge between the RNG and the two-equation k-ɛ models, found in the equation below. To avoid an unphysically large result for in Equation (3), since this equation could produce a value for very close to zero and also because the physical value of may approach to zero in such cases, the value of is calculated from the following equation:
(6)where : the turbulent length scale.
VOF and FAVOR are classifications of volume-fraction methods. In these two methods, firstly the area should be subdivided into a control volume grid or a small element. Each flow parameter like velocity, temperature and pressure values within the element are computed for each element containing liquids. Generally, these values represent the volumetric average of values in the elements.Numerous methods have been used recently to solve free infinite boundaries in the various numerical simulations. VOF is an easy and powerful method created based on the concept of a fractional intensity of fluid. A significant number of studies have confirmed that this method is more flexible and efficient than others dealing with the configurations of a complex free boundary. By using VOF technology the Flow-3D free surface was modelled and first declared in Hirt & Nichols (1981). In the VOF method there are three ingredients: a planner to define the surface, an algorithm for tracking the surface as a net mediator moving over a computational grid, and application of the boundary conditions to the surface. Configurations of the fluids are defined in terms of VOF function, F (x, y, z, t) (Hirt & Nichols 1981). And this VOF function shows the volume of flow per unit volume
(9)where is the density of the fluid, is a turbulent diffusion term, is a mass source, is the fractional volume open to flow. The components of velocity (u, v, w) are in the direction of coordinates (x, y, z) or (r, ). in the x-direction is the fractional area open to flow, and are identical area fractions for flow in the y and z directions. The R coefficient is based on the selection of the coordinate system.
The FAVOR method is a different method and uses another volume fraction technique, which is only used to define the geometry, such as the volume of liquid in each cell used to determine the position of fluid surfaces. Another fractional volume can be used to define the solid surface. Then, this information is used to determine the boundary conditions of the wall that the flow should be adapted for.
In this study, the experimental results of Ostad Mirza (2016) was simulated. In a channel composed of two 4 m long modules, with a transparent sidewall of height 0.6 m and 0.5 m width. The upstream chute slope (i.e. pseudo-bottom angle) Ɵ1 = 50°, the downstream chute slope Ɵ2 = 30° or 18.6°, the step heights h = 0.06 m, the total number of steps along the 50° chute 41 steps, the total number of steps along the 30° chute 34 steps and the total number of steps along the 18.6° chute 20 steps.
The flume inflow tool contained a jetbox with a maximum opening set to 0.12 meters, designed for passing the maximum unit discharge of 0.48 m2/s. The measurements of the flow properties (i.e. air concentration and velocity) were computed perpendicular to the pseudo-bottom as shown in Figure 1 at the centre of twenty stream-wise cross-sections, along the stepped chute, (i.e. in five steps up on the slope change and fifteen steps down on the slope change, namely from step number −09 to +23 on 50°–30° slope change, or from −09 to +15 on 50°–18.6° slope change, respectively).
Sketch of the air concentration C and velocity V measured perpendicular to the pseudo-bottom used by Mirza (Ostad Mirza 2016).
Pressure sensors were arranged with the x/l values for different slope change as shown in Table 1, where x is the distance from the step edge, along the horizontal step face, and l is the length of the horizontal step face. The location of pressure sensors is shown in Table 1.Table 1
Location of pressure sensors on horizontal step faces
Location of pressure sensors on horizontal step faces
Inlet boundary condition for Q = 0.235 m3/s and fluid elevation 4.21834 m.
A 3D numerical model of hydraulic phenomena was simulated based on an experimental study by Ostad Mirza (2016). The water surcharge and flow pressure over the stepped spillway was computed for two models of a stepped spillway with different discharge for each model. In this study, the package was used to simulate the flow parameters such as air entrainment, velocity distribution and dynamic pressures. The solver uses the finite volume technique to discretize the computational domain. In every test run, one incompressible fluid flow with a free surface flow selected at 20̊ was used for this simulation model. Table 2 shows the variables used in test runs.Table 2
Variables used in test runs
Table 2 Variables used in test runs
For stepped spillway simulation, several parameters should be specified to get accurate simulations, which is the scope of this research. Viscosity and turbulent, gravity and non-inertial reference frame, air entrainment, density evaluation and drift-flux should be activated for these simulations. There are five different choices in the ‘viscosity and turbulent’ option, in the viscosity flow and Renormalized Group (RNG) model. Then a dynamical model is selected as the second option, the ‘gravity and non-inertial reference frame’. Only the z-component was inputted as a negative 9.81 m/s2 and this value represents gravitational acceleration but in the same option the x and y components will be zero. Air entrainment is selected. Finally, in the drift-flux model, the density of phase one is input as (water) 1,000 kg/m3 and the density of phase two (air) as 1.225 kg/m3. Minimum volume fraction of phase one is input equal to 0.1 and maximum volume fraction of phase two to 1 to allow air concentration to reach 90%, then the option allowing gas to escape at free surface is selected, to obtain closer simulation.
The flow domain is divided into small regions relatively by the mesh in Flow-3D numerical model. Cells are the smallest part of the mesh, in which flow characteristics such as air concentration, velocity and dynamic pressure are calculated. The accuracy of the results and simulation time depends directly on the mesh block size so the cell size is very important. Orthogonal mesh was used in cartesian coordinate systems. A smaller cell size provides more accuracy for results, so we reduced the number of cells whilst including enough accuracy. In this study, the size of cells in x, y and z directions was selected as 0.015 m after several trials.
Figure 3 shows the 3D computational domain model 50–18.6 slope change, that is 6.0 m length, 0.50 m width and 4.23 m height. The 3D model of the computational domain model 50–30 slope changes this to 6.0 m length, 0.50 m width and 5.068 m height and the size of meshes in x, y, and z directions are 0.015 m. For the 50–18.6 slope change model: both total number of active and passive cells = 4,009,952, total number of active cells = 3,352,307, include real cells (used for solving the flow equations) = 3,316,269, open real cells = 3,316,269, fully blocked real cells equal to zero, external boundary cells were 36,038, inter-block boundary cells = 0 (Flow-3D report). For 50–30 slope change model: both total number of active and passive cells = 4,760,002, total number of active cells equal to 4,272,109, including real cells (used for solving the flow equations) were 3,990,878, open real cells = 3,990,878 fully blocked real cells = zero, external boundary cells were 281,231, inter-block boundary cells = 0 (Flow-3D report).
The 3D computational domain model (50–18.6) slope change, and boundary condition for (50–30 slope change) model.
When solving the Navier-Stokes equation and continuous equations, boundary conditions should be applied. The most important work of boundary conditions is to create flow conditions similar to physical status. The Flow-3D software has many types of boundary condition; each type can be used for the specific condition of the models. The boundary conditions in Flow-3D are symmetry, continuative, specific pressure, grid overlay, wave, wall, periodic, specific velocity, outflow, and volume flow rate.
There are two options to input finite flow rate in the Flow-3D software either for inlet discharge of the system or for the outlet discharge of the domain: specified velocity and volume flow rate. In this research, the X-minimum boundary condition, volume flow rate, has been chosen. For X-maximum boundary condition, outflow was selected because there is nothing to be calculated at the end of the flume. The volume flow rate and the elevation of surface water was set for Q = 0.1 and 0.235 m3/s respectively (Figure 2).
The bottom (Z-min) is prepared as a wall boundary condition and the top (Z-max) is computed as a pressure boundary condition, and for both (Y-min) and (Y-max) as symmetry.
The air concentration distribution profiles in two models of stepped spillway were obtained at an acquisition time equal to 25 seconds in skimming flow for both upstream and downstream of a slope change 50°–18.6° and 50°–30° for different discharge as in Table 2, and as shown in Figure 4 for 50°–18.6° slope change and Figure 5 for 50°–30° slope change configuration for dc/h = 4.6. The simulation results of the air concentration are very close to the experimental results in all curves and fairly close to that predicted by the advection-diffusion model for the air bubbles suggested by Chanson (1997) on a constant sloping chute.
Experimental and simulated dimensionless velocity distribution for steps number −5, −1, +1, +5. +11, +15 and +22 along the 50°–30° slope change for dc/h = 2.6.
But as is shown in all above mentioned figures it is clear that at the pseudo-bottom the CFD results of air concentration are less than experimental ones until the depth of water reaches a quarter of the total depth of water. Also the direction of the curves are parallel to each other when going up towards the surface water and are incorporated approximately near the surface water. For all curves, the cross-section is separate between upstream and downstream steps. Therefore the (-) sign for steps represents a step upstream of the slope change cross-section and the (+) sign represents a step downstream of the slope change cross-section.
The dimensionless velocity distribution (V/V90) profile was acquired at an acquisition time equal to 25 seconds in skimming flow of the upstream and downstream slope change for both 50°–18.6° and 50°–30° slope change. The simulation results are compared with the experimental ones showing that for all curves there is close similarity for each point between the observed and experimental results. The curves increase parallel to each other and they merge near at the surface water as shown in Figure 6 for slope change 50°–18.6° configuration and Figure 7 for slope change 50°–30° configuration. However, at step numbers +1 and +5 in Figure 7 there are few differences between the simulated and observed results, namely the simulation curves ascend regularly meaning the velocity increases regularly from the pseudo-bottom up to the surface water.
Figure 8 (50°–18.6° slope change) and Figure 9 (50°–30° slope change) compare the simulation results and the experimental results for the presented dimensionless dynamic pressure distribution for different points on the stepped spillway. The results show a good agreement with the experimental and numerical simulations in all curves. For some points, few discrepancies can be noted in pressure magnitudes between the simulated and the observed ones, but they are in the acceptable range. Although the experimental data do not completely agree with the simulated results, there is an overall agreement.
Comparison between simulated and experimental results for the dimensionless pressure for steps number −1, −2, −3 and +1, +2 +3 and +20 on the horizontal step faces of 50°–18.6° slope change configuration, for dc/h = 4.6, x is the distance from the step edge.
Comparison between simulated and experimental results for the dimensionless pressure for steps number −1, −2, −3 and +1, +2 and +30, +31 on the horizontal step face of 50°–30° slope change configuration, for dc/h = 4.6, x is the distance from the step edge.
The pressure profiles were acquired at an acquisition time equal to 70 seconds in skimming flow on 50°–18.6°, where p is the measured dynamic pressure, h is step height and ϒ is water specific weight. A negative sign for steps represents a step upstream of the slope change cross-section and a positive sign represents a step downstream of the slope change cross-section.
Figure 10 shows the experimental streamwise development of dimensionless pressure on the 50°–18.6° slope change for dc/h = 4.6, x/l = 0.35 on 50° sloping chute and x/l = 0.3 on 18.6° sloping chute compared with the numerical simulation. It is obvious from Figure 10 that the streamwise development of dimensionless pressure before slope change (steps number −1, −2 and −3) both of the experimental and simulated results are close to each other. However, it is clear that there is a little difference between the results of the streamwise development of dimensionless pressure at step numbers +1, +2 and +3. Moreover, from step number +3 to the end, the curves get close to each other.
Comparison between experimental and simulated results for the streamwise development of the dimensionless pressure on the 50°–18.6° slope change, for dc/h = 4.6, and x/l = 0.35 on 50° sloping chute and x/l = 0.3 on 18.6° sloping chute.
Figure 11 compares the experimental and the numerical results for the streamwise development of the dimensionless pressure on the