Figure 4.24 - Model with virtual valves in the extremities of the geometries to simulate the permeability of the mold promoting a more uniformed filling

Optimization of filling systems for low pressure by Flow-3D

Dissertação de Mestrado
Ciclo de Estudos Integrados Conducentes ao
Grau de Mestre em Engenharia Mecânica
Trabalho efectuado sob a orientação do
Doutor Hélder de Jesus Fernades Puga
Professor Doutor José Joaquim Carneiro Barbosa

ABSTRACT

논문의 일부로 튜터 선택 가능성과 해결해야 할 주제가 설정되는 매개변수를 염두에 두고 개발 주제 ‘Flow- 3D ®에 의한 저압 충전 시스템 최적화’가 선택되었습니다. 이를 위해서는 달성해야 할 목표와 이를 달성하기 위한 방법을 정의하는 것이 필요했습니다.

충전 시스템을 시뮬레이션하고 검증할 수 있는 광범위한 소프트웨어에도 불구하고 Flow-3D®는 시장에서 최고의 도구 중 하나로 표시되어 전체 충전 프로세스 및 행동 표현과 관련하여 탁월한 정확도로 시뮬레이션하는 능력을 입증했습니다.

이를 위해 관련 프로세스를 더 잘 이해하고 충진 시스템 시뮬레이션을 위한 탐색적 기반 역할을 하기 위해 이 도구를 탐색하는 것이 중요합니다. 지연 및 재료 낭비에 반영되는 실제적인 측면에서 충전 장치의 치수를 완벽하게 만드는 비용 및 시간 낭비. 이러한 방식으로 저압 주조 공정에서 충진 시스템을 설계하고 물리적 모델을 탐색하여 특성화하는 방법론을 검증하기 위한 것입니다.

이를 위해 다음 주요 단계를 고려하십시오.

시뮬레이션 소프트웨어 Flow 3D® 탐색;
충전 시스템 모델링;
모델의 매개변수를 탐색하여 모델링된 시스템의 시뮬레이션, 검증 및 최적화.

따라서 연구 중인 압력 곡선과 주조 분석에서 가장 관련성이 높은 정보의 최종 마이닝을 검증하기 위한 것입니다.

사용된 압력 곡선은 수집된 문헌과 이전에 수행된 실제 작업을 통해 얻었습니다. 결과를 통해 3단계 압력 곡선이 층류 충진 체계의 의도된 목적과 관련 속도가 0.5 𝑚/𝑠를 초과하지 않는다는 결론을 내릴 수 있었습니다.

충전 수준이 2인 압력 곡선은 0.5 𝑚/𝑠 이상의 속도로 영역을 채우는 더 난류 시스템을 갖습니다. 열전달 매개변수는 이전에 얻은 값이 주물에 대한 소산 거동을 확증하지 않았기 때문에 연구되었습니다.

이러한 방식으로 주조 공정에 더 부합하는 새로운 가치를 얻었습니다. 달성된 결과는 유사한 것으로 나타난 NovaFlow & Solid®에 의해 생성된 결과와 비교되어 시뮬레이션에서 설정된 매개변수를 검증했습니다. Flow 3D®는 주조 부품 시뮬레이션을 위한 강력한 도구로 입증되었습니다.

As part of the dissertation and bearing in mind the parameters in which the possibility of a choice of tutor and the subject to be addressed is established, the subject for development ’Optimization of filling systems for low pressure by Flow 3D ®’ was chosen. For this it was necessary to define the objectives to achieve and the methods to attain them. Despite the wide range of software able to simulate and validate filling systems, Flow 3D® has been shown as one of the best tools in the market, demonstrating its ability to simulate with distinctive accuracy with respect to the entire process of filling and the behavioral representation of the fluid obtained. To this end, it is important to explore this tool for a better understanding of the processes involved and to serve as an exploratory basis for the simulation of filling systems, simulation being one of the great strengths of the current industry due to the need to reduce costs and time waste, in practical terms, that lead to the perfecting of the dimensioning of filling devices, which are reflected in delays and wasted material. In this way it is intended to validate the methodology to design a filling system in lowpressure casting process, exploring their physical models and thus allowing for its characterization. For this, consider the following main phases: The exploration of the simulation software Flow 3D®; modeling of filling systems; simulation, validation and optimization of systems modeled by exploring the parameters of the models. Therefore, it is intended to validate the pressure curves under study and the eventual mining of the most relevant information in a casting analysis. The pressure curves that were used were obtained through the gathered literature and the practical work previously performed. Through the results it was possible to conclude that the pressure curve with 3 levels meets the intended purpose of a laminar filling regime and associated speeds never exceeding 0.5 𝑚/𝑠. The pressure curve with 2 filling levels has a more turbulent system, having filling areas with velocities above 0.5 𝑚/𝑠. The heat transfer parameter was studied due to the values previously obtained didn’t corroborate the behavior of dissipation regarding to the casting. In this way, new values, more in tune with the casting process, were obtained. The achieved results were compared with those generated by NovaFlow & Solid®, which were shown to be similar, validating the parameters established in the simulations. Flow 3D® was proven a powerful tool for the simulation of casting parts.

키워드

저압, Flow 3D®, 시뮬레이션, 파운드리, 압력-시간 관계,Low Pressure, Flow 3D®, Simulation, Foundry, Pressure-time relation

Figure 4.39 - Values of temperature contours using full energy heat transfer parameter for simula — Figure 4.39 – Values of temperature contours using full energy heat transfer parameter for simula

Figure 4.40 – Comparison between software simulations (a) Flow 3D® simulation,
(b) NovaFlow & Solid® simulation — Figure 4.40 – Comparison between software simulations (a) Flow 3D® simulation, (b) NovaFlow & Solid® simulation

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액체-수소 탱크를 위한 결합된 열역학-유체-역학 솔루션

Coupled thermodynamic-fluid-dynamic solution for a liquid-hydrogen tank

G. D. Grayson

Published Online:23 May 2012 https://doi.org/10.2514/3.26706

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Introduction

ROPELLANT 열 성층화 및 외부 교란에 대한 유체 역학적 반응은 발사체와 우주선 모두에서 중요합니다. 과거에는 결합된 솔루션을 제공할 수 있는 충분한 계산 기술이 부족하여 이러한 문제를 개별적으로 해결했습니다.1

이로 인해 모델링 기술의 불확실성을 허용하기 위해 큰 안전 계수를 가진 시스템이 과도하게 설계되었습니다. 고중력 환경과 저중력 환경 모두에서 작동하도록 설계된 미래 시스템은 기술적으로나 재정적으로 실현 가능하도록 과잉 설계 및 안전 요소가 덜 필요합니다.

이러한 유체 시스템은 열역학 및 유체 역학이 모두 중요한 환경에서 모델의 기능을 광범위하게 검증한 후에만 고충실도 수치 모델을 기반으로 할 수 있습니다. 상용 컴퓨터 코드 FLOW-3D2는 유체 역학 및 열 모델링 모두에서 가능성을 보여주었으며,1 따라서 열역학-유체-역학 엔지니어링 문제에서 결합된 질량, 운동량 및 에너지 방정식을 푸는 데 적합함을 시사합니다.

발사체의 복잡한 액체 가스 시스템에 대한 포괄적인 솔루션을 달성하기 위한 첫 번째 단계로 액체 유체 역학과 열역학을 통합하는 제안된 상단 단계 액체-수소(Lit) 탱크의 간단한 모델이 여기에 제시됩니다. FLOW-3D FLOW-3D 프로그램은 Los Alamos Scientific Laboratory에서 시작되었으며 마커 및 셀 방법에서 파생된 것입니다.3 현재 상태로 가져오기 위해 수년에 걸쳐 광범위한 코드 수정이 이루어졌습니다.2

프로그램은 다음과 같습니다. 일반 Navier-Stokes 방정식을 풀기 위해 수치 근사의 중앙 유한 차분 방법을 사용하는 3차원 유체 역학 솔버입니다. 모멘텀 및 에너지 방정식의 섹션은 특정 응용 프로그램에 따라 활성화 또는 비활성화할 수 있습니다.

코드는 1994년 9월 13일 접수를 인용하기 위해 무액체 표면, 복잡한 용기 기하학, 여러 점성 모델, 표면 장력, 다공성 매체를 통한 흐름 및 응고와 함께 압축성 또는 비압축성 유동 가정을 제공합니다. 1995년 1월 15일에 받은 개정; 1995년 2월 17일 출판 승인.

ROPELLANT thermal stratification and fluid-dynamic response to external disturbances are of concern in both launch vehicles and spacecraft. In the past these problems have been addressed separately for want of sufficient computational technology to provide for coupled solutions.1 This has resulted in overdesigned systems with large safety factors to allow for the uncertainty in modeling techniques. Future systems designed to perform in both highand low-gravity environments will require less overdesign and safety factors to be technically and financially feasible. Such fluid systems can be based on high-fidelity numerical models only after extensive validation of the models’ capabilities in environments where both the thermodynamics and the fluid dynamics are important. The commercial computer code FLOW-3D2 has shown promise in both fluid-dynamic and thermal modeling,1 thus suggesting suitability for solving the coupled mass, momentum, and energy equations in thermodynamic-fluid-dynamic engineering problems. As a first step to achieving a comprehensive solution for complex liquidgas systems in a launch vehicle, a simple model of a proposed upper-stage liquid-hydrogen (Lit) tank incorporating the liquid fluid dynamics and thermodynamics is presented here. FLOW-3D The FLOW-3D program originated at the Los Alamos Scientific Laboratory and is a derivative of the marker-and-cell method.3 Extensive code modifications have been made over the years to bring it to its present state.2 The program is a three-dimensional fluiddynamic solver that uses a central finite-difference method of numerical approximation to solve the general Navier-Stokes equations. Sections of the momentum and energy equations can be enabled or disabled depending on the particular application. The code provides compressible or incompressible flow assumptions with liquid free surfaces, complex container geometries, several viscosity models, surface tension, flow though porous media, and solidification, to cite Received Sept. 13, 1994; revision received Jan. 15, 1995; accepted for publication Feb. 17, 1995. Copyright © 1995 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. *Engineer/Scientist, Propulsion Analysis and Hydraulics, Space Transportation Division, MS 13-3, 5301 Bolsa Avenue. Member AIAA. a few of the possibilities. Further information on FLOW-3D’s capabilities and details of the numerical algorithms can be found in Ref. 2

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Fig. 2 Modeling of bubble point test apparatus (left) and computational grid (righ

Flow-3d를 이용한 표면장력 탱크용메시스크린모델링

Modeling of Mesh Screen for Use in Surface TensionTankUsing Flow-3d Software

Hyuntak Kim․ Sang Hyuk Lim․Hosung Yoon․Jeong-Bae Park*․Sejin Kwon†

ABSTRACT

Mesh screen modeling and liquid propellant discharge simulation of surface tension tank wereperformed using commercial CFD software Flow-3d. 350 × 2600, 400 × 3000 and 510 × 3600 DTW mesh screen were modeled using macroscopic porous media model. Porosity, capillary pressure, and drag
coefficient were assigned for each mesh screen model, and bubble point simulations were performed. The
mesh screen model was validated with the experimental data. Based on the screen modeling, liquidpropellant discharge simulation from PMD tank was performed. NTO was assigned as the liquidpropellant, and void was set to flow into the tank inlet to achieve an initial volume flowrate of
liquid propellant in 3 × 10-3 g acceleration condition. The intial flow pressure drop through the meshscreen was approximately 270 Pa, and the pressure drop increased with time. Liquid propellant
discharge was sustained until the flow pressure drop reached approximately 630 Pa, which was near
the estimated bubble point value of the screen model.

초 록

상용 CFD 프로그램 Flow-3d를 활용하여, 표면 장력 탱크 적용을 위한 메시 스크린의 모델링 및 추진제 배출 해석을 수행하였다. Flow-3d 내 거시적 다공성 매체 모델을 사용하였으며, 350 × 2600, 400× 3000, 510 × 3600 DTW 메시 스크린에 대한 공극률, 모세관압, 항력계수를 스크린 모델에 대입 후, 기포점 측정 시뮬레이션을 수행하였다.

시뮬레이션 결과를 실험 데이터와 비교하였으며, 메시 스크린 모델링의 적절성을 검증하였다. 이를 기반으로 스크린 모델을 포함한 PMD 구조체에 대한 추진제 배출 해석을 수행하였다. 추진제는 액상의 NTO를 가정하였으며, 3 × 10-3 g 가속 조건에서 초기 유량을만족하도록 void를 유입시켰다. 메시 스크린을 통한 차압은 초기 약 270 Pa에서 시간에 따라 증가하였으며, 스크린 모델의 예상 기포점과 유사한 630 Pa에 이르기까지 액상 추진제 배출을 지속하였다.

Key Words

Surface Tension Tank(표면장력 탱크), Propellant Management Device(추진제 관리 장치),
Mesh Screen(메시 스크린), Porous Media Model(다공성 매체 모델), Bubble Point(기포점)

서론

우주비행체를 미소 중력 조건 내에서 운용하 는 경우, 가압 기체가 액상의 추진제와 혼합되어 엔진으로 공급될 우려가 있으므로 이를 방지하 기 위한 탱크의 설계가 필요하다.

다이어프램 (Diaphragm), 피스톤(Piston) 등 다양한 장치들 이 활용되고 있으며, 이 중 표면 장력 탱크는 내 부의 메시 스크린(Mesh screen), 베인(Vane) 등 의 구조체에서 추진제의 표면장력을 활용함으로 써 액상 추진제의 이송 및 배출을 유도하는 방 식이다.

표면 장력 탱크는 구동부가 없는 구조로 신뢰성이 높고, 전 부분을 티타늄 등의 금속 재 질로 구성함으로써 부식성 추진제의 사용 조건 에서도 장기 운용이 가능한 장점이 있다. 위에서 언급한 메시 스크린(Mesh screen)은 수 십 마이크로미터 두께의 금속 와이어를 직조한 다공성 재질로 표면 장력 탱크의 핵심 구성 요소 중 하나이다.

미세 공극 상 추진제의 표면장력에 의해 기체와 액체 간 계면을 일정 차압 내에서 유지시킬 수 있다. 이러한 성질로 인해 일정 조 건에서 가압 기체가 메시 스크린을 통과하지 못 하게 되고, 스크린을 탱크 유로에 설치함으로써 액상의 추진제 배출을 유도할 수 있다.

메시 스크린이 가압 기체를 통과시키기 직전 의 기체-액체 계면에 형성되는 최대 차압을 기포 점 (Bubble point) 이라 칭하며, 메시 스크린의 주 요 성능 지표 중 하나이다. IPA, 물, LH2, LCH4 등 다양한 기준 유체 및 추진제, 다양한 메시 스 크린 사양에 대해 기포점 측정 관련 실험적 연 구가 이루어져 왔다 [1-3].

위 메시 스크린을 포함하여 표면 장력 탱크 내 액상의 추진제 배출을 유도하는 구조물 일체 를 PMD(Propellant management device)라 칭하 며, 갤러리(Gallery), 베인(Vane), 스펀지(Sponge), 트랩(Trap) 등 여러 종류의 구조물에 대해 각종 형상 변수를 내포한다[4, 5].

따라서 다양한 파라미터를 고려한 실험적 연구는 제약이 따를 수 있으며, 베인 등 상대적으로 작은 미소 중력 조건에서 개방형 유로를 활용하는 경우 지상 추진제 배출 실험이 불가능하다[6]. 그러므로 CFD를 통한 표면장력 탱크 추진제 배출 해석은 다양한 작동 조건 및 PMD 형상 변수에 따른 추진제 거동을 이해하고, 탱크를 설계하는 데 유용하게 활용될 수 있다.

상기 추진제 배출 해석을 수행하기 위해서는 핵심 요소 중 하나인 메시 스크린에 대한 모델링이 필수적이다. Chato, McQuillen 등은 상용 CFD 프로그램인 Fluent를 통해, 갤러리 내 유동 시뮬레이션을 수행하였으며, 이 때 메시 스크린에 ‘porous jump’ 경계 조건을 적용함으로써 액상의 추진제가 스크린을 통과할 때 생기는 압력 강하를 모델링하였다[7, 8].

그러나 앞서 언급한 메시 스크린의 기포점 특성을 모델링한 사례는 찾아보기 힘들다. 이는 스크린을 활용하는 표면 장력 탱크 내 액상 추진제 배출 현상을 해석적으로 구현하기 위해 반드시 필요한 부분이다. 본 연구에서는 자유표면 해석에 상대적으로 강점을 지닌 상용 CFD 프로그램 Flow-3d를 사용하여, 메시 스크린을 모델링하였다.

거시적 다공성 매체 모델(Macroscopic porous mediamodel)을 활용하여 메시 스크린 모델 영역에 공극률(Porosity), 모세관압(Capillary pressure), 항력 계수(Drag coefficient)를 지정하고, 이를 기반으로 기포점 측정 시뮬레이션을 수행, 해석 결과와 실험 데이터 간 비교 및 검증을 수행하였다.

이를 기반으로 메시 스크린 및 PMD구조체를 포함한 탱크의 추진제 배출 해석을 수행하고, 기포점 특성의 반영 여부를 확인하였다.

Fig. 1 Real geometry-based mesh screen model (left)
and mesh screen model based on macroscopic
porous media model in Flow-3d (righ — Fig. 1 Real geometry-based mesh screen model (left) and mesh screen model based on macroscopic porous media model in Flow-3d (righ

Fig. 3 Modeling of sump in a tank (left) and lower part
of the sump structure (right) — Fig. 3 Modeling of sump in a tank (left) and lower part of the sump structure (right)

참 고 문 헌

David J. C and Maureen T. K, ScreenChannel Liquid Aquisition Devices for Cryogenic Propellants” NASA-TM-2005- 213638, 2005
Hartwig, J., Mann, J. A. Jr., Darr, S. R., “Parametric Analysis of the LiquidHydrogen and Nitrogen Bubble Point Pressure for Cryogenic Liquid AcquisitionDevices”, Cryogenics, Vol. 63, 2014, pp. 25-36
Jurns, J. M., McQuillen, J. B.,BubblePoint Measurement with Liquid Methane of a Screen Capillary Liquid AcquisitionDevice”, NASA-TM-2009-215496, 2009
Jaekle, D. E. Jr., “Propellant Management Device: Conceptual Design and Analysis: Galleries”, AIAA 29th Joint PropulsionConference, AIAA-97-2811, 1997
Jaekle, D. E. Jr., “Propellant Management Device: Conceptual Design and Analysis: Traps and Troughs”, AIAA 31th Joint Propulsion Conference, AIAA-95-2531, 1995
Yu, A., Ji, B., Zhuang, B. T., Hu, Q., Luo, X. W., Xu, H. Y., “Flow Analysis inaVane-type Surface Tension Propellant Tank”, IOP Conference Series: MaterialsScience and Engineering, Vol. 52, No. 7, – 990 – 2013, Article number: 072018
Chato, D. J., McQuillen, J. B., Motil, B. J., Chao, D. F., Zhang, N., CFD simulation of Pressure Drops in Liquid Acquisition Device Channel with Sub-Cooled Oxygen”, World Academy of Science, Engineering and Technology, Vol. 3, 2009, pp. 144-149
McQuillen, J. B., Chao, D. F., Hall, N. R., Motil, B. J., Zhang, N., CFD simulation of Flow in Capillary Flow Liquid Acquisition Device Channel”, World Academy of Science, Engineering and Technology, Vol. 6, 2012, pp. 640-646
Hartwig, J., Chato, D., McQuillen, J.,  Screen Channel LAD Bubble Point Tests in Liquid Hydrogen”, International Journal of Hydrogen Energy, Vol. 39, No. 2, 2014, pp. 853-861
Fischer, A., Gerstmann, J., “Flow Resistance of Metallic Screens in Liquid, Gaseous and Cryogenic Flow”, 5th European Conferencefor Aeronautics and Space Sciences, Munich, Germany, 2013
Fries, N., Odic, K., Dreyer, M., Wickingof Perfectly Wetting Liquids into a MetallicMesh”, 2nd International Conference onPorous Media and its Applications inScience and Engineering, 2007
Seo, M, K., Kim, D, H., Seo, C, W., Lee, S, Y., Jang, S, P., Koo, J., “Experimental Study of Pressure Drop in CompressibleFluid through Porous Media”, Transactionsof the Korean Society of Mechanical Engineers – B, Vol. 37, No. 8, pp. 759-765, 2013.
Hartwig, J., Mann, J. A., “Bubble Point Pressures of Binary Methanol/Water Mixtures in Fine-Mesh Screens”, AlChEJournal, Vol. 60, No. 2, 2014, pp. 730-739

전체 원문보기

이종 금속 인터커넥트의 펄스 레이저 용접을 위한 가공 매개변수 최적화

Optimization of processing parameters for pulsed laser welding of dissimilar metal interconnects

본 논문은 독자의 편의를 위해 기계번역된 내용이어서 자세한 내용은 원문을 참고하시기 바랍니다.

전체 원문보기

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NguyenThi Tien^aYu-LungLo^abM.Mohsin Raza^aCheng-YenChen^cChi-PinChiu^c

^aNational Cheng Kung University, Department of Mechanical Engineering, Tainan, Taiwan

^bNational Cheng Kung University, Academy of Innovative Semiconductor and Sustainable Manufacturing, Tainan, Taiwan

^cJum-bo Co., Ltd, Xinshi District, Tainan, Taiwan

Abstract

워블 전략이 포함된 펄스 레이저 용접(PLW) 방법을 사용하여 알루미늄 및 구리 이종 랩 조인트의 제조를 위한 최적의 가공 매개변수에 대해 실험 및 수치 조사가 수행됩니다. 피크 레이저 출력과 접선 용접 속도의 대표적인 조합 43개를 선택하기 위해 원형 패킹 설계 알고리즘이 먼저 사용됩니다.

선택한 매개변수는 PLW 프로세스의 전산유체역학(CFD) 모델에 제공되어 용융 풀 형상(즉, 인터페이스 폭 및 침투 깊이) 및 구리 농도를 예측합니다. 시뮬레이션 결과는 설계 공간 내에서 PLW 매개변수의 모든 조합에 대한 용융 풀 형상 및 구리 농도를 예측하기 위해 3개의 대리 모델을 교육하는 데 사용됩니다.

마지막으로, 대체 모델을 사용하여 구성된 처리 맵은 용융 영역에 균열이나 기공이 없고 향상된 기계적 및 전기적 특성이 있는 이종 조인트를 생성하는 PLW 매개변수를 결정하기 위해 세 가지 품질 기준에 따라 필터링됩니다.

제안된 최적화 접근법의 타당성은 최적의 용접 매개변수를 사용하여 생성된 실험 샘플의 전단 강도, 금속간 화합물(IMC) 형성 및 전기 접촉 저항을 평가하여 입증됩니다.

결과는 최적의 매개변수가 1209N의 높은 전단 강도와 86µΩ의 낮은 전기 접촉 저항을 생성함을 확인합니다. 또한 용융 영역에는 균열 및 기공과 같은 결함이 없습니다.

An experimental and numerical investigation is performed into the optimal processing parameters for the fabrication of aluminum and copper dissimilar lap joints using a pulsed laser welding (PLW) method with a wobble strategy. A circle packing design algorithm is first employed to select 43 representative combinations of the peak laser power and tangential welding speed. The selected parameters are then supplied to a computational fluidic dynamics (CFD) model of the PLW process to predict the melt pool geometry (i.e., interface width and penetration depth) and copper concentration. The simulation results are used to train three surrogate models to predict the melt pool geometry and copper concentration for any combination of the PLW parameters within the design space. Finally, the processing maps constructed using the surrogate models are filtered in accordance with three quality criteria to determine the PLW parameters that produce dissimilar joints with no cracks or pores in the fusion zone and enhanced mechanical and electrical properties. The validity of the proposed optimization approach is demonstrated by evaluating the shear strength, intermetallic compound (IMC) formation, and electrical contact resistance of experimental samples produced using the optimal welding parameters. The results confirm that the optimal parameters yield a high shear strength of 1209 N and a low electrical contact resistance of 86 µΩ. Moreover, the fusion zone is free of defects, such as cracks and pores.

Fig. 1. Schematic illustration of Al-Cu lap-joint arrangement

Fig. 5. Lap-shear mechanical tests: (a) experimental setup and specimen dimensions, and (b) two different failures of lap-joint welding.
N. Thi Tien et al. — Fig. 5. Lap-shear mechanical tests: (a) experimental setup and specimen dimensions, and (b) two different failures of lap-joint welding. N. Thi Tien et al.

Fig. 9. Simulation and experimental results for melt pool profile. (a) Simulation results for melt pool cross-section, and (b) OM image of melt pool cross-section. (Note that laser processing parameter of 830 W and 565 mm/s is chosen.).

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Fig. 1. Schematic figure showing the PREP with additional gas flowing on the end face of electrode.

플라즈마 회전 전극 공정 중 분말 형성에 대한 공정 매개변수 및 냉각 가스의 영향

Yujie Cui^a Yufan Zhao^a1Haruko Numata^b Kenta Yamanaka^a Huakang Bian^aKenta Aoyagi^aAkihikoChiba^a
^aInstitute for Materials Research, Tohoku University, Sendai 980-8577, Japan^bDepartment of Materials Processing, Graduate School of Engineering, Tohoku University, Sendai 980-8577, Japan

Highlights

•The limitation of increasing the rotational speed in decreasing powder size was clarified.

•Cooling and disturbance effects varied with the gas flowing rate.

•Inclined angle of the residual electrode end face affected powder formation.

•Additional cooling gas flowing could be applied to control powder size.

Abstract

The plasma rotating electrode process (PREP) is rapidly becoming an important powder fabrication method in additive manufacturing. However, the low production rate of fine PREP powder limits the development of PREP. Herein, we investigated different factors affecting powder formation during PREP by combining experimental methods and numerical simulations. The limitation of increasing the rotation electrode speed in decreasing powder size is attributed to the increased probability of adjacent droplets recombining and the decreased tendency of granulation. The effects of additional Ar/He gas flowing on the rotational electrode on powder formation is determined through the cooling effect, the disturbance effect, and the inclined effect of the residual electrode end face simultaneously. A smaller-sized powder was obtained in the He atmosphere owing to the larger inclined angle of the residual electrode end face compared to the Ar atmosphere. Our research highlights the route for the fabrication of smaller-sized powders using PREP.

플라즈마 회전 전극 공정(PREP)은 적층 제조 에서 중요한 분말 제조 방법으로 빠르게 자리잡고 있습니다. 그러나 미세한 PREP 분말의 낮은 생산율은 PREP의 개발을 제한합니다. 여기에서 우리는 실험 방법과 수치 시뮬레이션을 결합하여 PREP 동안 분말 형성에 영향을 미치는 다양한 요인을 조사했습니다. 분말 크기 감소에서 회전 전극 속도 증가의 한계는 인접한 액적 재결합 확률 증가 및 과립화 경향 감소에 기인합니다.. 회전 전극에 흐르는 추가 Ar/He 가스가 분말 형성에 미치는 영향은 냉각 효과, 외란 효과 및 잔류 전극 단면의 경사 효과를 통해 동시에 결정됩니다. He 분위기에서는 Ar 분위기에 비해 잔류 전극 단면의 경사각이 크기 때문에 더 작은 크기의 분말이 얻어졌다. 우리의 연구는 PREP를 사용하여 더 작은 크기의 분말을 제조하는 경로를 강조합니다.

Keywords

Plasma rotating electrode process

Ti-6Al-4 V alloy, Rotating speed, Numerical simulation, Gas flowing, Powder size

Introduction

With the development of additive manufacturing, there has been a significant increase in high-quality powder production demand [1,2]. The initial powder characteristics are closely related to the uniform powder spreading [3,4], packing density [5], and layer thickness observed during additive manufacturing [6], thus determining the mechanical properties of the additive manufactured parts [7,8]. Gas atomization (GA) [9–11], centrifugal atomization (CA) [12–15], and the plasma rotating electrode process (PREP) are three important powder fabrication methods.

Currently, GA is the dominant powder fabrication method used in additive manufacturing [16] for the fabrication of a wide range of alloys [11]. GA produces powders by impinging a liquid metal stream to droplets through a high-speed gas flow of nitrogen, argon, or helium. With relatively low energy consumption and a high fraction of fine powders, GA has become the most popular powder manufacturing technology for AM.

The entrapped gas pores are generally formed in the powder after solidification during GA, in which the molten metal is impacted by a high-speed atomization gas jet. In addition, satellites are formed in GA powder when fine particles adhere to partially molten particles.

The gas pores of GA powder result in porosity generation in the additive manufactured parts, which in turn deteriorates its mechanical properties because pores can become crack initiation sites [17]. In CA, a molten metal stream is poured directly onto an atomizer disc spinning at a high rotational speed. A thin film is formed on the surface of the disc, which breaks into small droplets due to the centrifugal force. Metal powder is obtained when these droplets solidify.

Compared with GA powder, CA powder exhibits higher sphericity, lower impurity content, fewer satellites, and narrower particle size distribution [12]. However, very high speed is required to obtain fine powder by CA. In PREP, the molten metal, melted using the plasma arc, is ejected from the rotating rod through centrifugal force. Compared with GA powder, PREP-produced powders also have higher sphericity and fewer pores and satellites [18].

For instance, PREP-fabricated Ti6Al-4 V alloy powder with a powder size below 150 μm exhibits lower porosity than gas-atomized powder [19], which decreases the porosity of additive manufactured parts. Furthermore, the process window during electron beam melting was broadened using PREP powder compared to GA powder in Inconel 718 alloy [20] owing to the higher sphericity of the PREP powder.

In summary, PREP powder exhibits many advantages and is highly recommended for powder-based additive manufacturing and direct energy deposition-type additive manufacturing. However, the low production rate of fine PREP powder limits the widespread application of PREP powder in additive manufacturing.

Although increasing the rotating speed is an effective method to decrease the powder size [21,22], the reduction in powder size becomes smaller with the increased rotating speed [23]. The occurrence of limiting effects has not been fully clarified yet.

Moreover, the powder size can be decreased by increasing the rotating electrode diameter [24]. However, these methods are quite demanding for the PREP equipment. For instance, it is costly to revise the PREP equipment to meet the demand of further increasing the rotating speed or electrode diameter.

Accordingly, more feasible methods should be developed to further decrease the PREP powder size. Another factor that influences powder formation is the melting rate [25]. It has been reported that increasing the melting rate decreases the powder size of Inconel 718 alloy [26].

In contrast, the powder size of SUS316 alloy was decreased by decreasing the plasma current within certain ranges. This was ascribed to the formation of larger-sized droplets from fluid strips with increased thickness and spatial density at higher plasma currents [27]. The powder size of NiTi alloy also decreases at lower melting rates [28]. Consequently, altering the melting rate, varied with the plasma current, is expected to regulate the PREP powder size.

Furthermore, gas flowing has a significant influence on powder formation [27,29–31]. On one hand, the disturbance effect of gas flowing promotes fluid granulation, which in turn contributes to the formation of smaller-sized powder [27]. On the other hand, the cooling effect of gas flowing facilitates the formation of large-sized powder due to increased viscosity and surface tension. However, there is a lack of systematic research on the effect of different gas flowing on powder formation during PREP.

Herein, the authors systematically studied the effects of rotating speed, electrode diameter, plasma current, and gas flowing on the formation of Ti-6Al-4 V alloy powder during PREP as additive manufactured Ti-6Al-4 V alloy exhibits great application potential [32]. Numerical simulations were conducted to explain why increasing the rotating speed is not effective in decreasing powder size when the rotation speed reaches a certain level. In addition, the different factors incited by the Ar/He gas flowing on powder formation were clarified.

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재료 분사를 통한 다중 재료 3D 유체 장치의 액체-고체 공동 인쇄

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

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

Abstract

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

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

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

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

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

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

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

Keywords

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

원문보기

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

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

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

ABSTRACT

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

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

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

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

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

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

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

Figure 1: a) Computational domain for single track L-PBF which includes a 200 μm thick substrate and 45 μm powder layer thickness b) 3D temperature contour plot after scanning a single track with melt pool contours at two locations along the scanning direction where the green region indicates the melted regions.

Figure 2: Main effects plot of uncertain parameters: absorptivity, recoil pressure coefficient and laser beam radius on the melt pool dimensions (width and depth)

Figure 4: Validation of Numerical model with Recoil pressure coefficient B= 20, absorptivity = 0.45 and a) laser beam radius = 15 µm b) laser beam radius = 20 µm

CONCLUSION

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

REFERENCES

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

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Effect of roughness on separation zone dimensions.

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

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

Maryam Bagheri; Seyed M. Ali Zomorodian; Masih Zolghadr; H. Md. Azamathulla; C. Venkata Siva Rama Prasad

Abstract

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

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

HIGHLIGHTS

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

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

Keywords

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

INTRODUCTION

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

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

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

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

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

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

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

Laboratory channel dimensions.

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

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

METHOD

Experimental setup

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

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

Roughness plates.

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

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

Numerical simulation

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

(1)where u, v, and w represent the velocity components in the x, y, and z directions, respectively; A_x, A_y, and A_z are the surface flow fractions in the x, y, and z directions, respectively; V_F denotes flow volume fraction; r is the density of the fluid; t is time; and R_sor refers to the source of the mass. Equations (2)–(4) show momentum equations in x, y and z dimensions respectively :

(2)

(3)

(4)where G_x, G_y, and G_z are the accelerations caused by gravity in the x, y, and z directions, respectively; and f_x, f_y, and f_z are the accelerations caused by viscosity in the x, y, and z directions, respectively.

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

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

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

Hydraulic conditions of the flow

Q(L/s)	Fr	Y₁ (m)	Q₂/Q₁
16	0.449	0.09	0.22
18	0.335	0.09	0.61
21	0.242	0.09	0.71
23	0.180	0.09	1.04

RESULTS AND DISCUSSION

Experimental results

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

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

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

Effect of roughness on separation zone dimensions.

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

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

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

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

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

Decrease percentage of separation zone area in 5 cm drop

Fr	n=0.011	n=0.017	n=0.023	n=0.028	n=0.030	n=0.032
0.08	10.56	11.06	25.27	33.03	35.57	36.5
0.121	7.66	11.14	11.88	15.93	34.59	36.25
0.353	1.38	2.63	8.17	14.39	31.20	31.29
0.362	3	11.54	19.56	25.73	37.89	38.31

Table 3

Decrease percentage of separation zone area in 10 cm drop

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

Figure 6 VIEW LARGE DOWNLOAD SLIDE

Effect of drop implementation on separation zone dimensions.

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

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

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

Figure 7 VIEW LARGE DOWNLOAD SLIDE

Combined effect of roughness and drop on separation zone dimensions.

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

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

Velocity profiles for various roughness coefficients along turnout width.

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

Effect of roughness on separation zone dimensions in numerical study.

Numerical results

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

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

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

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

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

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

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

Exprimental and numerical measured velocity.

CONCLUSION

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

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

DATA AVAILABILITY STATEMENT

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

REFERENCES

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

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곡면에 GMAW용 용접 비드의 형성 특성 및 제어 방법

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

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Abstract

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

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

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

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

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

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

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

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

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

Keywords

Molten pool behaviors
GMAW-based WAAM
Deposition with weave welding
Welding on curved surface

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

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

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

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

Abstract

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

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

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

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

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

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

그 대가로 표면 장력을 낮추고 용융 풀 균일성에 영향을 미칩니다.

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

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

Figure 1. Powder bed schematic with voids.

Figure 3. Temperature field contour formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.

Figure 4. Detailed view of shallow depth melt mode with temperature field at 0.695 ms

Figure 5. Melt flow stream traces formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms

Figure 6. Density evolution of the melt pool at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.

Figure 7. Un-melted and melted regions at different time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms

Figure 8. Transformation from shallow depth melt flow to deep keyhole formation when laser power increased from (a) 170 W to (b) 200 W

Figure 9. Stream traces and laser beam multiple reflections in deep keyhole melt flow mode

Figure 10. A comparison between analytical and CFD simulation results for peak thermal distribution value in the deep keyhole formation

Figure 11. A comparison among experiments [49], CFD and analytical simulations for deep keyhole top width and bottom width

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nanomaterials-11-03284-v2 다운로드

Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump

낮은 레이놀즈 수 유압 점프의 수치 모델링에서 OpenFOAM 및 FLOW-3D의 성능 평가

ArnauBayon^a DanielValero^b RafaelGarcía-Bartual^a Francisco JoséVallés-Morán^a P. AmparoLópez-Jiménez^a

Abstract

A comparative performance analysis of the CFD platforms OpenFOAM and FLOW-3D is presented, focusing on a 3D swirling turbulent flow: a steady hydraulic jump at low Reynolds number. Turbulence is treated using RANS approach RNG k-ε. A Volume Of Fluid (VOF) method is used to track the air–water interface, consequently aeration is modeled using an Eulerian–Eulerian approach. Structured meshes of cubic elements are used to discretize the channel geometry. The numerical model accuracy is assessed comparing representative hydraulic jump variables (sequent depth ratio, roller length, mean velocity profiles, velocity decay or free surface profile) to experimental data. The model results are also compared to previous studies to broaden the result validation. Both codes reproduced the phenomenon under study concurring with experimental data, although special care must be taken when swirling flows occur. Both models can be used to reproduce the hydraulic performance of energy dissipation structures at low Reynolds numbers.

CFD 플랫폼 OpenFOAM 및 FLOW-3D의 비교 성능 분석이 3D 소용돌이치는 난류인 낮은 레이놀즈 수에서 안정적인 유압 점프에 초점을 맞춰 제시됩니다. 난류는 RANS 접근법 RNG k-ε을 사용하여 처리됩니다.

VOF(Volume Of Fluid) 방법은 공기-물 계면을 추적하는 데 사용되며 결과적으로 Eulerian-Eulerian 접근 방식을 사용하여 폭기가 모델링됩니다. 입방체 요소의 구조화된 메쉬는 채널 형상을 이산화하는 데 사용됩니다. 수치 모델 정확도는 대표적인 유압 점프 변수(연속 깊이 비율, 롤러 길이, 평균 속도 프로파일, 속도 감쇠 또는 자유 표면 프로파일)를 실험 데이터와 비교하여 평가됩니다.

모델 결과는 또한 결과 검증을 확장하기 위해 이전 연구와 비교됩니다. 소용돌이 흐름이 발생할 때 특별한 주의가 필요하지만 두 코드 모두 실험 데이터와 일치하는 연구 중인 현상을 재현했습니다. 두 모델 모두 낮은 레이놀즈 수에서 에너지 소산 구조의 수리 성능을 재현하는 데 사용할 수 있습니다.

Keywords

CFDRANS, OpenFOAM, FLOW-3D ,Hydraulic jump, Air–water flow, Low Reynolds number

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Fig. 2. Semi-Lagrangian cellwise advection. (a) Forward advection scheme, (b) Backward advection scheme.

Three-dimensional cellwise conservative unsplit geometric VOF schemes

3차원 셀별 보수 미분할 기하학적 VOF 체계

Raphaël Comminal, JonSpangenberg

Abstract

This work presents two unsplit geometric VOF schemes that extend the two-dimensional cellwise conservative unsplit (CCU) scheme [Comminal et al., J. Comput. Phys. 283 (2015) 582–608] to three dimensions. The novelty of the 3D-CCU schemes lies in the representation of the streaksurfaces of donating regions by polyhedral surfaces whose vertices are calculated with the 4th order Runge-Kutta scheme. Moreover, the advected liquid volumes are computed using a truncation algorithm [López et al., J. Comput. Phys. 392 (2019) 666–693] suited for arbitrary non-convex and self-intersecting polyhedra, which removes the need for tetrahedral decomposition. The 3D-CCU advection schemes were coupled to three interface reconstruction methods (Youngs’ method, the Mixed Youngs-Centered scheme, and the Least-Square Fit algorithm). The resulting VOF methods were tested in classical benchmark advection tests, including translation, rigid-body rotation, shear and deformation flows. The proposed 3D-CCU schemes conserve the liquid volume and maintain the physical boundedness of liquid volume fractions to the machine precision. The 3D-CCU schemes perform favorably compared to other unsplit geometric VOF schemes when coupled to Youngs’ interface reconstruction method. Moreover, the 3D-CCU schemes coupled to the Least-Square Fit algorithm are more accurate than most other VOF schemes that use a second-order accurate interface reconstruction, except those where a 3D extension of the Mosso-Swartz interface reconstruction is employed. The comparison of the different VOF schemes highlights the importance of coupling accurate interface reconstruction methods with accurate unsplit advection schemes.

이 연구는 2 차원 CCU (Cellwise Conservative Unsplit) 방식을 확장하는 두 가지 분할되지 않은 기하학적 VOF 방식을 제시합니다 [Comminal et al., J. Comput. Phys. 283 (2015) 582–608]을 3 차원으로 변경했습니다. 3D-CCU 체계의 참신함은 4 차 Runge-Kutta 체계로 정점이 계산되는 다면체 표면으로 기부 지역의 줄무늬 표면을 표현하는 데 있습니다.

더욱, 가변 액체 부피는 절단 알고리즘을 사용하여 계산됩니다 [López et al., J. Comput. Phys. 392 (2019) 666–693]은 임의의 볼록하지 않고 자기 교차하는 다면체에 적합하며, 이는 사면체 분해의 필요성을 제거합니다. 3D-CCU 이류 계획은 세 가지 인터페이스 재구성 방법 (Youngs의 방법, Mixed Youngs-Centered 계획 및 Least-Square Fit 알고리즘)과 결합되었습니다. 결과 VOF 방법은 평행 이동, 강체 회전, 전단 및 변형 흐름을 포함한 고전적인 벤치 마크 이류 테스트에서 테스트되었습니다.

제안된 3D-CCU 방식은 액체 부피를 보존하고 기계 정밀도에 대한 액체 부피 분율의 물리적 경계를 유지합니다. 3D-CCU 방식은 Youngs의 인터페이스 재구성 방식과 결합 할 때 다른 분할되지 않은 기하학적 VOF 방식에 비해 우수한 성능을 발휘합니다.

또한 Least-Square Fit 알고리즘과 결합 된 3D-CCU 체계는 Mosso-Swartz 인터페이스 재구성의 3D 확장이 사용되는 경우를 제외하고 2 차 정확한 인터페이스 재구성을 사용하는 대부분의 다른 VOF 체계보다 더 정확합니다. 서로 다른 VOF 체계의 비교는 정확한 인터페이스 재구성 방법과 정확한 분할되지 않은 이류 체계를 결합하는 것의 중요성을 강조합니다.

Keywords

Volume-of-fluid methodUnsplit geometric schemeCellwise advectionSemi-Lagrangian trackingVolume conservation

Fig. 1. Eulerian fluxwise advection. (a) Positive donating region with respect to the left cell; (b) Negative donating region; (c) Intersection of a donating region with the cell's face, yielding a positive and a negative region; (d) Temporally-consistent donating regions equivalent to a cellwise advection; (e) Temporal inconsistency of adjacent donating regions. — Fig. 1. Eulerian fluxwise advection. (a) Positive donating region with respect to the left cell; (b) Negative donating region; (c) Intersection of a donating region with the cell’s face, yielding a positive and a negative region; (d) Temporally-consistent donating regions equivalent to a cellwise advection; (e) Temporal inconsistency of adjacent donating regions.

Fig. 3. (a) Cartesian grid cell. (b) Images of the cell's vertices with ruled surfaces. (c) Polyhedral cell's image with triangulated faces. — Fig. 3. (a) Cartesian grid cell. (b) Images of the cell’s vertices with ruled surfaces. (c) Polyhedral cell’s image with triangulated faces.

Fig. 4. Construction of donating regions. (a) Streakline of a cell's vertex P0 represented by the 2-segment polygonal line P0–P1/2–P1. (b) Triangulated streaksurface of a cell's edge P0Q0. (c) Streaktube of a cell's face P0Q0R0S0. (d) Pyramidal volume flux correction ⁎ capping the donating region of the face P0Q0R0S0. — Fig. 4. Construction of donating regions. (a) Streakline of a cell’s vertex P0 represented by the 2-segment polygonal line P0–P1/2–P1. (b) Triangulated streaksurface of a cell’s edge P0Q0. (c) Streaktube of a cell’s face P0Q0R0S0. (d) Pyramidal volume flux correction ⁎ capping the donating region of the face P0Q0R0S0.

Fig. 5. Interface reconstruction. (a) PLIC polygon in the grid cell, (b) Non-planar image of the PLIC polygon inside the cell's image by isomorphism, (c) Planar PLIC inside the cell's image by computation of the average normal vector. (Triangulation of the cell's image faces are omitted for clarity.) — Fig. 5. Interface reconstruction. (a) PLIC polygon in the grid cell, (b) Non-planar image of the PLIC polygon inside the cell’s image by isomorphism, (c) Planar PLIC inside the cell’s image by computation of the average normal vector. (Triangulation of the cell’s image faces are omitted for clarity.)

Fig. 6. Convergence of the geometric errors in the translation tests.

Fig. 7. Reconstructed PLIC polygons (in light blue) superimposed to the exact sphere position (in dark blue) at the end of the rotation tests for the LSF method and CFL = 1.

Fig. 8. Reconstructed PLIC polygons in the shear tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs' method. — Fig. 8. Reconstructed PLIC polygons in the shear tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs’ method.

Fig. 9. Reconstructed PLIC polygons in the deformation tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs' method. — Fig. 9. Reconstructed PLIC polygons in the deformation tests, at Tf/2 (top row) and Tf (bottom row). Blue polygons are computed with the LSF procedure; green polygons with centered column differences; red polygons with Youngs’ method.

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1
This definition of the CFL number is different from the usual definition used in multi-dimensional algebraic advection schemes. However, the component-wise definition is more meaningful in the context of geometric VOF schemes, because it determines the number of layers of cells around the interfacial cells where the liquid volume fractions need to be updated.

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Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics

전산 유체 역학을 사용하여 GMAW에 대한 Marangoni 흐름에서 슬래그 이동의 수치 시뮬레이션

Dae-WonCho^a Yeong-DoPark^b MuralimohanCheepu^c^aBusan Machinery Research Center, Korea Institute of Machinery and Materials, 48, Mieumsandan 5-ro 41beon-gil, Gangseo-gu, Busan 46744, Republic of Korea^bDepartment of Advanced Materials Engineering, Dong-Eui University, Busan, Republic of Korea^cSuper-TIG Welding Co., Limited, Busan, Republic of Korea

Keywords : Marangoni flowMolten slag movementMolten pool behavorSurface tension gradient

Abstract

이 연구는 전산 유체 역학을 이용하여 스프레이 모드 가스 금속 아크 용접에서 생성되는 산화물인 용융 슬래그의 거동을 분석했습니다. 주로 규산염 (SiO2)으로 구성된 용융 슬래그는 용융 풀 표면에 있습니다. 일반적으로 용융 슬래그는 아크 플라즈마 경계 주변에서 생성된다고 가정합니다.

따라서 이 연구의 수치 시뮬레이션에서 슬래그는 특정 밀도와 크기를 가진 구형 입자로 모델링됩니다. Marangoni 유동 효과를 비교하기 위해 이 연구는 표면 장력 구배가 다른 두 가지 사례 (양수 및 음수)를 조사했습니다. 수치 시뮬레이션과 실험 결과 모두 음의 표면 장력 구배가 비드 가장자리에 갇힌 슬래그를 형성하는 반면 양의 표면 장력 구배는 상단 표면의 중앙에 갇힌 슬래그를 형성하는 것으로 나타났습니다.

This study analyzed the behavior of molten slag, which is an oxide generated during spray mode gas metal arc welding, with computational fluid dynamics. The molten slag, composed mainly of silicate (SiO₂), is located on the molten pool surface. It is generally assumed that the molten slag is generated around the arc plasma boundary. Therefore, in the numerical simulation in this study the slag is modeled as a spherical particle, which has a specific density and size. To compare the Marangoni flow effect, this study investigated two different cases where the surface tension gradients were different (positive and negative). In both the numerical simulation and experimental results it was found that negative surface tension gradient formed trapped slag on the bead edge while the positive surface tension gradient formed trapped slag on the center of the top surface.

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Weld bead surface images showing the slag formation location for (a) wire 1 and (b) wire 2.

The effect of alloying elements of gas metal arc welding (GMAW) wire on weld pool flow and slag formation location in cold metal transfer (CMT)

가스 금속 아크 용접 (GMAW) 와이어의 합금 원소가 CMT (Cold Metal Transfer)에서 용접 풀 흐름 및 슬래그 형성 위치에 미치는 영향

Md. R. U. Ahsan1,3, Muralimohan. Cheepu2, Yeong-Do Park* 2,3
1Department of Mechanical Engineering, International University of Business, Agriculture and Technology,
Dhaka 1230, Bangladesh.
r.ahsan06me@gmail.com
2Department of Advanced Materials and Industrial Management Engineering, Dong-Eui University, Busan
47340, Republic of Korea.
muralicheepu@gmail.com
3Department of Advanced Materials Engineering, Dong-Eui University, B

Abstract

용접시 표면 장력 구동 흐름 또는 마랑고니 흐름은 용접 비드 모양을 제어하는데 중요한 역할을 하므로 용접 접합 품질에 영향을 미칩니다. 용해된 금속의 표면 장력은 보통 음의 온도 계수를 가지므로 용접 풀이 중심에서 토우 방향으로 흐르게 됩니다.

표면 장력의 이 온도 계수는 황(S), 산소(O), 셀레늄(Se) 및 텔루륨(Te)과 같은 표면 활성 요소가 있는 경우 양의 계수로 변경할 수 있습니다. 소모품에 존재하는 탈산화 원소의 양이 용접 금속에 존재하는 산소량을 결정합니다. 탈산화제 양이 적으면 용접 금속에 산소 농도가 높아집니다.

적절한 양의 산소가 있으면 용융지에 표면 장력 구배의 양의 온도 계수가 발생할 수 있습니다. 이 경우 용접 풀은 토우에서 중앙 방향으로 흐릅니다. 그 결과, 아크와 용융지에 있는 화농성 반응의 경우, 합금 요소의 다양한 산화물이 슬래그(slag)라고 합니다. 슬래그는 용융지 표면에 떠서 용융지 흐름 패턴에 따라 누적됩니다.

그 결과, 슬래그는 용융지 흐름 패턴에 따라 용접 비드 중심 또는 토우 중심을 따라 형성됩니다. 슬래그는 용접 비드의 외관과 도장 접착력을 저하시키므로 제거해야 합니다. 쉽게 분리할 수 있기 때문에 용접 비드 중심 부근에서 슬래그가 형성되는 것이 좋습니다.

용접 풀의 현장 고속 비디오 촬영, 용접 금속 화학 성분 분석, 소모품 합금 요소가 용접 풀 흐름 패턴 및 슬래그 형성 위치에 미치는 영향이 공개되어 CMT-GMAW의 생산성 향상을 위해 용접 소모품 선택을 용이하게 할 수 있습니다.

The surface tension driven flow or Marangoni flow in welding plays an important role in governing weld bead shape hence affecting the weld joint quality. The surface tension of molten metal usually has a negative temperature coefficient causing the weld pool to flow from the center towards the toe.

This temperature coefficient of the surface tension can be altered to be a positive one in the presence of surface-active elements like sulfur (S), oxygen (O), selenium (Se) and tellurium (Te). The amount of deoxidizing elements present in the consumables governs the amount of oxygen present in the weld metal. The presence of a lower amount of deoxidizers results in higher concentration of oxygen in the weld metal.

The presence of adequate amount of oxygen can result in a positive temperature coefficient of surface tension gradient in the weld pool. In such situation, the weld pool flows from the toe towards the direction of the center. As a result, of pyrometallurgical reactions in the arc and the weld pool various oxides of the alloying elements are former which are referred as slag.

The slags float on the weld pool surface and accumulate following the weld pool flow pattern. As a result, slags form either along the center of the weld bead or the toe depending on the weld pool flow pattern. The slags need to be removed as they degrade the weld bead appearance and paint adhesiveness.

Due to easy detachability, slag formation near the center of the weld bead is desired. From in-situ high-speed videography of weld pool, weld metal chemical composition analysis, the effect of consumables alloying elements on weld pool flow pattern and slag formation location are disclosed, which can facilitate the selection of the welding consumables for better productivity in CMT-GMAW.

Fig. 2: High-speed movie frames and schematic showing the weld pool flow pattern and the slag formation location for wire 1 and wire 2.

Fig. 3: Quantitative analysis data on slag formation for different wire.

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원문 바로 가기

Figure 2. Experimental setups for the (a) Al/Cu overlap joint and (b) laser welding process.

Investigation on Laser Welding of Al Ribbon to Cu Sheet: Weldability, Microstructure, and Mechanical and Electrical Properties

알루미늄 리본과 구리 시트의 레이저 용접에 대한 조사 : 용접성, 미세 구조, 기계적 및 전기적 특성

Won‐Sang Shin 1,†, Dae‐Won Cho 2,†, Donghyuck Jung 1, Heeshin Kang 3, Jeng O Kim 3, Yoon‐Jun Kim 1,*
and Changkyoo Park 3,*

Al 리본과 Cu 시트의 펄스 레이저 용접은 전력 전자 모듈의 전기적 상호 연결에 대해 조사되었습니다. 결함 없는 Al / Cu 조인트를 얻기 위해 레이저 출력, 스캔 속도 및 열 입력이 서로 다른 다양한 실험 조건이 사용되었습니다. Al / Cu 레이저 용접 중에 금속 간 화합물이 용접 영역에 형성되었습니다. 전자 탐침 마이크로 분석기와 투과 전자 현미경으로 Al4Cu9, Al2Cu, AlCu 등으로 밝혀진 금속 간 화합물의 상을 확인했습니다. 전산 유체 역학 시뮬레이션은 Marangoni 효과가 용융 풀의 순환을 유도하여 혼합물을 생성하는 것으로 나타났습니다. Al과 Cu의 결합과 Al / Cu 조인트에서 소용돌이 모양의 구조 형성. Al / Cu 접합부의 인장 전단강도와 전기 저항을 측정하였으며 용접 면적과 강한 상관 관계를 보였다. Al / Cu 접합부의 용접 면적이 증가함에 따라 기계적 강도의 감소와 전기 저항의 증가가 측정 되었습니다. 또한 무결점 Al / Cu 접합을 위한 공정 창을 개발하고 Al / Cu 레이저 브레이즈 용접을 위한 실험 조건을 조사하여 Al / Cu 접합에서 금속 간 화합물 형성을 최소화했습니다.

Introduction

전기 상호 연결은 전력 전자 모듈을 패키징하는 데 중요합니다. 우수한 기계적 및 전기적 특성을 가진 견고한 전기적 상호 연결은 전력 전자 모듈의 전기적 고장을 방지하는 데 필수적입니다. 저항 스폿 용접, 브레이징, 납땜 및 초음파 용접 (USW)이 전기 상호 연결에 사용되었습니다.

납땜과 납땜 모두 저온 공정으로 인해 접합부에서 한계 변형과 잔류 응력이 발생합니다 [1]. 필러 합금은 두 공정 모두 견고한 전기 접촉을 달성하는 데 필수적입니다. 따라서 조인트는 서로 접촉하는 서로 다른 금속으로 구성됩니다.

결과적으로 조인트는 부식 환경에서 갈바닉 부식에 취약 할 수 있습니다 [2,3]. 더욱이, 비금속과 충전재 사이의 친화도를 고려해야 하기 때문에 제한된 충전재 만 특정 조인트에 사용할 수 있습니다 [1]. USW는 용접 온도가 낮고 용접 시간이 짧기 때문에 접합부의 변형이 비교적 적습니다.

따라서 이는 특히 연질 재료 (예 : Al, Cu, Ag, Au 및 Ni)의 경우 기존 접합 방법을 대체하고 있습니다 [4–6]. 그러나 Cu를위한 USW 공정의 경우, 표면 산화물이 강해 용접성이 저하되는 것을 방지하기 위해 Cu 표면에 Sn 또는 Ni 코팅이 필요하며, 이는 공정 속도를 늦추고 산업적 응용을위한 경제적 측면을 악화시킨다 [7 , 8].

레이저 용접은 쉬운 제어, 고정밀 및 원격 처리의 특성으로 인해 전력 전자 모듈의 전기 연결에 대한 유망한 후보입니다. 열의 영향을 받는 작은 영역과 변형은 전기 접점의 손상을 최소화 할 것으로 예상됩니다 [9-11]. 또한 레이저 용접을 위해 추가 표면 준비가 필요하지 않습니다.

이종 재료의 용접은 산업 응용 분야에서 중요했습니다. 더욱이 그림 1 [12,13]에서 볼 수 있듯이 전기 연결을위한 와이어 또는 리본 본딩에 여러 다른 조인트가 필요하기 때문에 전력 전자 모듈에서 필수적인 기술이되고 있습니다.

전기 접점의 다양한 조합 중에서 Al과 Cu는 높은 전기 전도성으로 인해 전기 연결에 중요한 재료로 종종 간주됩니다 [14]. 그러나 Al과 Cu의 서로 다른 용접은 금속 간 화합물 (IMC)의 형성을 촉진하고 동시에 Al / Cu 조인트의 기계적 및 전기적 특성에 영향을 줍니다. 일반적으로 Al / Cu 조인트 내부에 IMC가 있으면 연성 및 전기 저항에 해를 끼치므로 균열이 쉽게 발생하고 용접을 통한 전기 전도도를 방해합니다 [15,16].

따라서 견고한 Al / Cu 조인트를 얻으려면 IMC의 형성을 피해야합니다. 여러 연구에서 Al 및 Cu 시트의 레이저 빔 용접을 조사했습니다. 연속파 (CW) 레이저가 Al / Cu 조인트에 사용되었습니다 [17-23]. 큰 열 입력과 상당한 IMC 형성으로 인해 용접 영역에서 많은 균열이 관찰되었습니다 [18,19].

CW 레이저 빔의 공간 진동은 Al / Cu 조인트의 용접 품질을 향상시키는 것으로 나타났습니다. 직선 CW 레이저 빔 [18-20]과 비교하여 용접 영역에서 IMC 크기가 더 작은 기공과 균열이 더 적습니다.

Al과 Cu 시트의 겹침 접합에는 CW 단일 모드 파이버 레이저를 사용했으며, IMC 형성을 억제하여 높은 용접 속도 (즉, 50m / min)에서 견고한 Al / Cu 접합을 얻었습니다 [22]. Mai et al. [23]은 다른 Al / Cu 용접을 달성하기 위해 펄스 레이저를 사용했습니다.

그들은 Al / Cu 용접성이 레이저 공정 매개 변수에 크게 의존한다는 것을 밝혔으며 100mm / min 미만의 스캔 속도에서 균열없는 Al / Cu 접합을 달성하는 데 성공했습니다.

본문 내용 생략 : 문서 하단부의 원문보기를 참고하시기 바랍니다.

Figure 1. Schematic diagram of the insulated gate bipolar transistors (IGBT) power module. Red‐dotted box indicated the electrical connections

Figure 3. Schematic diagram of the numerical simulation domain and boundary conditions.

Figure 4. Experimental setup for the four‐point electrical resistance measurement.

Figure 5. Cross‐sectional OM image of the Al/Cu joints in parallel to the laser welding direction. The laser power and scan speed were set at 2300 W and 20 mm/s, respectively.

Figure 6 shows the cross‐sectional SEM images of the Al/Cu joints, and corresponding EPMA element mapping of Al and Cu for the (a) 23/20, (b) 25/28.6, (c) 25/15.4, and (d) 27/20. — Figure 6 shows the cross‐sectional SEM images of the Al/Cu joints, and corresponding EPMA element mapping of Al and Cu for the (a) 23/20,

Figure 6. Cross‐sectional SEM image and elemental distribution mapping of Al and Cu elements for the (a) 23/20, (b) 25/28.6, (c) 25/15.4, and (d) 27/20. — Figure 6. Cross‐sectional SEM image and elemental distribution mapping of Al and Cu elements for the (d) 27/20.

Figure 7. EPMA line scan analysis and identification of the IMCs for the (a) 23/20 and (b) 25/15.4.

Figure 8. TEM analysis for the 25/28.6. (a) Indicating the location of TEM analysis in SEM image of the welding zone. (b) TEM bright‐field image and SAED pattern insets, examined at the location (1) in figure (a), confirmed Al‐rich phase (white globular shape) and Al2Cu eutectic phase (gray region), and (c) TEM bright‐field image and SAED pattern inset of Al4Cu9, examined at the location (2) in figure (a).

Figure 9. Temperature profiles and molten pool flow on transverse cross‐section (y–z plane at x = 1.23 cm): (a) Negative surface tension gradient for the 23/20 (Case 1), (b) negative surface tension gradient for the 25/15.4 (Case 2), (c) positive surface tension gradient for the 25/15.4 (Case 3), and (d) without surface tension for the 25/15.4 (Case 4).

Figure 12. Results of the tensile shear tests for the (a) 23/20: fracture at the Al ribbon and (b) 25/15.4: fracture at the weld

Figure 13. Stress–strain curves obtained by the tensile shear tests.

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논문 원본 바로가기

$Figure 2. Ink fraction contours for mesh 1 through 4 (left to right) at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs.$

Coupled CFD-Response Surface Method (RSM) Methodology for Optimizing Jettability Operating Conditions

분사성 작동 조건을 최적화하기 위한 결합된 CFD-Response Surface Method(RSM)

Nuno Couto 1, Valter Silva 1,2,* , João Cardoso 2, Leo M. González-Gutiérrez 3 and Antonio Souto-Iglesias 41
INEGI-FEUP, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal;
nunodiniscouto@hotmail.com
2 VALORIZA, Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal; jps.cardoso@ipportalegre.pt
3 CEHINAV, DMFPA, ETSIN, Universidad Politécnica de Madrid, 28040 Madrid, Spain; leo.gonzalez@upm.es
4 CEHINAV, DACSON, ETSIN, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
antonio.souto@upm.es

Correspondence: valter.silva@ipportalegre.pt; Tel.: +351-245-301-592

소개

물방울 생성에 대한 이해는 여러 산업 응용 분야에서 매우 중요합니다 [ 1 ]. 잉크젯 프린팅 프로세스는 일반적으로 10 ~ 100 μm [ 1 ] 범위의 독특하고 작은 액적 크기를 특징으로 하며 연속적 또는 충동적 흐름을 사용하여 얻을 수 있습니다 (마지막 방식은 주문형 드롭 (DoD)이라고도 함). 잉크젯).

여러 장점 덕분에 DoD 방법은 산업 환경에서 상당한 수용을 얻고 있습니다 [ 2 ].DoD는 복잡한 프로세스이며 유체 속성, 노즐 형상 및 구동 파형 [ 1 , 3 ]의 세 가지 주요 범주로 분류되는 여러 매개 변수에 따라 달라집니다 .그러나 길이와 시간 척도가 모두 마이크로 오더 [ 4 ] 이기 때문에 실험을하기가 어렵습니다 .

결과적으로 실험 설정은 항상 비용이 많이 들고 복잡하며 CFD (전산 유체 역학)와 같은 고급 수치 접근 방식이 엄격한 요구 사항입니다 [ 5 , 6 ]. VOF (volume-of-fluid) 접근 방식은 액체 분해 및 액적 생성에 대한 다상 공정을 시뮬레이션하기위한 적절한 대안으로 밝혀졌으며 과거 연구에서 그대로 사용되었습니다 [ 7 , 8], 인쇄 프로세스의 맥락에서 전자는 여전히 현재 연구의 주제입니다.

또한 VOF 체계를 사용하면 단일 운동량 방정식 세트를 해결하고 도메인 전체에 걸쳐 각 유체의 체적 분율을 추적하여 명확하게 정의된 인터페이스로 둘 이상의 혼합 불가능한 유체를 효과적으로 시뮬레이션 할 수 있습니다. Feng [ 9 ]는 VOF 접근 방식을 사용하여 일시적인 유체 인터페이스 변형 및 중단을 효과적으로 추적하는 패키지 FLOW-3D를 사용하여 낙하 배출 중 복잡한 유체 역학 프로세스를 시뮬레이션하는 선구자 작업 중 하나를 수행했습니다.

주요 목표는 볼륨 및 속도와 같은 민감한 변수를 더 잘 이해하면서 장치 개발에서 일반적인 설계 규칙을 구현하는 것이 었습니다. 이러한 종류의 공정과 관련된 주요 질문 중 하나는 안정적인 액적 형성을 위한 작동 범위의 정의입니다.

Fromm [ 10 ]은 Reynolds 수와 Weber 수의 제곱근 비율이 2보다 작으면 안정적인 방울을 생성 할 수 없다는 것을 확인했습니다. 이 무차원 값은 나중에 Z 번호로 알려졌으며 분사 가능성 범위 [ 11 ]를 정의합니다 . 문헌에서 분사 가능성을 위한 Z 간격은 1 ~ 10 [ 12 ], 4 ~ 14 [ 13 ] 또는 0.67 ~ 50 [ 14]을 찾을 수 있습니다.

이것은 Z 값 만으로는 분사 가능성 조건을 나타낼 수 없음을 분명히 의미합니다. 실제로, 다른 속성을 가진 유체는 다른 인쇄 품질을 나타내면서 동일한 Z 값을 나타낼 수 있습니다. 액적 생성 공정과 해당 분사 성은 주로 전체 공정 품질에 큰 영향을 미치는 매개 변수 세트에 의해 결정됩니다.

토대 메커니즘을 더 잘 이해하려면 확장 된 작동 조건 및 매개 변수 세트를 고려하여 여러 실험 또는 수치 실행을 수행해야 합니다. DoE (design-of-experiment) 접근 방식과 같은 체계적인 접근 방식이 없으면 이것은 달성하기 매우 어려운 작업이 될 수 있습니다. 최적화 문제를 해결하기 위해 반응 표면 방법을 사용하여 처음으로 체계화된 접근 방식이 개발된 Box and Wilson [ 15 ] 의 선구자 기사 이후 ,이 입증된 방법론은 많은 화학 및 산업 공정[ 16 ] 및 기타 관련 학계에 성공적으로 적용되었습니다.

예를 들어 Silva와 Rouboa [ 17 ]는 직접 메탄올 연료 전지의 출력 밀도에 영향을 미치는 관련 매개 변수를 식별하기 위해 반응 표면 방법론 (RSM)을 사용했습니다. 많은 실제 산업 응용 분야에서 실험 연구는 작동 매개 변수를 조절하기 어렵 기 때문에 제한적이지만 주로 설정을 개발하거나 실험을 실행하는 데 드는 비용이 높기 때문입니다.

따라서 솔루션은 주요 시스템 응답을 시뮬레이션하고 예측할 수 있는 효과적인 수학적 모델의 개발에 의존합니다. DoE와 같은 최적화 방법론을 수치 모델과 결합하면 비용이 많이 들고 시간이 많이 걸리는 실험을 피하고 다양한 입력 조합을 사용하여 최적의 조건을 얻을 수 있습니다 [ 16 ].

실바와 루 보아 [ 18] CFD 프레임 워크 하에서 개발 된 2D Eulerian-Eulerian 바이오 매스 가스화 모델에서 얻은 결과를 RSM과 결합하여 다양한 응용 분야에서 합성 가스를 생성하기 위한 최적의 작동 조건을 찾습니다.

저자는 입력 요인으로 인한 최상의 응답과 최소한의 변동을 모두 보장하는 작동 조건을 찾을 수 있었습니다. Frawley et al. [ 19 ] CFD 및 DoE 기술 (특히 RSM)을 결합하여 파이프의 팔꿈치에서 고체 입자 침식에 대한 다양한 주요 요인의 영향을 조사하여 침식 예측 모델을 개발할 수 있습니다.우리가 아는 한, DoD 잉크젯 프로세스의 개선 및 더 나은 이해에 적용되는 DoE 접근법 (실험적으로 또는 모든 종류의 수치 모델과 결합)을 구현하는 연구는 없습니다. 선도 기업이 이러한 접근 방식을 적용 할 가능성이 있지만 관련 결과는 민감할 수 있으므로 더 넓은 커뮤니티에서 사용할 수 없습니다. 이 사실은 DoD 잉크젯 공정에서 액적 생성에 대한 여러 매개 변수의 영향을 평가하기 위한 이러한 종류의 연구로서 현재 논문의 영향을 증가 시킬 수 있습니다.

CFD 프레임 워크 내에서 VOF 접근 방식을 사용하여 여러 컴퓨터 실험의 설계를 개발하고 RSM을 분석 도구로 사용했습니다. 충분한 수치 정확도와 수용 가능한 시간 계산 시뮬레이션의 균형을 맞추기 위해 메쉬 수렴 연구가 수행되었습니다. 설계 목적을 위해 점도, 표면 장력, 입구 속도 및 노즐 직경이 입력 요인으로 선택되었습니다. 응답은 break-up 시간과 break-up 길이였습니다.

Figure 1. Schematic of the computational domain

Figure 2. Ink fraction contours for mesh 1 through 4 (left to right) at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs.

Figure 3. Comparison between surface tensions at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs

Figure 4. Comparison between viscosity values at the following four time steps: (a) 6 μs, (b) 12 μs, (c) 18 μs, and (d) 24 μs.

Figure 5. Comparison between different nozzle diameters at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs

Figure 6. Comparison between different inlet velocities at the following four time steps: (a) 6 µs, (b) 12 µs, (c) 18 µs, and (d) 24 µs

Figure 8. Contour response plots for break-up time as a function of (a) surface tension and viscosity, (b) nozzle diameter and viscosity, (c) inlet velocity and viscosity, (d) nozzle diameter and surface tension, (e) inlet velocity and surface tension, and (f) inlet velocity and nozzle diameter.

Figure 12. Break-up length as a function of the We–Ca space (obtained from the 25 runs).

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Figure 1.1: A water droplet with a radius of 1 mm resting on a glass substrate. The surface of the droplet takes on a spherical cap shape. The contact angle θ is defined by the balance of the interfacial forces.

Effect of substrate cooling and droplet shape and composition on the droplet evaporation and the deposition of particles

기판 냉각 및 액적 모양 및 조성이 액적 증발 및 입자 증착에 미치는 영향

by Vahid Bazargan
M.A.Sc., Mechanical Engineering, The University of British Columbia, 2008
B.Sc., Mechanical Engineering, Sharif University of Technology, 2006
B.Sc., Chemical & Petroleum Engineering, Sharif University of Technology, 2006

고착 방울은 평평한 기판에 놓인 액체 방울입니다. 작은 고정 액적이 증발하는 동안 액적의 접촉선은 고정된 접촉 영역이 있는 고정된 단계와 고정된 접촉각이 있는 고정 해제된 단계의 두 가지 단계를 거칩니다. 고정된 접촉 라인이 있는 증발은 액적 내부에서 접촉 라인을 향한 흐름을 생성합니다.

이 흐름은 입자를 운반하고 접촉 선 근처에 침전시킵니다. 이로 인해 일반적으로 관찰되는 “커피 링”현상이 발생합니다. 이 논문은 증발 과정과 고착성 액적의 증발 유도 흐름에 대한 연구를 제공하고 콜로이드 현탁액에서 입자의 침착에 대한 통찰력을 제공합니다. 여기서 우리는 먼저 작은 고착 방울의 증발을 연구하고 증발 과정에서 기판의 열전도도의 중요성에 대해 논의합니다.

현재 증발 모델이 500µm 미만의 액적 크기에 대해 심각한 오류를 생성하는 방법을 보여줍니다. 우리의 모델에는 열 효과가 포함되어 있으며, 특히 증발 잠열의 균형을 맞추기 위해 액적에 열을 제공하는 기판의 열전도도를 포함합니다. 실험 결과를 바탕으로 접촉각의 진화와 관련된 접촉 선의 가상 움직임을 정의하여 고정 및 고정 해제 단계의 전체 증발 시간을 고려합니다.

우리의 모델은 2 % 미만의 오차로 500 µm보다 작은 물방울에 대한 실험 결과와 일치합니다. 또한 유한한 크기의 라인 액적의 증발을 연구하고 증발 중 접촉 라인의 복잡한 동작에 대해 논의합니다. 에너지 공식을 적용하고 접촉 선이 구형 방울의 후퇴 접촉각보다 높은 접촉각을 가진 선 방울의 두 끝에서 후퇴하기 시작 함을 보여줍니다. 그리고 라인 방울 내부의 증발 유도 흐름을 보여줍니다.

마지막으로, 계면 활성제 존재 하에서 접촉 라인의 거동을 논의하고 입자 증착에 대한 Marangoni 흐름 효과에 대해 논의합니다. 열 Marangoni 효과는 접촉 선 근처에 증착 된 입자의 양에 영향을 미치며, 기판 온도가 낮을수록 접촉 선 근처에 증착되는 입자의 양이 많다는 것을 알 수 있습니다.

Figure 2.1: Evaporation modes of sessile droplets on a substrate: (a) evaporation at constant contact angle (de-pinned stage) and (b) evaporation at constant contact area (pinned stage)

Figure 2.2: A sessil droplet with its image can be profiled as the equiconvex lens formed by two intersecting spheres with radius of a.

Figure 2.3: The droplet life time for both evaporation modes derived from Equation 2.2.

Figure 2.4: A probability of escape for vapor molecules at two different sites of the surface of the droplet for diffusion controlled evaporation. The random walk path initiated from a vapor molecule is more likely to result in a return to the surface if the starting point is further away from the edge of the droplet.

Figure 2.5: Schematic of the sessile droplet on a substrate. The evaporation rate at the surface of the droplet is enhanced toward the edge of the droplet.

Figure 2.6: The domain mesh (a) and the solution of the Laplace equation for diffusion of the water vapor molecule with the concentration of Cv = 1.9×10−8 g/mm3 at the surface of the droplet into the ambient air with the relative humidity of 55%, i.e. φ = 0.55 (b).

Figure 3.1: The portable micro printing setup. A motorized linear stage from Zaber Technologies Inc. was used to control the place and speed of the micro nozzle.

Figure 4.6: Temperature contours inside the substrate adjacent to the droplet

Figure 4.7: The effect of substrate cooling on the evaporation rate, the basic model shows the same value for all substrates.

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Result of simulation by changing surface tension

잉크젯 프린팅에서 해상력에 관한 컴퓨터 시뮬레이션 연구

A Study on the Simulation of the Resolution for Ink-Jet Printing

Lee, Ji-Eun (Dept. of Graphic Arts Engineering, Graduate School, Pukyong National University) ;
Youn, Jong-Tae (Dept. of Graphic Arts Information, College of Engineering, Pukyong National University) ;
Koo, Chul-Whoi (Dept. of Graphic Arts Information, College of Engineering, Pukyong National University)

이지은 (부경대학교 대학원 인쇄공학과) ;
윤종태 (부경대학교 공과대학 인쇄정보공학과) ;
구철회 (부경대학교 공과대학 인쇄정보공학과)

초록

Ink-jet is part of the non impact printing that shooting the ink drop from the nozzle to paper. It is very silence and express good color. There are two types of printing that continuous and drop on demand. But drop on demand process is becoming the mainstream. these days, LCD, PDP is passed more than semiconductor industry. And we expect organic EL, FED as a next display. But product equipment, main component and technology have a gap between an advanced country and us nevertheless physical development. Expecially, previous process part is depended on imports. Ink-jet printing technology that there isn’t complicated photo lithography process is attracted, so ink-jet printing resolution is more embossed. But there were not many of ink-jet resolution thesis but ink-jet head or nozzle. Because, to out of the ink from the nozzle is unseeable and hard to experiment. Therefore this thesis was experimented and simulated how can ink-jet printer improved resolution by flow-3d simulation package program.

잉크젯은 노즐에서 종이로 잉크 방울을 분사하는 비 충격 인쇄의 일부입니다. 매우 조용하고 좋은 색상을 표현합니다. 연속 및 요청시 드롭되는 두 가지 유형의 인쇄가 있습니다. 그러나 주문형 드롭 프로세스가 주류가되고 있습니다. 요즘 LCD, PDP는 반도체 산업을 넘어서고 있습니다. 그리고 우리는 유기 EL, FED를 다음 디스플레이로 기대합니다. 그러나 제품 장비, 주요 부품 및 기술은 선진국과 우리의 물리적 발달 사이에 격차가 있습니다. 특히 이전 공정 부분은 수입품에 의존합니다. 복잡한 포토 리소그래피 공정이없는 잉크젯 프린팅 기술이 매료되어 잉크젯 프린팅 해상도가 더욱 강조됩니다. 하지만 잉크젯 해상도 논문은 많지 않고 잉크젯 헤드 나 노즐이 많았습니다. 왜냐하면 노즐에서 잉크가 빠져 나가는 것은 보이지 않고 실험하기 어렵 기 때문입니다. 따라서이 논문은 flow-3d 시뮬레이션 패키지 프로그램을 통해 잉크젯 프린터가 해상도를 향상시킬 수있는 방법을 실험하고 시뮬레이션했습니다.

국내 및 해외에 다양한 인쇄 기술이 보급되어 있는 상황에서 잉크젯 기술은 1990년대 후반부터 궤도에 올랐다. 잉크젯은 비접촉성 인쇄 기술의 하나로 인쇄 표면에 잉크 방울 들을 투사해 전자적으로 조정하기 때문에 여러 가지 장점들이 있다. 원하는 양을 원하는 때 제작 가능하고 2,400dpi이상의 높은 해상도를 가지며 잉크 방울의 크기를 조절하여 보다 정확한 이미지인 그레이 스케일 이미지를 얻을 수 있다. 따라서 사진과 같은 이미 지를 만들 수 있다. 또한 기존의 붓을 이용한 디자인에 비해 높은 해상도의 이미지를 손 쉽게 만들 수 있으므로 그래픽 디자인에 대한 적용 범위를 확장할 수 있다. 그리고 카트 리지에 저장되어 있는 잉크를 이미지에 필요한 양만큼 소비하기 때문에 생산비 절감에 유리하다. 이는 코팅 기술이 가지고 있는 원료의 소모를 획기적으로 개선할 수 있다.또 한 코팅 방법과는 달리 기판에 영향을 주지 않는다. 거칠거나 민감한 모든 종류의 표면 위에 인쇄가 가능하며, 1분당 100,000라인의 인쇄 속도로 고속 처리에 적합하다. 현재 잉 크젯 프린터의 성능을 평가하는 방법 중에 가장 기본적인 것은 해상도이다. 그렇기 때문 에 인쇄물의 해상도에서는 dpi가 무척 중요하다. dpi는 dot per inch의 약자로 1인치당 찍은 점의 수이다. dpi는 인쇄물의 해상력을 결정하는 단위이다. 예를 들어 300dpi는 1인 치에 300개의 점을 찍는 밀도로 잉크 점을 찍어 인쇄를 한다는 뜻이다. 당연히 dpi는 숫 자가 클수록 인쇄물이 더 정교해진다. 그러나 제조업체에 따라 출력 dpi 수가 다르며 요 구되는 최적의 해상도도 프린터 엔진의 특성에 따라 다르다. 일반적인 인쇄물은 200dpi 면 좋은 품질이며, 300dpi를 넘으면 매우 우수한 품질이 된다. 우리가 일상생활에서 보 는 대부분의 인쇄물은 100~300dpi 정도롤 사용한다. 잉크젯 프린터에 1,440dpi라고 쓰여 있는 것은 dot의 실질적인 것을 말하는 것이 아니라, 이상적인 종이에 잉크 방울을 려 구현할 수 있는 이론상의 수치이다. 종이에 작은 잉크 입자돌을 뿌려 번지게 하는 방법 으로 인해, 표시된 해상력만큼 재현하지 못하는 경우가 많다. 따라서 실제로는 600dpi 잉크젯 프린터라고 해도 인쇄소에서 300dpi로 출력한 것보다 품질이 떨어지기도 한다. 그러므로 좋은 품질을 얻기 위해서는 목표로 한 해상력 보다 높게 인쇄해야 하는데 그 러기 위해서는 잉크젯의 해상력에 관한 연구가 필수적이다. 잉크에서는 주로 헤드와 노즐에 관한 연구들이 많이 있지만,~9 본 논문에서는 잉크젯의 해상력에 관한 연구를 하고자 한다. 본 연구의 목적은 FLOW-3D 시뮬레이션 프로그램을 이용하여 액적의 비산 모양을 시뮬레이션 함으로서 해상력에 대한 예측을 하기 위한 것이다. 잉크 방울의 크기가 해상 력에 미친다는 것을 알고, 잉크의 물성을 변화시켜가며 액적을 줄이기 위한 시뮬레이션 을 하였다.

ZSimulation of the bubble jet printing by FLOW-3D

논문 원문 바로가기

자유 표면 모델링 방법

본 자료는 국내 사용자들의 편의를 위해 원문 번역을 해서 제공하기 때문에 일부 오역이 있을 수 있어서 원문과 함께 수록합니다. 자료를 이용하실 때 참고하시기 바랍니다.

Free Surface Modeling Methods

An interface between a gas and liquid is often referred to as a free surface. The reason for the “free” designation arises from the large difference in the densities of the gas and liquid (e.g., the ratio of density for water to air is 1000). A low gas density means that its inertia can generally be ignored compared to that of the liquid. In this sense the liquid moves independently, or freely, with respect to the gas. The only influence of the gas is the pressure it exerts on the liquid surface. In other words, the gas-liquid surface is not constrained, but free.

자유 표면 모델링 방법

기체와 액체 사이의 계면은 종종 자유 표면이라고합니다. ‘자유’라는 호칭이 된 것은 기체와 액체의 밀도가 크게 다르기 때문입니다 (예를 들어, 물 공기에 대한 밀도 비는 1000입니다). 기체의 밀도가 낮다는 것은 액체의 관성에 비해 기체의 관성은 일반적으로 무시할 수 있다는 것을 의미합니다. 이러한 의미에서, 액체는 기체에 대해 독립적으로, 즉 자유롭게 움직입니다. 기체의 유일한 효과는 액체의 표면에 대한 압력입니다. 즉, 기체와 액체의 표면은 제약되어있는 것이 아니라 자유롭다는 것입니다.

In heat-transfer texts the term ‘Stephen Problem’ is often used to describe free boundary problems. In this case, however, the boundaries are phase boundaries, e.g., the boundary between ice and water that changes in response to the heat supplied from convective fluid currents.

열전달에 관한 문서는 자유 경계 문제를 묘사할 때 “Stephen Problem’”라는 용어가 자주 사용됩니다. 그러나 여기에서 경계는 상(phase) 경계, 즉 대류적인 유체의 흐름에 의해 공급된 열에 반응하여 변화하는 얼음과 물 사이의 경계 등을 말합니다.

Whatever the name, it should be obvious that the presence of a free or moving boundary introduces serious complications for any type of analysis. For all but the simplest of problems, it is necessary to resort to numerical solutions. Even then, free surfaces require the introduction of special methods to define their location, their movement, and their influence on a flow.

이름이 무엇이든, 자유 또는 이동 경계가 존재한다는 것은 어떤 유형의 분석에도 복잡한 문제를 야기한다는 것은 분명합니다. 가장 간단한 문제를 제외한 모든 문제에 대해서는 수치 해석에 의존할 필요가 있습니다. 그 경우에도 자유 표면은 위치, 이동 및 흐름에 미치는 영향을 정의하기 위한 특별한 방법이 필요합니다.

In the following discussion we will briefly review the types of numerical approaches that have been used to model free surfaces, indicating the advantages and disadvantages of each method. Regardless of the method employed, there are three essential features needed to properly model free surfaces:

A scheme is needed to describe the shape and location of a surface,
An algorithm is required to evolve the shape and location with time, and
Free-surface boundary conditions must be applied at the surface.

다음 설명에서는 자유 표면 모델링에 사용되어 온 다양한 유형의 수치적 접근에 대해 간략하게 검토하고 각 방법의 장단점을 설명합니다. 어떤 방법을 사용하는지에 관계없이 자유롭게 표면을 적절히 모델화하는 다음의 3 가지 기능이 필요합니다.

표면의 형상과 위치를 설명하는 방식
시간에 따라 모양과 위치를 업데이트 하는 알고리즘
표면에 적용할 자유 표면 경계 조건

Lagrangian Grid Methods

Conceptually, the simplest means of defining and tracking a free surface is to construct a Lagrangian grid that is imbedded in and moves with the fluid. Many finite-element methods use this approach. Because the grid and fluid move together, the grid automatically tracks free surfaces.

라그랑주 격자 법

개념적으로 자유 표면을 정의하고 추적하는 가장 간단한 방법은 유체와 함께 이동하는 라그랑주 격자를 구성하는 것입니다. 많은 유한 요소 방법이 이 접근 방식을 사용합니다. 격자와 유체가 함께 움직이기 때문에 격자는 자동으로 자유 표면을 추적합니다.

At a surface it is necessary to modify the approximating equations to include the proper boundary conditions and to account for the fact that fluid exists only on one side of the boundary. If this is not done, asymmetries develop that eventually destroy the accuracy of a simulation.

표면에서 적절한 경계 조건을 포함하고 유체가 경계의 한면에만 존재한다는 사실을 설명하기 위해 근사 방정식을 수정해야합니다. 이것이 수행되지 않으면 결국 시뮬레이션의 정확도를 훼손하는 비대칭이 발생합니다.

The principal limitation of Lagrangian methods is that they cannot track surfaces that break apart or intersect. Even large amplitude surface motions can be difficult to track without introducing regridding techniques such as the Arbitrary-Lagrangian-Eulerian (ALE) method. References 1970 and 1974 may be consulted for early examples of these approaches.

라그랑지안 방법의 주요 제한은 분리되거나 교차하는 표면을 추적 할 수 없다는 것입니다. ALE (Arbitrary-Lagrangian-Eulerian) 방법과 같은 격자 재생성 기법을 도입하지 않으면 진폭이 큰 표면 움직임도 추적하기 어려울 수 있습니다. 이러한 접근법의 초기 예를 보려면 참고 문헌 1970 및 1974를 참조하십시오.

The remaining free-surface methods discussed here use a fixed, Eulerian grid as the basis for computations so that more complicated surface motions may be treated.

여기에서 논의된 나머지 자유 표면 방법은 보다 복잡한 표면 움직임을 처리할 수 있도록 고정된 오일러 그리드를 계산의 기준으로 사용합니다.

Surface Height Method

Low amplitude sloshing, shallow water waves, and other free-surface motions in which the surface does not deviate too far from horizontal, can be described by the height, H, of the surface relative to some reference elevation. Time evolution of the height is governed by the kinematic equation, where (u,v,w) are fluid velocities in the (x,y,z) directions. This equation is a mathematical expression of the fact that the surface must move with the fluid:

표면 높이 법

낮은 진폭의 슬로 싱, 얕은 물결 및 표면이 수평에서 너무 멀리 벗어나지 않는 기타 자유 표면 운동은 일부 기준 고도에 대한 표면의 높이 H로 설명 할 수 있습니다. 높이의 시간 진화는 운동학 방정식에 의해 제어되며, 여기서 (u, v, w)는 (x, y, z) 방향의 유체 속도입니다. 이 방정식은 표면이 유체와 함께 움직여야한다는 사실을 수학적으로 표현한 것입니다.

Finite-difference approximations to this equation are easy to implement. Further, only the height values at a set of horizontal locations must be recorded so the memory requirements for a three-dimensional numerical solution are extremely small. Finally, the application of free-surface boundary conditions is also simplified by the condition on the surface that it remains nearly horizontal. Examples of this technique can be found in References 1971 and 1975.

이 방정식의 유한 차분 근사를 쉽게 실행할 수 있습니다. 또한 3 차원 수치 해법의 메모리 요구 사항이 극도로 작아지도록 같은 높이의 위치 값만을 기록해야합니다. 마지막으로 자유 표면 경계 조건의 적용도 거의 수평을 유지하는 표면의 조건에 의해 간소화됩니다. 이 방법의 예는 참고 문헌의 1971 및 1975을 참조하십시오.

Marker-and-Cell (MAC) Method

The earliest numerical method devised for time-dependent, free-surface, flow problems was the Marker-and-Cell (MAC) method (see Ref. 1965). This scheme is based on a fixed, Eulerian grid of control volumes. The location of fluid within the grid is determined by a set of marker particles that move with the fluid, but otherwise have no volume, mass or other properties.

MAC 방법

시간 의존성을 가지는 자유 표면 흐름의 문제에 대해 처음 고안된 수치 법이 MAC (Marker-and-Cell) 법입니다 (참고 문헌 1965 참조). 이 구조는 컨트롤 볼륨 고정 오일러 격자를 기반으로합니다. 격자 내의 유체의 위치는 유체와 함께 움직이고, 그 이외는 부피, 질량, 기타 특성을 갖지 않는 일련의 마커 입자에 의해 결정됩니다.

Grid cells containing markers are considered occupied by fluid, while those without markers are empty (or void). A free surface is defined to exist in any grid cell that contains particles and that also has at least one neighboring grid cell that is void. The location and orientation of the surface within the cell was not part of the original MAC method.

마커를 포함한 격자 셀은 유체로 채워져있는 것으로 간주되며 마커가 없는 격자 셀은 빈(무효)것입니다. 입자를 포함하고, 적어도 하나의 인접 격자 셀이 무효인 격자의 자유 표면은 존재하는 것으로 정의됩니다. 셀 표면의 위치와 방향은 원래의 MAC 법에 포함되지 않았습니다.

Evolution of surfaces was computed by moving the markers with locally interpolated fluid velocities. Some special treatments were required to define the fluid properties in newly filled grid cells and to cancel values in cells that are emptied.

표면의 발전(개선)은 국소적으로 보간된 유체 속도로 마커를 이동하여 계산되었습니다. 새롭게 충전된 격자 셀의 유체 특성을 정의하거나 비어있는 셀의 값을 취소하거나 하려면 특별한 처리가 필요했습니다.

The application of free-surface boundary conditions consisted of assigning the gas pressure to all surface cells. Also, velocity components were assigned to all locations on or immediately outside the surface in such a way as to approximate conditions of incompressibility and zero-surface shear stress.

자유 표면 경계 조건의 적용은 모든 표면 셀에 가스 압력을 할당하는 것으로 구성되었습니다. 또한 속도 성분은 비압축성 및 제로 표면 전단 응력의 조건을 근사화하는 방식으로 표면 위 또는 외부의 모든 위치에 할당되었습니다.

The extraordinary success of the MAC method in solving a wide range of complicated free-surface flow problems is well documented in numerous publications. One reason for this success is that the markers do not track surfaces directly, but instead track fluid volumes. Surfaces are simply the boundaries of the volumes, and in this sense surfaces may appear, merge or disappear as volumes break apart or coalesce.

폭넓게 복잡한 자유 표면 흐름 문제 해결에 MAC 법이 놀라운 성공을 거두고 있는 것은 수많은 문헌에서 충분히 입증되고 있습니다. 이 성공 이유 중 하나는 마커가 표면을 직접 추적하는 것이 아니라 유체의 체적을 추적하는 것입니다. 표면은 체적의 경계에 불과하며, 그러한 의미에서 표면은 분할 또는 합체된 부피로 출현(appear), 병합, 소멸 할 가능성이 있습니다.

A variety of improvements have contributed to an increase in the accuracy and applicability of the original MAC method. For example, applying gas pressures at interpolated surface locations within cells improves the accuracy in problems driven by hydrostatic forces, while the inclusion of surface tension forces extends the method to a wider class of problems (see Refs. 1969, 1975).

다양한 개선으로 인해 원래 MAC 방법의 정확성과 적용 가능성이 증가했습니다. 예를 들어, 셀 내 보간 된 표면 위치에 가스 압력을 적용하면 정 수력으로 인한 문제의 정확도가 향상되는 반면 표면 장력의 포함은 방법을 더 광범위한 문제로 확장합니다 (참조 문헌. 1969, 1975).

In spite of its successes, the MAC method has been used primarily for two-dimensional simulations because it requires considerable memory and CPU time to accommodate the necessary number of marker particles. Typically, an average of about 16 markers in each grid cell is needed to ensure an accurate tracking of surfaces undergoing large deformations.

수많은 성공에도 불구하고 MAC 방법은 필요한 수의 마커 입자를 수용하기 위해 상당한 메모리와 CPU 시간이 필요하기 때문에 주로 2 차원 시뮬레이션에 사용되었습니다. 일반적으로 큰 변형을 겪는 표면의 정확한 추적을 보장하려면 각 그리드 셀에 평균 약 16 개의 마커가 필요합니다.

Another limitation of marker particles is that they don’t do a very good job of following flow processes in regions involving converging/diverging flows. Markers are usually interpreted as tracking the centroids of small fluid elements. However, when those fluid elements get pulled into long convoluted strands, the markers may no longer be good indicators of the fluid configuration. This can be seen, for example, at flow stagnation points where markers pile up in one direction, but are drawn apart in a perpendicular direction. If they are pulled apart enough (i.e., further than one grid cell width) unphysical voids may develop in the flow.

마커 입자의 또 다른 한계는 수렴 / 발산 흐름이 포함된 영역에서 흐름 프로세스를 따라가는 작업을 잘 수행하지 못한다는 것입니다. 마커는 일반적으로 작은 유체 요소의 중심을 추적하는 것으로 해석됩니다. 그러나 이러한 유체 요소가 길고 복잡한 가닥으로 당겨지면 마커가 더 이상 유체 구성의 좋은 지표가 될 수 없습니다. 예를 들어 마커가 한 방향으로 쌓여 있지만 수직 방향으로 떨어져 있는 흐름 정체 지점에서 볼 수 있습니다. 충분히 분리되면 (즉, 하나의 그리드 셀 너비 이상) 비 물리적 공극이 흐름에서 발생할 수 있습니다.

Surface Marker Method

One way to limit the memory and CPU time consumption of markers is to keep marker particles only on surfaces and not in the interior of fluid regions. Of course, this removes the volume tracking property of the MAC method and requires additional logic to determine when and how surfaces break apart or coalesce.

표면 마커 법

마커의 메모리 및 CPU 시간의 소비를 제한하는 방법 중 하나는 마커 입자를 유체 영역의 내부가 아니라 표면에만 보존하는 것입니다. 물론 이는 MAC 법의 체적 추적 특성이 배제되기 때문에 표면이 분할 또는 합체하는 방식과 시기를 특정하기위한 논리를 추가해야합니다.

In two dimensions the marker particles on a surface can be arranged in a linear order along the surface. This arrangement introduces several advantages, such as being able to maintain a uniform particle spacing and simplifying the computation of intersections between different surfaces. Surface markers also provide a convenient way to locate the surface within a grid cell for the application of boundary conditions.

2 차원의 경우 표면 마커 입자는 표면을 따라 선형으로 배치 할 수 있습니다. 이 배열은 입자의 간격을 균일하게 유지할 수있는 별도의 표면이 교차하는 부분의 계산이 쉽다는 등 몇 가지 장점이 있습니다. 또한 표면 마커를 사용하여 경계 조건을 적용하면 격자 셀의 표면을 간단한 방법으로 찾을 수 있습니다.

Unfortunately, in three-dimensions there is no simple way to order particles on surfaces, and this leads to a major failing of the surface marker technique. Regions may exist where surfaces are expanding and no markers fill the space. Without markers the configuration of the surface is unknown, consequently there is no way to add markers. Reference 1975 contains examples that show the advantages and limitations of this method.

불행히도 3 차원에서는 표면에 입자를 정렬하는 간단한 방법이 없으며 이로 인해 표면 마커 기술이 크게 실패합니다. 표면이 확장되고 마커가 공간을 채우지 않는 영역이 존재할 수 있습니다. 마커가 없으면 표면의 구성을 알 수 없으므로 마커를 추가 할 방법이 없습니다.
참고 문헌 1975이 방법의 장점과 한계를 보여주는 예제가 포함되어 있습니다.

Volume-of-Fluid (VOF) Method

The last method to be discussed is based on the concept of a fluid volume fraction. The idea for this approach originated as a way to have the powerful volume-tracking feature of the MAC method without its large memory and CPU costs.

VOF (Volume-of-Fluid) 법

마지막으로 설명하는 방법은 유체 부피 분율의 개념을 기반으로합니다. 이 접근 방식에 대한 아이디어는 대용량 메모리 및 CPU 비용없이 MAC 방식의 강력한 볼륨 추적 기능을 갖는 방법에서 시작되었습니다.

Within each grid cell (control volume) it is customary to retain only one value for each flow quantity (e.g., pressure, velocity, temperature, etc.) For this reason it makes little sense to retain more information for locating a free surface. Following this reasoning, the use of a single quantity, the fluid volume fraction in each grid cell, is consistent with the resolution of the other flow quantities.

각 격자 셀 (제어 체적) 내에서 각 유량 (예 : 압력, 속도, 온도 등)에 대해 하나의 값만 유지하는 것이 일반적입니다. 이러한 이유로 자유 표면을 찾기 위해 더 많은 정보를 유지하는 것은 거의 의미가 없습니다. 이러한 추론에 따라 각 격자 셀의 유체 부피 분율인 단일 수량의 사용은 다른 유량의 해상도와 일치합니다.

If we know the amount of fluid in each cell it is possible to locate surfaces, as well as determine surface slopes and surface curvatures. Surfaces are easy to locate because they lie in cells partially filled with fluid or between cells full of fluid and cells that have no fluid.

각 셀 내의 유체의 양을 알고 있는 경우, 표면의 위치 뿐만 아니라 표면 경사와 표면 곡률을 결정하는 것이 가능합니다. 표면은 유체 가 부분 충전 된 셀 또는 유체가 전체에 충전 된 셀과 유체가 전혀없는 셀 사이에 존재하기 때문에 쉽게 찾을 수 있습니다.

Slopes and curvatures are computed by using the fluid volume fractions in neighboring cells. It is essential to remember that the volume fraction should be a step function, i.e., having a value of either one or zero. Knowing this, the volume fractions in neighboring cells can then be used to locate the position of fluid (and its slope and curvature) within a particular cell.

경사와 곡률은 인접 셀의 유체 체적 점유율을 사용하여 계산됩니다. 체적 점유율은 계단 함수(step function)이어야 합니다, 즉, 값이 1 또는 0 인 것을 기억하는 것이 중요합니다. 이 것을 안다면, 인접 셀의 부피 점유율을 사용하여 특정 셀 내의 유체의 위치 (및 그 경사와 곡률)을 찾을 수 있습니다.

Free-surface boundary conditions must be applied as in the MAC method, i.e., assigning the proper gas pressure (plus equivalent surface tension pressure) as well as determining what velocity components outside the surface should be used to satisfy a zero shear-stress condition at the surface. In practice, it is sometimes simpler to assign velocity gradients instead of velocity components at surfaces.

자유 표면 경계 조건을 MAC 법과 동일하게 적용해야 합니다. 즉, 적절한 기체 압력 (및 대응하는 표면 장력)을 할당하고, 또한 표면에서 제로 전단 응력을 충족 시키려면 표면 외부의 어떤 속도 성분을 사용할 필요가 있는지를 확인합니다. 사실, 표면에서의 속도 성분 대신 속도 구배를 지정하는 것이보다 쉬울 수 있습니다.

Finally, to compute the time evolution of surfaces, a technique is needed to move volume fractions through a grid in such a way that the step-function nature of the distribution is retained. The basic kinematic equation for fluid fractions is similar to that for the height-function method, where F is the fraction of fluid function:

마지막으로, 표면의 시간 변화를 계산하려면 분포의 계단 함수의 성질이 유지되는 방법으로 격자를 통과하고 부피 점유율을 이동하는 방법이 필요합니다. 유체 점유율의 기본적인 운동학방정식은 높이 함수(height-function) 법과 유사합니다. F는 유체 점유율 함수입니다.

A straightforward numerical approximation cannot be used to model this equation because numerical diffusion and dispersion errors destroy the sharp, step-function nature of the F distribution.

이 방정식을 모델링 할 때 간단한 수치 근사는 사용할 수 없습니다. 수치의 확산과 분산 오류는 F 분포의 명확한 계단 함수(step-function)의 성질이 손상되기 때문입니다.

It is easy to accurately model the solution to this equation in one dimension such that the F distribution retains its zero or one values. Imagine fluid is filling a column of cells from bottom to top. At some instant the fluid interface is in the middle region of a cell whose neighbor below is filled and whose neighbor above is empty. The fluid orientation in the neighboring cells means the interface must be located above the bottom of the cell by an amount equal to the fluid fraction in the cell. Then the computation of how much fluid to move into the empty cell above can be modified to first allow the empty region of the surface-containing cell to fill before transmitting fluid on to the next cell.

F 분포가 0 또는 1의 값을 유지하는 같은 1 차원에서이 방정식의 해를 정확하게 모델링하는 것은 간단합니다. 1 열의 셀에 위에서 아래까지 유체가 충전되는 경우를 상상해보십시오. 어느 순간에 액체 계면은 셀의 중간 영역에 있고, 그 아래쪽의 인접 셀은 충전되어 있고, 상단 인접 셀은 비어 있습니다. 인접 셀 내의 유체의 방향은 계면과 셀의 하단과의 거리가 셀 내의 유체 점유율과 같아야 한다는 것을 의미합니다. 그 다음 먼저 표면을 포함하는 셀의 빈 공간을 충전 한 후 다음 셀로 유체를 보내도록 위쪽의 빈 셀에 이동하는 유체의 양의 계산을 변경할 수 있습니다.

In two or three dimensions a similar procedure of using information from neighboring cells can be used, but it is not possible to be as accurate as in the one-dimensional case. The problem with more than one dimension is that an exact determination of the shape and location of the surface cannot be made. Nevertheless, this technique can be made to work well as evidenced by the large number of successful applications that have been completed using the VOF method. References 1975, 1980, and 1981 should be consulted for the original work on this technique.

2 차원과 3 차원에서 인접 셀의 정보를 사용하는 유사한 절차를 사용할 수 있지만, 1 차원의 경우만큼 정확하게 하는 것은 불가능합니다. 2 차원 이상의 경우의 문제는 표면의 모양과 위치를 정확히 알 수없는 것입니다. 그래도 VOF 법을 사용하여 달성 된 다수의 성공 사례에서 알 수 있듯이 이 방법을 잘 작동시킬 수 있습니다. 이 기법에 관한 초기의 연구 내용은 참고 문헌 1975,1980,1981를 참조하십시오.

The VOF method has lived up to its goal of providing a method that is as powerful as the MAC method without the overhead of that method. Its use of volume tracking as opposed to surface-tracking function means that it is robust enough to handle the breakup and coalescence of fluid masses. Further, because it uses a continuous function it does not suffer from the lack of divisibility that discrete particles exhibit.

VOF 법은 MAC 법만큼 강력한 기술을 오버 헤드없이 제공한다는 목표를 달성 해 왔습니다. 표면 추적이 아닌 부피 추적 기능을 사용하는 것은 유체 질량의 분할과 합체를 처리하는 데 충분한 내구성을 가지고 있다는 것을 의미합니다. 또한 연속 함수를 사용하기 때문에 이산된 입자에서 발생하는 숫자를 나눌 수 없는 문제를 겪지 않게 됩니다.

Variable-Density Approximation to the VOF Method

One feature of the VOF method that requires special treatment is the application of boundary conditions. As a surface moves through a grid, the cells containing fluid continually change, which means that the solution region is also changing. At the free boundaries of this changing region the proper free surface stress conditions must also be applied.

VOF 법의 가변 밀도 근사

VOF 법의 특수 처리가 필요한 기능 중 하나는 경계 조건의 적용입니다. 표면이 격자를 통과하여 이동할 때 유체를 포함하는 셀은 끊임없이 변화합니다. 즉, 계산 영역도 변화하고 있다는 것입니다. 이 변화하고있는 영역의 자유 경계에는 적절한 자유 표면 응력 조건도 적용해야합니다.

Updating the flow region and applying boundary conditions is not a trivial task. For this reason some approximations to the VOF method have been used in which flow is computed in both liquid and gas regions. Typically, this is done by treating the flow as a single fluid having a variable density. The F function is used to define the density. An argument is then made that because the flow equations are solved in both liquid and gas regions there is no need to set interfacial boundary conditions.

유체 영역의 업데이트 및 경계 조건의 적용은 중요한 작업입니다. 따라서 액체와 기체의 두 영역에서 흐름이 계산되는 VOF 법에 약간의 근사가 사용되어 왔습니다. 일반적으로 가변 밀도를 가진 단일 유체로 흐름을 처리함으로써 이루어집니다. 밀도를 정의하려면 F 함수를 사용합니다. 그리고, 흐름 방정식은 액체와 기체의 두 영역에서 계산되기 때문에 계면의 경계 조건을 설정할 필요가 없다는 논증이 이루어집니다.

Unfortunately, this approach does not work very well in practice for two reasons. First, the sensitivity of a gas region to pressure changes is generally much greater than that in liquid regions. This makes it difficult to achieve convergence in the coupled pressure-velocity solution. Sometimes very large CPU times are required with this technique.

공교롭게도 이 방법은 두 가지 이유로 인해 실제로는 그다지 잘 작동하지 않습니다. 하나는 압력의 변화에 대한 기체 영역의 감도가 일반적으로 액체 영역보다 훨씬 큰 것입니다. 따라서 압력 – 속도 결합 해법 수렴을 달성하는 것은 어렵습니다. 이 기술은 필요한 CPU 시간이 매우 커질 수 있습니다.

The second, and more significant, reason is associated with the possibility of a tangential velocity discontinuity at interfaces. Because of their different responses to pressure, gas and liquid velocities at an interface are usually quite different. In the Variable-Density model interfaces are moved with an average velocity, but this often leads to unrealistic movement of the interfaces.

두 번째 더 중요한 이유는 계면에서 접선 속도가 불연속이되는 가능성에 관련이 있습니다. 압력에 대한 반응이 다르기 때문에 계면에서 기체와 액체의 속도는 일반적으로 크게 다릅니다. 가변 밀도 모델은 계면은 평균 속도로 동작하지만, 이는 계면의 움직임이 비현실적으로 되는 경우가 많습니다.

Even though the Variable-Density method is sometimes referred to as a VOF method, because is uses a fraction-of-fluid function, this designation is incorrect. For accurately tracking sharp liquid-gas interfaces it is necessary to actually treat the interface as a discontinuity. This means it is necessary to have a technique to define an interface discontinuity, as well as a way to impose the proper boundary conditions at that interface. It is also necessary to use a special numerical method to track interface motions though a grid without destroying its character as a discontinuity.

가변 밀도 방법은 유체 분율 함수를 사용하기 때문에 VOF 방법이라고도하지만 이것은 올바르지 않습니다. 날카로운 액체-가스 인터페이스를 정확하게 추적하려면 인터페이스를 실제로 불연속으로 처리해야합니다. 즉, 인터페이스 불연속성을 정의하는 기술과 해당 인터페이스에서 적절한 경계 조건을 적용하는 방법이 필요합니다. 또한 불연속성으로 특성을 훼손하지 않고 격자를 통해 인터페이스 동작을 추적하기 위해 특수한 수치 방법을 사용해야합니다.

Summary

A brief discussion of the various techniques used to numerically model free surfaces has been given here with some comments about their relative advantages and disadvantages. Readers should not be surprised to learn that there have been numerous variations of these basic techniques proposed over the years. Probably the most successful of the methods is the VOF technique because of its simplicity and robustness. It is this method, with some refinement, that is used in the FLOW-3D program.

여기에서는 자유 표면을 수치적으로 모델링 할 때 사용하는 다양한 방법에 대해 상대적인 장점과 단점에 대한 설명을 포함하여 쉽게 설명하였습니다. 오랜 세월에 걸쳐 이러한 기본적인 방법이 많이 제안되어 온 것을 알고도 독자 여러분은 놀라지 않을 것입니다. 아마도 가장 성과를 거둔 방법은 간결하고 강력한 VOF 법 입니다. 이 방법에 일부 개량을 더한 것이 현재 FLOW-3D 프로그램에서 사용되고 있습니다.

Attempts to improve the VOF method have centered on better, more accurate, ways to move fluid fractions through a grid. Other developments have attempted to apply the method in connection with body-fitted grids and to employ more than one fluid fraction function in order to model more than one fluid component. A discussion of these developments is beyond the scope of this introduction.

VOF 법의 개선은 더 나은, 더 정확한 방법으로 유체 점유율을 격자를 통과하여 이동하는 것에 중점을 두어 왔습니다. 기타 개발은 물체 적합 격자(body-fitted grids) 관련 기법을 적용하거나 여러 유체 성분을 모델링하기 위해 여러 유체 점유율 함수를 채용하기도 했습니다. 이러한 개발에 대한 논의는 여기에서의 설명 범위를 벗어납니다.

References

1965 Harlow, F.H. and Welch, J.E., Numerical Calculation of Time-Dependent Viscous Incompressible Flow, Phys. Fluids 8, 2182.

1969 Daly, B.J., Numerical Study of the Effect of Surface Tension on Interface Instability, Phys. Fluids 12, 1340.

1970 Hirt, C.W., Cook, J.L. and Butler, T.D., A Lagrangian Method for Calculating the Dynamics of an Incompressible Fluid with Free Surface, J. Comp. Phys. 5, 103.

1971 Nichols, B.D. and Hirt, C.W.,Calculating Three-Dimensional Free Surface Flows in the Vicinity of Submerged and Exposed Structures, J. Comp. Phys. 12, 234.

1974 Hirt, C.W., Amsden, A.A., and Cook, J.L.,An Arbitrary Lagrangian-Eulerian Computing Method for all Flow Speeds, J. Comp. Phys., 14, 227.

1975 Nichols, B.D. and Hirt, C.W., Methods for Calculating Multidimensional, Transient Free Surface Flows Past Bodies, Proc. of the First International Conf. On Num. Ship Hydrodynamics, Gaithersburg, ML, Oct. 20-23.

1980 Nichols, B.D. and Hirt, C.W., Numerical Simulation of BWR Vent-Clearing Hydrodynamics, Nucl. Sci. Eng. 73, 196.

1981 Hirt, C.W. and Nichols, B.D., Volume of Fluid (VOF) Method for the Dynamics of Free Boundaries, J. Comp. Phys. 39, 201.

Thermocapillary Actuation

표면 장력의 온도 의존성은 유체 방울을 패턴 있는 표면 위로 전달하는 데 사용될 수 있습니다. 표면은 유체 방울이 친수성-수소성 인터페이스에 의해 형성된 채널을 따르도록 제한되도록 친수성 또는 친수성 접촉 각도로 패턴화됩니다. 또한 프로그램 가능한 방식으로 가열된 마이크로 히터의 배열은 열전압 작동을 유발하여 유체 방울을 뜨거운 영역에서 차가운 지역으로 유도합니다. 아래 이미지는 문제 설정의 상단 및 단면 뷰(Anton A)를 보여줍니다. Darhuber 외.) 다음에 Flow-3D를 설정합니다.

Liquid droplet moving along hydrophilic microstripe — Top-view of a liquid droplet moving along a hydrophilic microstripe. The array of Ti-resistors (shown in light gray) beneath the hydrophilic stripes locally heat the droplet thereby modifying the surface tension and propelling the liquid toward the colder regions of the device surface. The dark gray stripes represent the leads and contacts (Au) for the heating resistors.

Cross sectional view of device — Cross-sectional view of a portion of the device containing two micro-heaters and an overlying droplet.

더 차가운 표면 온도 영역은 인접한 따뜻한 지점보다 더 높은 표면 장력을 유지하여 액체를 당기는 접선 표면 힘을가합니다. 부분적 습윤 (접촉각> 0) 표면은 전체 습윤 표면 (접촉각 = 0)에 비해 부피 손실이 적은 유체 수송을 허용하기 때문에 바람직한 옵션입니다.

Top view of the setup in FLOW-3D showing three microheaters in pink, yellow and blue respectively. The central hydrophilic strip is shown in black with a fluid (water) droplet in sky blue.

아래 애니메이션은 완전히 젖은 표면과 부분적으로 젖은 표면의 비교를 보여줍니다. 예상대로 완전히 젖은 표면은 부분적으로 젖은 표면보다 액적을 더 평평하게 (그리고 더 많이 퍼지게) 만듭니다. 히터가 한 번에 하나씩 활성화되면 물방울이 더 차가운 영역으로 이동됩니다. 더 많은 유체가 남겨질수록 시뮬레이션이 끝날 때까지 완전히 젖은 표면은 더 많은 유체 볼륨을 잃는 것을 볼 수 있습니다. 따라서 부분적으로 젖은 표면은 유체 손실을 줄이기위한 더 바람직한 옵션입니다. 두 경우 모두 소수성 표면으로 둘러싸인 중앙 친수성 스트립으로 인해 물방울이 중앙에 머물러야합니다.

Animation of the results post-processed in FlowSight.

References

Anton A. Darhuber, Joseph P. Valentino, Sandra M. Trian and Sigurd Wagner, Thermocapillary Actuation of Droplets on Chemically Patterned Surfaces by Programmable Microheater Arrays, Journal of Microelectrochemical Systems, Vol. 12, No. 6, December 2003

Coating Application/코팅분야 응용

해석 조건

Viscosity(점도) = 0.204 Pa-s
Density(밀도) = 965 kg/m^3
Surface tension(표면 장력) = 0.035N/m
Roll coating

물리 모델

Surface tension(표면 장력) 모델
Viscosity(점도)
Moving Objects(운동)

Classic Inlet Flooded Regime

Revers Operating Regime

Inlet Starved Operating Regime

2D 시뮬레이션은 작동 코팅 윈도우의 빠른 평가를 제공
계단식, 공기 유입, 기아 및 런백을 식별
리빙(Ribbing)은 3D 분석이 필요

해석 결과

Air Entrainment(공기혼입) Analysis

일부 자유 표면 유동에서 난류 또는 특정 유동조건으로 인해 자유 표면에 가스(Air)가 혼입될 수 있습니다. 그러므로 유동 해석시 가스(Air) 혼입에 대한 고려를 해야합니다.

공기혼입의 예시

댐 수문게이트
정화장치 부문
Dam aerated flow region(댐 공기 유동 영역) etc.

Air entrainment physical processes(공기 혼입 물리 프로세스)

Entrained air transprot(혼입 공기 수송 모델)
: 혼입계수(The Entrainment rate coefficient)는 0.5가 적합
: 표면장력(The surface tension) 고려
Bulking : Variable density(가변 밀도 모델)
: 유입 유체의 밀도 조정은 유체 및 공기 밀도의 조합을 설명하기 위해 자동으로 계산됩니다. 결과적으로, 체적 유량은 유체 및 혼입 된 공기 혼합물의 총 체적 유량이며, 경계(Boundary)에서 공기의 농도를 정의 할 때 사용자가 고려해야합니다.
Turbulence model(RANS, RNG etc.)
: 공기 혼입(Air entrainment) 모델을 사용할 때 적절한 난류모델을 고려해야 합니다. 난류 모델에 대한 설명은 아래 링크를 참조하시길 바랍니다.

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Buoyancy : Variable density + Drift-Flux(부력의 효과)
: 부력(Buoyancy force) 효과를 고려하면 드리프트 플럭스 모델(Drift-Flux model)과의 상호 작용을 설명 할 수 있습니다. 이 경우, 기포는 밀도의 차이로 인해 유체 내에서 이동할 수 있으며 유체 운동에 영향을 줄 수 있습니다.

공기 혼입 모델(Air entrainement model) 해석 사례

Laser Welding and Additive Manufacturing

Application

Shallow penetration weld (Shallow 침투 용접)
Deep penetration weld (Deep 침투 용접)
Laser-arc hybrid welding(레이저-아크 하이브리드 용접)
Laser repair technology
Laser cladding(레이저 클레딩)
Laser powder bed fusion process

Laser/Heat source(레이저/열원)

레이저 출력 및 용접 속도 향상
– 더 큰 키홀(Keyhole) 개방 및 깊이 변동이 적음
– 후면 용융 풀 (Moltan Pool)의 난기류가 최소화된 키홀(Keyhole) 앞부분 벽(Wall)에 레이저 빔(Laser beam)이 노출
– 다공성 형성(Porosity formation) 최소화

Laser beam motion(레이저 빔 모션)

레이저 빔(Laser beam) 기울기 증가
– 큰 각도에서 유사한 방향을 따라 작용하는 중력 및 반동 압력으로 인해 후면 용융 풀(Moltan pool)에서 층류(Laminar flow)가 관찰
– 다공성 발생(Porosity occurrence) 최소화

해석 사례

Laser metal deposition(레이저 금속 증착) -Single layer

40마이크론 유체 입자 주입 (500,000/sec)
레이저 출력 : 100W
스캔속도 : 1cm/sec
레이저 빔 직경 : 2mm
재질 : IN-718 meterail alloy

Laser metal deposition(레이저 금속 증착) – Multilayer

Laser powder bed fusion process

FLOW-3D DEM 및 FLOW-3D WELD 고려
– 용융 영역(Melt region)
– 용융 풀(Melt pool)의 속도 및 온도
– 고체 영역(Solid fraction)
레이저 방사(Laser irradiation) 조건
– 출력 : 200W
– 스캔속도 : 3m/s
– Spot radius : 0.1mm

Additive Manufacturing & Welding Bibliography

다음은 적층 제조 및 용접 참고 문헌의 기술 문서 모음입니다. 이 모든 논문에는 FLOW-3D AM 결과가 나와 있습니다. FLOW-3D AM을 사용하여 적층 제조, 레이저 용접 및 기타 용접 기술에서 발견되는 프로세스를 성공적으로 시뮬레이션하는 방법에 대해 자세히 알아보십시오.

2023년 3월 10일 update

46-23 Guangwei Lu, Jinxin Liu, Zhixian Cao, Youwei Li, Xueting Lei, Ying Li, A computational study of 3D flow structure in two consecutive bends subject to the influence of tributary inflow in the middle Yangtze Rive, Engineering Applications of Computational Fluid Mechanics, 17.1; 2183901, 2023. doi.org/10.1080/19942060.2023.2183901

45-23 Daniel Martinez, Philip King, Santosh Reddy Sama, Jay Sim, Hakan Toykoc, Guha Manogharan, Effect of freezing range on reducing casting defects through 3D sand-printed mold designs, The International Journal of Advanced Manufacturing Technology, 2023. doi.org/10.1007/s00170-023-11112-x

39-23 Peter S. Cook, David J. Ritchie, Determining the laser absorptivity of Ti-6Al-4V during laser powder bed fusion by calibrated melt pool simulation, Optics & Laser Technology, 162; 109247. 2023. doi.org/10.1016/j.optlastec.2023.109247

36-23 Yixuan Chen, Weihao Wang, Yao Ou, Yingna Wu, Zirong Zhai, Rui Yang, Impact of laser power and scanning velocity on microstructure and mechanical properties of Inconel 738LC alloys fabricated by laser powder bed fusion, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, pp. 138-149, 2023. doi.org/10.1007/978-3-031-22524-6_15

34-23 Chao Kang, Ikki Ikeda, Motoki Sakaguchi, Recoil and solidification of a paraffin droplet impacted on a metal substrate: Numerical study and experimental verification, Journal of Fluids and Structures, 118; 103839, 2023. doi.org/10.1016/j.jfluidstructs.2023.103839

30-23 Fei Wang, Tiechui Yuan, Ruidi Li, Shiqi Lin, Zhonghao Xie, Lanbo Li, Valentino Cristino, Rong Xu, Bing liu, Comparative study on microstructures and mechanical properties of ultra ductility single-phase Nb40Ti40Ta20 refractory medium entropy alloy by selective laser melting and vacuum arc melting, Journal of Alloys and Compounds, 942; 169065, 2023. doi.org/10.1016/j.jallcom.2023.169065

29-23 Haejin Lee, Yeonghwan Song, Seungkyun Yim, Kenta Aoyagi, Akihiko Chiba, Byoungsoo Lee, Influence of linear energy on side surface roughness in powder bed fusion electron beam melting process: Coupled experimental and simulation study, Powder Technology, 418; 118292, 2023.

27-23 Yinan Chen, Bo Li, Double-phase refractory medium entropy alloy NbMoTi via selective laser melting (SLM) additive manufacturing, Journal of Physics: Conference Series, 2419; 012074, 2023. doi.org/10.1088/1742-6596/2419/1/012074

23-23 Yunwei Gui, Kenta Aoyagi, Akihiko Chiba, Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization, Materials Science and Engineering: A, 864; 144595, 2023. doi.org/10.1016/j.msea.2023.144595

21-23 Tatsuhiko Sakai, Yasuhiro Okamoto, Nozomi Taura, Riku Saito, Akira Okada, Effect of scanning speed on molten metal behaviour under angled irradiation with a continuous-wave laser, Journal of Materials Processing Technology, 313; 117866, 2023. doi.org/10.1016/j.jmatprotec.2023.117866

19-23 Gianna M. Valentino, Arunima Banerjee, Alexander lark, Christopher M. Barr, Seth H. Myers, Ian D. McCue, Influence of laser processing parameters on the density-ductility tradeoff in additively manufactured pure tantalum, Additive Manufacturing Letters, 4; 100117, 2023. doi.org/10.1016/j.addlet.2022.100117

15-23 Runbo Jiang, Zhongshu Ren, Joseph Aroh, Amir Mostafaei, Benjamin Gould, Tao Sun, Anthony D. Rollett, Quantifying equiaxed vs epitaxial solidification in laser melting of CMSX-4 single crystal superalloy, Metallurgical and Materials Transactions A, 54; pp. 808-822, 2023. doi.org/10.1007/s11661-022-06929-2

14-23 Nguyen Thi Tien, Yu-Lung Lo, M. Mohsin Raza, Cheng-Yen Chen, Chi-Pin Chiu, Optimization of processing parameters for pulsed laser welding of dissimilar metal interconnects, Optics & Laser Technology, 159; 109022, 2023. doi.org/10.1016/j.optlastec.2022.109022

9-23 Hou Yi Chia, Wentao Yan, High-fidelity modeling of metal additive manufacturing, Additive Manufacturing Technology: Design, Optimization, and Modeling, Ed. Kun Zhou, 2023.

8-23 Akash Aggarwal, Yung C. Shin, Arvind Kumar, Investigation of the transient coupling between the dynamic laser beam absorptance and the melt pool – vapor depression morphology in laser powder bed fusion process, International Journal of Heat and Mass Transfer, 201.2; 123663, 2023. doi.org/10.1016/j.ijheatmasstransfer.2022.123663

180-22 Xu Kaikai, Gong Yadong, Zhang Qiang, Numerical simulation of dynamic analysis of molten pool in the process of direct energy deposition, The International Journal of Advanced Manufacturing Technology, 2022. doi.org/10.1007/s00170-022-10271-7

179-22 Yasuhiro Okamoto, Nozomi Taura, Akira Okada, Study on laser drilling process of solid metal on its liquid, International Journal of Electrical Machining, 27; 2022. doi.org/10.2526/ijem.27.35

175-22 Lu Min, Xhi Xiaojie, Lu Peipei, Wu Meiping, Forming quality and wettability of surface texture on CuSn10 fabricated by laser powder bed fusion, AIP Advances, 12.12; 125114, 2022. doi.org/10.1063/5.0122076

174-22 Thinus Van Rhijn, Willie Du Preez, Maina Maringa, Dean Kouprianoff, An investigation into the optimization of the selective laser melting process parameters for Ti6Al4V through numerical modelling, JOM, 2022. doi.org/10.1007/s11837-022-05608-2

171-22 Jonathan Yoshioka, Mohsen Eshraghi, Temporal evolution of temperature gradient and solidification rate in laser powder bed fusion additive manufacturing, Heat and Mass Transfer, 2022. doi.org/10.1007/s00231-022-03318-8

170-22 Subin Shrestha and Kevin Chou, Residual heat effect on the melt pool geometry during the laser powder bed fusion process, Journal of Manufacturing and Materials Processing, 6.6; 153, 2022. doi.org/10.3390/jmmp6060153

169-22 Aryan Aryan, Obinna Chukwubuzo, Desmond Bourgeois, Wei Zhang, Hardness prediction by incorporating heat transfer and molten pool fluid flow in a mult-pass, multi-layer weld for onsite repair of Grade 91 steel, U.S. Department of Energy Office of Scientific and Technical Information, DOE-OSU-0032067, 2022. doi.org/10.2172/1898594

158-22 Dafan Du, Lu Wang, Anping Dong, Wentao Yan, Guoliang Zhu, Baode Sun, Promoting the densification and grain refinement with assistance of static magnetic field in laser powder bed fusion, International Journal of Machine Tools and Manufacture, 183; 103965, 2022. doi.org/10.1016/j.ijmachtools.2022.103965

157-22 Han Chu, Jiang Ping, Geng Shaoning, Liu Kun, Nucleation mechanism in oscillating laser welds of 2024 aluminium alloy: A combined experimental and numerical study, Optics & Laser Technology, 158.A; 108812, 2022. doi.org/10.1016/j.optlastec.2022.108812

153-22 Zixiang Li, Yinan Cui, Baohua Chang, Guan Liu, Ze Pu, Haoyu Zhang, Zhiyue Liang, Changmeng Liu, Li Wang, Dong Du, Manipulating molten pool in in-situ additive manufacturing of Ti-22Al-25 Nb through alternating dual-electron beams, Additive Manufacturing, 60.A; 103230, 2022. doi.org/10.1016/j.addma.2022.103230

149-22 Qian Chen, Yao Fu, Albert C. To, Multiphysics modeling of particle spattering and induced defect formation mechanism in Inconel 718 laser powder bed fusion, The International Journal of Advanced Manufacturing Technology, 123; pp. 783-791, 2022. doi.org/10.1007/s00170-022-10201-7

146-22 Zixuan Wan, Hui-ping Wang, Jingjing Li, Baixuan Yang, Joshua Solomon, Blair Carlson, Effect of welding mode on remote laser stitch welding of zinc-coated steel with different sheet thickness combinations, Journal of Manufacturing Science and Engineering, MANU-21-1598, 2022. doi.org/10.1115/1.4055792

143-22 Du-Rim Eo, Seong-Gyu Chung, JeongHo Yang, Won Tae Cho, Sun-Hong Park, Jung-Wook Cho, Surface modification of high-Mn steel via laser-DED: Microstructural characterization and hot crack susceptibility of clad layer, Materials & Design, 223; 111188, 2022. doi.org/10.1016/j.matdes.2022.111188

142-22 Zichuan Fu, Xiangman Zhou, Bin Luo, Qihua Tian, Numerical simulation study of the effect of weld current on WAAM welding pool dynamic and weld bead morphology, International Conference on Mechanical Design and Simulation, Proceedings, 12261; 122614G, 2022.

132-22 Yiyu Huang, Zhonghao Xie, Wenshu Li, Haoyu Chen, Bin Liu, Bingfeng Wang, Dynamic mechanical properties of the selective laser melting NiCrFeCoMo0.2 high entropy alloy and the microstructure of molten pool, Journal of Alloys and Compounds, 927; 167011, 2022. doi.org/10.1016/j.jallcom.2022.167011

126-22 Jingqi Zhang, Yingang Liu, Gang Sha, Shenbao Jin, Ziyong Hou, Mohamad Bayat, Nan Yang, Qiyang Tan, Yu Yin, Shiyang Liu, Jesper Henri Hattel, Matthew Dargusch, Xiaoxu Huang, Ming-Xing Zhang, Designing against phase and property heterogeneities in additively manufactured titanium alloys, Nature Communications, 13; 4660, 2022. doi.org/10.1038/s41467-022-32446-2

119-22 Xu Kaikai, Gong Yadong, Zhao Qiang, Numerical simulation on molten pool flow of Inconel718 alloy based on VOF during additive manufacturing, Materials Today Communications, 33; 104147, 2022. doi.org/10.1016/j.mtcomm.2022.104147

118-22 AmirPouya Hemmasian, Francis Ogoke, Parand Akbari, Jonathan Malen, Jack Beuth, Amir Barati Farimani, Surrogate modeling of melt pool thermal field using deep learning, SSRN, 2022. doi.org/10.2139/ssrn.4190835

117-22 Chiara Ransenigo, Marialaura Tocci, Filippo Palo, Paola Ginestra, Elisabetta Ceretti, Marcello Gelfi, Annalisa Pola, Evolution of melt pool and porosity during laser powder bed fusion of Ti6Al4V alloy: Numerical modelling and experimental validation, Lasers in Manufacturing and Materials Processing, 2022. doi.org/10.1007/s40516-022-00185-3

112-22 Chris Jasien, Alec Saville, Chandler Gus Becker, Jonah Klemm-Toole, Kamel Fezzaa, Tao Sun, Tresa Pollock, Amy J. Clarke, In situ x-ray radiography and computational modeling to predict grain morphology in β-titanium during simulated additive manufacturing, Metals, 12.7; 1217, 2022. doi.org/10.3390/met12071217

110-22 Haotian Zhou, Haijun Su, Yinuo Guo, Peixin Yang, Yuan Liu, Zhonglin Shen, Di Zhao, Haifang Liu, Taiwen Huang, Min Guo, Jun Zhang, Lin Liu, Hengzhi Fu, Formation and evolution mechanisms of pores in Inconel 718 during selective laser melting: Meso-scale modeling and experimental investigations, Journal of Manufacturing Processes, 81; pp. 202-213, 2022. doi.org/10.1016/j.jmapro.2022.06.072

109-22 Yufan Zhao, Huakang Bian, Hao Wang, Aoyagi Kenta, Yamanaka Kenta, Akihiko Chiba, Non-equilibrium solidification behavior associated with powder characteristics during electron beam additive manufacturing, Materials & Design, 221; 110915, 2022. doi.org/10.1016/j.matdes.2022.110915

107-22 Dan Lönn, David Spångberg, Study of process parameters in laser beam welding of copper hairpins, Thesis, University of Skövde, 2022.

106-22 Liping Guo, Hongze Wang, Qianglong Wei, Hanjie Liu, An Wang, Yi Wu, Haowei Wang, A comprehensive model to quantify the effects of additional nano-particles on the printability in laser powder bed fusion of aluminum alloy and composite, Additive Manufacturing, 58; 103011, 2022. doi.org/10.1016/j.addma.2022.103011

104-22 Hongjiang Pan, Thomas Dahmen, Mohamad Bayat, Kang Lin, Xiaodan Zhang, Independent effects of laser power and scanning speed on IN718’s precipitation and mechanical properties produced by LBPF plus heat treatment, Materials Science and Engineering: A, 849; 143530, 2022. doi.org/10.1016/j.msea.2022.143530

101-22 Yufan Zhao, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, A survey on basic influencing factors of solidified grain morphology during electron beam melting, Materials & Design, 221; 110927, 2022. doi.org/10.1016/j.matdes.2022.110927

98-22 Jon Spangenberg, Wilson Ricardo Leal da Silva, Md. Tusher Mollah, Raphaël Comminal, Thomas Juul Andersen, Henrik Stang, Integrating reinforcement with 3D concrete printing: Experiments and numerical modelling, Third RILEM International Conference on Concrete and Digital Fabrication, Eds. Ana Blanco, Peter Kinnell, Richard Buswell, Sergio Cavalaro, pp. 379-384, 2022.

93-22 Minglei Qu, Qilin Guo, Luis I. Escano, Samuel J. Clark Kamel Fezzaa, Lianyi Chen, Mitigating keyhole pore formation by nanoparticles during laser powder bed fusion additive manufacturing, Additive Manufacturing Letters, 100068, 2022. doi.org/10.1016/j.addlet.2022.100068

86-22 Patiparn Ninpetch, Prasert Chalermkarnnon, Pruet Kowitwarangkul, Multiphysics simulation of thermal-fluid behavior in laser powder bed fusion of H13 steel: Influence of layer thickness and energy input, Metals and Materials International, 2022. doi.org/10.1007/s12540-022-01239-z

85-22 Merve Biyikli, Taner Karagoz, Metin Calli, Talha Muslim, A. Alper Ozalp, Ali Bayram, Single track geometry prediction of laser metal deposited 316L-Si via multi-physics modelling and regression analysis with experimental validation, Metals and Materials International, 2022. doi.org/10.1007/s12540-022-01243-3

76-22 Zhichao Yang, Shuhao Wang, Lida Zhu, Jinsheng Ning, Bo Xin, Yichao Dun, Wentao Yan, Manipulating molten pool dynamics during metal 3D printing by ultrasound, Applied Physics Reviews, 9; 021416, 2022. doi.org/10.1063/5.0082461

73-22 Yu Sun, Liqun Li, Yu Hao, Sanbao Lin, Xinhua Tang, Fenggui Lu, Numerical modeling on formation of periodic chain-like pores in high power laser welding of thick steel plate, Journal of Materials Processing Technology, 306; 117638, 2022. doi.org/10.1016/j.jmatprotec.2022.117638

67-22 Yu Hao, Hiu-Ping Wang, Yu Sun, Liqun Li, Yihan Wu, Fenggui Lu, The evaporation behavior of zince and its effect on spattering in laser overlap welding of galvanized steels, Journal of Materials Processing Technology, 306; 117625, 2022. doi.org/10.1016/j.jmatprotec.2022.117625

65-22 Yanhua Zhao, Chuanbin Du, Peifu Wang, Wei Meng, Changming Li, The mechanism of in-situ laser polishing and its effect on the surface quality of nickel-based alloy fabricated by selective laser melting, Metals, 12.5; 778, 2022. doi.org/10.3390/met12050778

58-22 W.E. Alphonso, M. Bayat, M. Baier, S. Carmignato, J.H. Hattel, Multi-physics numerical modelling of 316L Austenitic stainless steel in laser powder bed fusion process at meso-scale, 17th UK Heat Transfer Conference (UKHTC2021), Manchester, UK, April 4-6, 2022.

57-22 Brandon Hayes, Travis Hainsworth, Robert MacCurdy, Liquid-solid co-printing of multi-material 3D fluidic devices via material jetting, Additive Manufacturing, in press, 102785, 2022. doi.org/10.1016/j.addma.2022.102785

55-22 Xiang Wang, Lin-Jie Zhang, Jie Ning, Suck-joo Na, Fluid thermodynamic simulation of Ti-6Al-4V alloy in laser wire deposition, 3D Printing and Additive Manufacturing, 2022. doi.org/10.1089/3dp.2021.0159

54-22 Junhao Zhao, Binbin Wang, Tong Liu, Liangshu Luo, Yanan Wang, Xiaonan Zheng, Liang Wang, Yanqing Su, Jingjie Guo, Hengzhi Fu, Dayong Chen, Study of in situ formed quasicrystals in Al-Mn based alloys fabricated by SLM, Journal of Alloys and Compounds, 909; 164847, 2022. doi.org/10.1016/j.jallcom.2022.164847

48-22 Yueming Sun, Jianxing Ma, Fei Peng, Konstantin G. Kornev, Making droplets from highly viscous liquids by pushing a wire through a tube, Physics of Fluids, 34; 032119, 2022. doi.org/10.1063/5.0082003

46-22 H.Z. Lu, T. Chen, H. Liu, H. Wang, X. Luo, C.H. Song, Constructing function domains in NiTi shape memory alloys by additive manufacturing, Virtual and Physical Prototyping, 17.3; 2022. doi.org/10.1080/17452759.2022.2053821

42-22 Islam Hassan, P. Ravi Selvaganapathy, Microfluidic printheads for highly switchable multimaterial 3D printing of soft materials, Advanced Materials Technologies, 2101709, 2022. doi.org/10.1002/admt.202101709

41-22 Nan Yang, Youping Gong, Honghao Chen, Wenxin Li, Chuanping Zhou, Rougang Zhou, Huifeng Shao, Personalized artificial tibia bone structure design and processing based on laser powder bed fusion, Machines, 10.3; 205, 2022. doi.org/10.3390/machines10030205

31-22 Bo Shen, Raghav Gnanasambandam, Rongxuan Wang, Zhenyu (James) Kong, Multi-Task Gaussian process upper confidence bound for hyperparameter tuning and its application for simulation studies of additive manufacturing, IISE Transactions, 2022. doi.org/10.1080/24725854.2022.2039813

27-22 Lida Zhu, Shuhao Wang, Hao Lu, Dongxing Qi, Dan Wang, Zhichao Yang, Investigation on synergism between additive and subtractive manufacturing for curved thin-walled structure, Virtual and Physical Prototyping, 17.2; 2022. doi.org/10.1080/17452759.2022.2029009

24-22 Hoon Sohn, Peipei Liu, Hansol Yoon, Kiyoon Yi, Liu Yang, Sangjun Kim, Real-time porosity reduction during metal directed energy deposition using a pulse laser, Journal of Materials Science & Technology, 116; pp. 214-223. doi.org/10.1016/j.jmst.2021.12.013

18-22 Yaohong Xiao, Zixuan Wan, Pengwei Liu, Zhuo Wang, Jingjing Li, Lei Chen, Quantitative simulations of grain nucleation and growth at additively manufactured bimetallic interfaces of SS316L and IN625, Journal of Materials Processing Technology, 302; 117506, 2022. doi.org/10.1016/j.jmatprotec.2022.117506

06-22 Amal Charles, Mohamad Bayat, Ahmed Elkaseer, Lore Thijs, Jesper Henri Hattel, Steffen Scholz, Elucidation of dross formation in laser powder bed fusion at down-facing surfaces: Phenomenon-oriented multiphysics simulation and experimental validation, Additive Manufacturing, 50; 102551, 2022. doi.org/10.1016/j.addma.2021.102551

05-22 Feilong Ji, Xunpeng Qin, Zeqi Hu, Xiaochen Xiong, Mao Ni, Mengwu Wu, Influence of ultrasonic vibration on molten pool behavior and deposition layer forming morphology for wire and arc additive manufacturing, International Communications in Heat and Mass Transfer, 130; 105789, 2022. doi.org/10.1016/j.icheatmasstransfer.2021.105789

150-21 Daniel Knüttel, Stefano Baraldo, Anna Valente, Konrad Wegener, Emanuele Carpanzano, Model based learning for efficient modelling of heat transfer dynamics, Procedia CIRP, 102; pp. 252-257, 2021. doi.org/10.1016/j.procir.2021.09.043

149-21 T. van Rhijn, W. du Preez, M. Maringa, D. Kouprianoff, Towards predicting process parameters for selective laser melting of titanium alloys through the modelling of melt pool characteristics, Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie, 40.1; 2021.

148-21 Qian Chen, Multiscale process modeling of residual deformation and defect formation for laser powder bed fusion additive manufacturing, Thesis, University of Pittsburgh, Pittsburgh, PA USA, 2021.

147-21 Pareekshith Allu, Developing process parameters through CFD simulations, Lasers in Manufacturing Conference, 2021.

143-21 Asif Ur Rehman, Muhammad Arif Mahmood, Fatih Pitir, Metin Uymaz Salamci, Andrei C. Popescu, Ion N. Mihailescu, Spatter formation and splashing induced defects in laser-based powder bed fusion of AlSi10Mg alloy: A novel hydrodynamics modelling with empirical testing, Metals, 11.12; 2023, 2021. doi.org/10.3390/met11122023

142-21 Islam Hassan, Ponnambalam Ravi Selvaganapathy, A microfluidic printhead with integrated hybrid mixing by sequential injection for multimaterial 3D printing, Additive Manufacturing, 102559, 2021. doi.org/10.1016/j.addma.2021.102559

137-21 Ting-Yu Cheng, Ying-Chih Liao, Enhancing drop mixing in powder bed by alternative particle arrangements with contradictory hydrophilicity, Journal of the Taiwan Institute of Chemical Engineers, 104160, 2021. doi.org/10.1016/j.jtice.2021.104160

134-21 Asif Ur Rehman, Muhammad Arif Mahmood, Fatih Pitir, Metin Uymaz Salamci, Andrei C. Popescu, Ion N. Mihailescu, 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, Nanomaterials, 11.2; 3284, 2021. doi.org/10.3390/nano11123284

128-21 Sang-Woo Han, Won-Ik Cho, Lin-Jie Zhang, Suck-Joo Na, Coupled simulation of thermal-metallurgical-mechanical behavior in laser keyhole welding of AH36 steel, Materials & Design, 212; 110275, 2021. doi.org/10.1016/j.matdes.2021.110275

127-21 Jiankang Huang, Zhuoxuan Li, Shurong Yu, Xiaoquan Yu, Ding Fan, Real-time observation and numerical simulation of the molten pool flow and mass transfer behavior during wire arc additive manufacturing, Welding in the World, 2021. doi.org/10.1007/s40194-021-01214-z

123-21 Boxue Song, Tianbiao Yu, Xingyu Jiang, Wenchao Xi, Xiaoli Lin, Zhelun Ma, ZhaoWang, Development of the molten pool and solidification characterization in single bead multilayer direct energy deposition, Additive Manufacturing, 102479, 2021. doi.org/10.1016/j.addma.2021.102479

112-21 Kathryn Small, Ian D. McCue, Katrina Johnston, Ian Donaldson, Mitra L. Taheri, Precision modification of microstructure and properties through laser engraving, JOM, 2021. doi.org/10.1007/s11837-021-04959-6

111-21 Yongki Lee, Jason Cheon, Byung-Kwon Min, Cheolhee Kim, Modelling of fume particle behaviour and coupling glass contamination during vacuum laser beam welding, Science and Technology of Welding and Joining, 2021. doi.org/10.1080/13621718.2021.1990658

110-21 Menglin Liu, Hao Yi, Huajun Cao, Rufeng Huang, Le Jia, Heat accumulation effect in metal droplet-based 3D printing: Evolution mechanism and elimination strategy, Additive Manufacturing, 48.A; 102413, 2021. doi.org/10.1016/j.addma.2021.102413

108-21 Nozomi Taura, Akiya Mitsunobu, Tatsuhiko Sakai, Yasuhiro Okamoto, Akira Okada, Formation and its mechanism of high-speed micro-grooving on metal surface by angled CW laser irradiation, Journal of Laser Micro/Nanoengineering, 16.2, 2021. doi.org/10.2961/jlmn.2021.02.2006

105-21 Jon Spangenberg, Wilson Ricardo Leal da Silva, Raphaël Comminal, Md. Tusher Mollah, Thomas Juul Andersen, Henrik Stang, Numerical simulation of multi-layer 3D concrete printing, RILEM Technical Letters, 6; pp. 119-123, 2021. doi.org/10.21809/rilemtechlett.2021.142

104-21 Lin Chen, Chunming Wang, Gaoyang Mi, Xiong Zhang, Effects of laser oscillating frequency on energy distribution, molten pool morphology and grain structure of AA6061/AA5182 aluminum alloys lap welding, Journal of Materials Research and Technology, 15; pp. 3133-3148, 2021. doi.org/10.1016/j.jmrt.2021.09.141

101-21 R.J.M. Wolfs, T.A.M. Salet, N. Roussel, Filament geometry control in extrusion-based additive manufacturing of concrete: The good, the bad and the ugly, Cement and Concrete Research, 150; 106615, 2021. doi.org/10.1016/j.cemconres.2021.106615

89-21 Wenlin Ye, Jin Bao, Jie Lei, Yichang Huang, Zhihao Li, Peisheng Li, Ying Zhang, Multiphysics modeling of thermal behavior of commercial pure titanium powder during selective laser melting, Metals and Materials International, 2021. doi.org/10.1007/s12540-021-01019-1

81-21 Lin Chen, Gaoyang Mi, Xiong Zhang, Chunming Wang, Effects of sinusoidal oscillating laser beam on weld formation, melt flow and grain structure during aluminum alloys lap welding, Journals of Materials Processing Technology, 298; 117314, 2021. doi.org/10.1016/j.jmatprotec.2021.117314

77-21 Yujie Cui, Yufan Zhao, Haruko Numata, Kenta Yamanaka, Huakang Bian, Kenta Aoyagi, Akihiko Chiba, Effects of process parameters and cooling gas on powder formation during the plasma rotating electrode process, Powder Technology, 393; pp. 301-311, 2021. doi.org/10.1016/j.powtec.2021.07.062

76-21 Md Tusher Mollah, Raphaël Comminal, Marcin P. Serdeczny, David B. Pedersen, Jon Spangenberg, Stability and deformations of deposited layers in material extrusion additive manufacturing, Additive Manufacturing, 46; 102193, 2021. doi.org/10.1016/j.addma.2021.102193

72-21 S. Sabooni, A. Chabok, S.C. Feng, H. Blaauw, T.C. Pijper, H.J. Yang, Y.T. Pei, Laser powder bed fusion of 17–4 PH stainless steel: A comparative study on the effect of heat treatment on the microstructure evolution and mechanical properties, Additive Manufacturing, 46; 102176, 2021. doi.org/10.1016/j.addma.2021.102176

71-21 Yu Hao, Nannan Chena, Hui-Ping Wang, Blair E. Carlson, Fenggui Lu, Effect of zinc vapor forces on spattering in partial penetration laser welding of zinc-coated steels, Journal of Materials Processing Technology, 298; 117282, 2021. doi.org/10.1016/j.jmatprotec.2021.117282

67-21 Lu Wang, Wentao Yan, Thermoelectric magnetohydrodynamic model for laser-based metal additive manufacturing, Physical Review Applied, 15.6; 064051, 2021. doi.org/10.1103/PhysRevApplied.15.064051

61-21 Ian D. McCue, Gianna M. Valentino, Douglas B. Trigg, Andrew M. Lennon, Chuck E. Hebert, Drew P. Seker, Salahudin M. Nimer, James P. Mastrandrea, Morgana M. Trexler, Steven M. Storck, Controlled shape-morphing metallic components for deployable structures, Materials & Design, 208; 109935, 2021. doi.org/10.1016/j.matdes.2021.109935

60-21 Mahyar Khorasani, AmirHossein Ghasemi, Martin Leary, William O’Neil, Ian Gibson, Laura Cordova, Bernard Rolfe, Numerical and analytical investigation on meltpool temperature of laser-based powder bed fusion of IN718, International Journal of Heat and Mass Transfer, 177; 121477, 2021. doi.org/10.1016/j.ijheatmasstransfer.2021.121477

57-21 Dae-Won Cho, Yeong-Do Park, Muralimohan Cheepu, Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics, International Communications in Heat and Mass Transfer, 125; 105243, 2021. doi.org/10.1016/j.icheatmasstransfer.2021.105243

55-21 Won-Sang Shin, Dae-Won Cho, Donghyuck Jung, Heeshin Kang, Jeng O Kim, Yoon-Jun Kim, Changkyoo Park, Investigation on laser welding of Al ribbon to Cu sheet: Weldability, microstructure and mechanical and electrical properties, Metals, 11.5; 831, 2021. doi.org/10.3390/met11050831

50-21 Mohamad Bayat, Venkata K. Nadimpalli, Francesco G. Biondani, Sina Jafarzadeh, Jesper Thorborg, Niels S. Tiedje, Giuliano Bissacco, David B. Pedersen, Jesper H. Hattel, On the role of the powder stream on the heat and fluid flow conditions during directed energy deposition of maraging steel—Multiphysics modeling and experimental validation, Additive Manufacturing, 43;102021, 2021. doi.org/10.1016/j.addma.2021.102021

47-21 Subin Shrestha, Kevin Chou, An investigation into melting modes in selective laser melting of Inconel 625 powder: single track geometry and porosity, The International Journal of Advanced Manufacturing Technology, 2021. doi.org/10.1007/s00170-021-07105-3

34-21 Haokun Sun, Xin Chu, Cheng Luo, Haoxiu Chen, Zhiying Liu, Yansong Zhang, Yu Zou, Selective laser melting for joining dissimilar materials: Investigations ofiInterfacial characteristics and in situ alloying, Metallurgical and Materials Transactions A, 52; pp. 1540-1550, 2021. doi.org/10.1007/s11661-021-06178-9

32-21 Shanshan Zhang, Subin Shrestha, Kevin Chou, On mesoscopic surface formation in metal laser powder-bed fusion process, Supplimental Proceedings, TMS 150th Annual Meeting & Exhibition (Virtual), pp. 149-161, 2021. doi.org/10.1007/978-3-030-65261-6_14

22-21 Patiparn Ninpetch, Pruet Kowitwarangkul, Sitthipong Mahathanabodee, Prasert Chalermkarnnon, Phadungsak Rattanadecho, Computational investigation of thermal behavior and molten metal flow with moving laser heat source for selective laser melting process, Case Studies in Thermal Engineering, 24; 100860, 2021. doi.org/10.1016/j.csite.2021.100860

19-21 M.B. Abrami, C. Ransenigo, M. Tocci, A. Pola, M. Obeidi, D. Brabazon, Numerical simulation of laser powder bed fusion processes, La Metallurgia Italiana, February; pp. 81-89, 2021.

16-21 Wenjun Ge, Jerry Y.H. Fuh, Suck Joo Na, Numerical modelling of keyhole formation in selective laser melting of Ti6Al4V, Journal of Manufacturing Processes, 62; pp. 646-654, 2021. doi.org/10.1016/j.jmapro.2021.01.005

11-21 Mohamad Bayat, Venkata K. Nadimpalli, David B. Pedersen, Jesper H. Hattel, A fundamental investigation of thermo-capillarity in laser powder bed fusion of metals and alloys, International Journal of Heat and Mass Transfer, 166; 120766, 2021. doi.org/10.1016/j.ijheatmasstransfer.2020.120766

10-21 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Thermal properties of powder beds in energy absorption and heat transfer during additive manufacturing with electron beam, Powder Technology, 381; pp. 44-54, 2021. doi.org/10.1016/j.powtec.2020.11.082

9-21 Subin Shrestha, Kevin Chou, A study of transient and steady-state regions from single-track deposition in laser powder bed fusion, Journal of Manufacturing Processes, 61; pp. 226-235, 2021. doi.org/10.1016/j.jmapro.2020.11.023

6-21 Qian Chen, Yunhao Zhao, Seth Strayer, Yufan Zhao, Kenta Aoyagi, Yuichiro Koizumi, Akihiko Chiba, Wei Xiong, Albert C. To, Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment, Additive Manufacturing, 37; 101642, 2021. doi.org/10.1016/j.addma.2020.101642

04-21 Won-Ik Cho, Peer Woizeschke, Analysis of molten pool dynamics in laser welding with beam oscillation and filler wire feeding, International Journal of Heat and Mass Transfer, 164; 120623, 2021. doi.org/10.1016/j.ijheatmasstransfer.2020.120623

121-20 Yufan Zhao, Yujie Cui, Haruko Numata, Huakang Bian, Kimio Wako, Kenta Yamanaka, Kenta Aoyagi, Akihiko Chiba, Centrifugal granulation behavior in metallic powder fabrication by plasma rotating electrode process, Scientific Reports, 10; 18446, 2020. doi.org/10.1038/s41598-020-75503-w

116-20 Raphael Comminal, Wilson Ricardo Leal da Silva, Thomas Juul Andersen, Henrik Stang, Jon Spangenberg, Modelling of 3D concrete printing based on computational fluid dynamics, Cement and Concrete Research, 138; 106256, 2020. doi.org/10.1016/j.cemconres.2020.106256

112-20 Peng Liu, Lijin Huan, Yu Gan, Yuyu Lei, Effect of plate thickness on weld pool dynamics and keyhole-induced porosity formation in laser welding of Al alloy, The International Journal of Advanced Manufacturing Technology, 111; pp. 735-747, 2020. doi.org/10.1007/s00170-020-05818-5

108-20 Fan Chen, Wentao Yan, High-fidelity modelling of thermal stress for additive manufacturing by linking thermal-fluid and mechanical models, Materials & Design, 196; 109185, 2020. doi.org/10.1016/j.matdes.2020.109185

104-20 Yunfu Tian, Lijun Yang, Dejin Zhao, Yiming Huang, Jiajing Pan, Numerical analysis of powder bed generation and single track forming for selective laser melting of SS316L stainless steel, Journal of Manufacturing Processes, 58; pp. 964-974, 2020. doi.org/10.1016/j.jmapro.2020.09.002

100-20 Raphaël Comminal, Sina Jafarzadeh, Marcin Serdeczny, Jon Spangenberg, Estimations of interlayer contacts in extrusion additive manufacturing using a CFD model, International Conference on Additive Manufacturing in Products and Applications (AMPA), Zurich, Switzerland, September 1-3: Industrializing Additive Manufacturing, pp. 241-250, 2020. doi.org/10.1007/978-3-030-54334-1_17

97-20 Paree Allu, CFD simulation for metal Additive Manufacturing: Applications in laser- and sinter-based processes, Metal AM, 6.4; pp. 151-158, 2020.

95-20 Yufan Zhao, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Role of operating and environmental conditions in determining molten pool dynamics during electron beam melting and selective laser melting, Additive Manufacturing, 36; 101559, 2020. doi.org/10.1016/j.addma.2020.101559

94-20 Yan Zeng, David Himmler, Peter Randelzhofer, Carolin Körner, Processing of in situ Al3Ti/Al composites by advanced high shear technology: influence of mixing speed, The International Journal of Advanced Manufacturing Technology, 110; pp. 1589-1599, 2020. doi.org/10.1007/s00170-020-05956-w

93-20 H. Hamed Zargari, K. Ito, M. Kumar, A. Sharma, Visualizing the vibration effect on the tandem-pulsed gas metal arc welding in the presence of surface tension active elements, International Journal of Heat and Mass Transfer, 161; 120310, 2020. doi.org/10.1016/j.ijheatmasstransfer.2020.120310

90-20 Guangxi Zhao, Jun Du, Zhengying Wei, Siyuan Xu, Ruwei Geng, Numerical analysis of aluminum alloy fused coating process, Journal of the Brazilian Society of Mechanical Science and Engineering, 42; 483, 2020. doi.org/10.1007/s40430-020-02569-y

85-20 Wenkang Huang, Hongliang Wang, Teresa Rinker, Wenda Tan, Investigation of metal mixing in laser keyhold welding of dissimilar metals, Materials & Design, 195; 109056, 2020. doi.org/10.1016/j.matdes.2020.109056

82-20 Pan Lu, Zhang Cheng-Lin, Wang Liang, Liu Tong, Liu Jiang-lin, Molten pool structure, temperature and velocity flow in selective laser melting AlCu5MnCdVA alloy, Materials Research Express, 7; 086516, 2020. doi.org/10.1088/2053-1591/abadcf

80-20 Yujie Cui, Yufan Zhao, Haruko Numata, Huakang Bian, Kimio Wako, Kento Yamanaka, Kenta Aoyagi, Chen Zhang, Akihiko Chiba, Effects of plasma rotating electrode process parameters on the particle size distribution and microstructure of Ti-6Al-4 V alloy powder, Powder Technology, 376; pp. 363-372, 2020. doi.org/10.1016/j.powtec.2020.08.027

78-20 F.Q. Liu, L. Wei, S.Q. Shi, H.L. Wei, On the varieties of build features during multi-layer laser directed energy deposition, Additive Manufacturing, 36; 101491, 2020. doi.org/10.1016/j.addma.2020.101491

75-20 Nannan Chen, Zixuan Wan, Hui-Ping Wang, Jingjing Li, Joshua Solomon, Blair E. Carlson, Effect of Al single bond Si coating on laser spot welding of press hardened steel and process improvement with annular stirring, Materials & Design, 195; 108986, 2020. doi.org/10.1016/j.matdes.2020.108986

72-20 Yujie Cui, Kenta Aoyagi, Yufan Zhao, Kenta Yamanaka, Yuichiro Hayasaka, Yuichiro Koizumi, Tadashi Fujieda, Akihiko Chiba, Manufacturing of a nanosized TiB strengthened Ti-based alloy via electron beam powder bed fusion, Additive Manufacturing, 36; 101472, 2020. doi.org/10.1016/j.addma.2020.101472

64-20 Dong-Rong Liu, Shuhao Wang, Wentao Yan, Grain structure evolution in transition-mode melting in direct energy deposition, Materials & Design, 194; 108919, 2020. doi.org/10.1016/j.matdes.2020.108919

61-20 Raphael Comminal, Wilson Ricardo Leal da Silva, Thomas Juul Andersen, Henrik Stang, Jon Spangenberg, Influence of processing parameters on the layer geometry in 3D concrete printing: Experiments and modelling, 2nd RILEM International Conference on Concrete and Digital Fabrication, RILEM Bookseries, 28; pp. 852-862, 2020. doi.org/10.1007/978-3-030-49916-7_83

60-20 Marcin P. Serdeczny, Raphaël Comminal, Md. Tusher Mollah, David B. Pedersen, Jon Spangenberg, Numerical modeling of the polymer flow through the hot-end in filament-based material extrusion additive manufacturing, Additive Manufacturing, 36; 101454, 2020. doi.org/10.1016/j.addma.2020.101454

58-20 H.L. Wei, T. Mukherjee, W. Zhang, J.S. Zuback, G.L. Knapp, A. De, T. DebRoy, Mechanistic models for additive manufacturing of metallic components, Progress in Materials Science, preprint, 2020. doi.org/10.1016/j.pmatsci.2020.100703

55-20 Masoud Mohammadpour, Experimental study and numerical simulation of heat transfer and fluid flow in laser welded and brazed joints, Thesis, Southern Methodist University, Dallas, TX, US; Available in Mechanical Engineering Research Theses and Dissertations, 24, 2020.

48-20 Masoud Mohammadpour, Baixuan Yang, Hui-Ping Wang, John Forrest, Michael Poss, Blair Carlson, Radovan Kovacevica, Influence of laser beam inclination angle on galvanized steel laser braze quality, Optics and Laser Technology, 129; 106303, 2020. doi.org/10.1016/j.optlastec.2020.106303

34-20 Binqi Liu, Gang Fang, Liping Lei, Wei Liu, A new ray tracing heat source model for mesoscale CFD simulation of selective laser melting (SLM), Applied Mathematical Modeling, 79; pp. 506-520, 2020. doi.org/10.1016/j.apm.2019.10.049

27-20 Xuesong Gao, Guilherme Abreu Farira, Wei Zhang and Kevin Wheeler, Numerical analysis of non-spherical particle effect on molten pool dynamics in laser-powder bed fusion additive manufacturing, Computational Materials Science, 179, art. no. 109648, 2020. doi.org/10.1016/j.commatsci.2020.109648

26-20 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka and Akihiko Chiba, Isothermal γ → ε phase transformation behavior in a Co-Cr-Mo alloy depending on thermal history during electron beam powder-bed additive manufacturing, Journal of Materials Science & Technology, 50, pp. 162-170, 2020. doi.org/10.1016/j.jmst.2019.11.040

21-20 Won-Ik Cho and Peer Woizeschke, Analysis of molten pool behavior with buttonhole formation in laser keyhole welding of sheet metal, International Journal of Heat and Mass Transfer, 152, art. no. 119528, 2020. doi.org/10.1016/j.ijheatmasstransfer.2020.119528

06-20 Wei Xing, Di Ouyang, Zhen Chen and Lin Liu, Effect of energy density on defect evolution in 3D printed Zr-based metallic glasses by selective laser melting, Science China Physics, Mechanics & Astronomy, 63, art. no. 226111, 2020. doi.org/10.1007/s11433-019-1485-8

04-20 Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

02-20 Dongsheng Wu, Shinichi Tashiro, Ziang Wu, Kazufumi Nomura, Xueming Hua, and Manabu Tanaka, Analysis of heat transfer and material flow in hybrid KPAW-GMAW process based on the novel three dimensional CFD simulation, International Journal of Heat and Mass Transfer, 147, art. no. 118921, 2020. doi.org/10.1016/j.ijheatmasstransfer.2019.118921

01-20 Xiang Huang, Siying Lin, Zhenxiang Bu, Xiaolong Lin, Weijin Yi, Zhihong Lin, Peiqin Xie, and Lingyun Wang, Research on nozzle and needle combination for high frequency piezostack-driven dispenser, International Journal of Adhesion and Adhesives, 96, 2020. doi.org/10.1016/j.ijadhadh.2019.102453

88-19 Bo Cheng and Charles Tuffile, Numerical study of porosity formation with implementation of laser multiple reflection in selective laser melting, Proceedings Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, ASME 2019 14th International Manufacturing Science and Engineering Conference, Erie, Pennsylvania, USA, June 10-14, 2019. doi.org/10.1115/MSEC2019-2891

87-19 Shuhao Wang, Lida Zhu, Jerry Ying His Fuh, Haiquan Zhang, and Wentao Yan, Multi-physics modeling and Gaussian process regression analysis of cladding track geometry for direct energy deposition, Optics and Lasers in Engineering, 127:105950, 2019. doi.org/10.1016/j.optlaseng.2019.105950

78-19 Bo Cheng, Lukas Loeber, Hannes Willeck, Udo Hartel, and Charles Tuffile, Computational investigation of melt pool process dynamics and pore formation in laser powder bed fusion, Journal of Materials Engineering and Performance, 28:11, 6565-6578, 2019. doi.org/10.1007/s11665-019-04435-y

77-19 David Souders, Pareekshith Allu, Anurag Chandorkar, and Ruendy Castillo, Application of computational fluid dynamics in developing process parameters for additive manufacturing, Additive Manufacturing Journal, 9th International Conference on 3D Printing and Additive Manufacturing Technologies (AM 2019), Bangalore, India, September 7-9, 2019.

75-19 Raphaël Comminal, Marcin Piotr Serdeczny, Navid Ranjbar, Mehdi Mehrali, David Bue Pedersen, Henrik Stang, Jon Spangenberg, Modelling of material deposition in big area additive manufacturing and 3D concrete printing, Proceedings, Advancing Precision in Additive Manufacturing, Nantes, France, September 16-18, 2019.

73-19 Baohua Chang, Zhang Yuan, Hao Cheng, Haigang Li, Dong Du 1, and Jiguo Shan, A study on the influences of welding position on the keyhole and molten pool behavior in laser welding of a titanium alloy, Metals, 9:1082, 2019. doi.org/10.3390/met9101082

57-19 Shengjie Deng, Hui-Ping Wang, Fenggui Lu, Joshua Solomon, and Blair E. Carlson, Investigation of spatter occurrence in remote laser spiral welding of zinc-coated steels, International Journal of Heat and Mass Transfer, Vol. 140, pp. 269-280, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.06.009

53-19 Mohamad Bayat, Aditi Thanki, Sankhya Mohanty, Ann Witvrouw, Shoufeng Yang, Jesper Thorborg, Niels Skat Tieldje, and Jesper Henri Hattel, Keyhole-induced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation, Additive Manufacturing, Vol. 30, 2019. doi.org/10.1016/j.addma.2019.100835

51-19 P. Ninpetch, P. Kowitwarangkul, S. Mahathanabodee, R. Tongsri, and P. Ratanadecho, Thermal and melting track simulations of laser powder bed fusion (L-PBF), International Conference on Materials Research and Innovation (ICMARI), Bangkok, Thailand, December 17-21, 2018. IOP Conference Series: Materials Science and Engineering, Vol. 526, 2019. doi.org/10.1088/1757-899X/526/1/012030

46-19 Hongze Wang and Yu Zou, Microscale interaction between laser and metal powder in powder-bed additive manufacturing: Conduction mode versus keyhole mode, International Journal of Heat and Mass Transfer, Vol. 142, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.118473

45-19 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka, and Akihiko Chiba, Manipulating local heat accumulation towards controlled quality and microstructure of a Co-Cr-Mo alloy in powder bed fusion with electron beam, Materials Letters, Vol. 254, pp. 269-272, 2019. doi.org/10.1016/j.matlet.2019.07.078

44-19 Guoxiang Xu, Lin Li, Houxiao Wang, Pengfei Li, Qinghu Guo, Qingxian Hu, and Baoshuai Du, Simulation and experimental studies of keyhole induced porosity in laser-MIG hybrid fillet welding of aluminum alloy in the horizontal position, Optics & Laser Technology, Vol. 119, 2019. doi.org/10.1016/j.optlastec.2019.105667

38-19 Subin Shrestha and Y. Kevin Chou, A numerical study on the keyhole formation during laser powder bed fusion process, Journal of Manufacturing Science and Engineering, Vol. 141, No. 10, 2019. doi.org/10.1115/1.4044100

34-19 Dae-Won Cho, Jin-Hyeong Park, and Hyeong-Soon Moon, A study on molten pool behavior in the one pulse one drop GMAW process using computational fluid dynamics, International Journal of Heat and Mass Transfer, Vol. 139, pp. 848-859, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.05.038

30-19 Mohamad Bayat, Sankhya Mohanty, and Jesper Henri 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.org/10.1016/j.ijheatmasstransfer.2019.05.003

29-19 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Daixiu Wei, Kenta Yamanaka, and Akihiko Chiba, Comprehensive study on mechanisms for grain morphology evolution and texture development in powder bed fusion with electron beam of Co–Cr–Mo alloy, Materialia, Vol. 6, 2019. doi.org/10.1016/j.mtla.2019.100346

28-19 Pareekshith Allu, Computational fluid dynamics modeling in additive manufacturing processes, The Minerals, Metals & Materials Society (TMS) 148th Annual Meeting & Exhibition, San Antonio, Texas, USA, March 10-14, 2019.

24-19 Simulation Software: Use, Advantages & Limitations, The Additive Manufacturing and Welding Magazine, Vol. 2, No. 2, 2019

22-19 Hunchul Jeong, Kyungbae Park, Sungjin Baek, and Jungho Cho, Thermal efficiency decision of variable polarity aluminum arc welding through molten pool analysis, International Journal of Heat and Mass Transfer, Vol. 138, pp. 729-737, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.04.089

07-19 Guangxi Zhao, Jun Du, Zhengying Wei, Ruwei Geng and Siyuan Xu, Numerical analysis of arc driving forces and temperature distribution in pulsed TIG welding, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 41, No. 60, 2019. doi.org/10.1007/s40430-018-1563-0

04-19 Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, Vol. 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

03-19 Dongsheng Wu, Anh Van Nguyen, Shinichi Tashiro, Xueming Hua and Manabu Tanaka, Elucidation of the weld pool convection and keyhole formation mechanism in the keyhold plasma arc welding, International Journal of Heat and Mass Transfer, Vol. 131, pp. 920-931, 2019. doi.org/10.1016/j.ijheatmasstransfer.2018.11.108

97-18 Wentao Yan, Ya Qian, Wenjun Ge, Stephen Lin, Wing Kam Liu, Feng Lin, Gregory J. Wagner, Meso-scale modeling of multiple-layer fabrication process in Selective Electron Beam Melting: Inter-layer/track voids formation, Materials & Design, 2018. doi.org/10.1016/j.matdes.2017.12.031

84-18 Bo Cheng, Xiaobai Li, Charles Tuffile, Alexander Ilin, Hannes Willeck and Udo Hartel, Multi-physics modeling of single track scanning in selective laser melting: Powder compaction effect, Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium, pp. 1887-1902, 2018.

81-18 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Daixiu Wei, Kenta Yamanaka and Akihiko 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, Vol. 26, pp. 202-214, 2019. doi.org/10.1016/j.addma.2018.12.002

77-18 Jun Du and Zhengying Wei, Numerical investigation of thermocapillary-induced deposited shape in fused-coating additive manufacturing process of aluminum alloy, Journal of Physics Communications, Vol. 2, No. 11, 2018. doi.org/10.1088/2399-6528/aaedc7

76-18 Yu Xiang, Shuzhe Zhang, Zhengying We, Junfeng Li, Pei Wei, Zhen Chen, Lixiang Yang and Lihao Jiang, Forming and defect analysis for single track scanning in selective laser melting of Ti6Al4V, Applied Physics A, 124:685, 2018. doi.org/10.1007/s00339-018-2056-9

74-18 Paree Allu, CFD simulations for laser welding of Al Alloys, Proceedings, Die Casting Congress & Exposition, Indianapolis, IN, October 15-17, 2018.

72-18 Hunchul Jeong, Kyungbae Park, Sungjin Baek, Dong-Yoon Kim, Moon-Jin Kang and Jungho Cho, Three-dimensional numerical analysis of weld pool in GMAW with fillet joint, International Journal of Precision Engineering and Manufacturing, Vol. 19, No. 8, pp. 1171-1177, 2018. doi.org/10.1007/s12541-018-0138-4

60-18 R.W. Geng, J. Du, Z.Y. Wei and G.X. Zhao, An adaptive-domain-growth method for phase field simulation of dendrite growth in arc preheated fused-coating additive manufacturing, IOP Conference Series: Journal of Physics: Conference Series 1063, 012077, 2018. doi.org/10.1088/1742-6596/1063/1/012077 (Available at http://iopscience.iop.org/article/10.1088/1742-6596/1063/1/012077/pdf and in shared drive)

59-18 Guangxi Zhao, Jun Du, Zhengying Wei, Ruwei Geng and Siyuan Xu, Coupling analysis of molten pool during fused coating process with arc preheating, IOP Conference Series: Journal of Physics: Conference Series 1063, 012076, 2018. doi.org/10.1088/1742-6596/1063/1/012076 (Available at http://iopscience.iop.org/article/10.1088/1742-6596/1063/1/012076/pdf and in shared drive)

58-18 Siyuan Xu, Zhengying Wei, Jun Du, Guangxi Zhao and Wei Liu, Numerical simulation and analysis of metal fused coating forming, IOP Conference Series: Journal of Physics: Conference Series 1063, 012075, 2018. doi.org/10.1088/1742-6596/1063/1/012075

55-18 Jason Cheon, Jin-Young Yoon, Cheolhee Kim and Suck-Joo Na, A study on transient flow characteristic in friction stir welding with realtime interface tracking by direct surface calculation, Journal of Materials Processing Tech., vol. 255, pp. 621-634, 2018.

54-18 V. Sukhotskiy, P. Vishnoi, I. H. Karampelas, S. Vader, Z. Vader, and E. P. Furlani, Magnetohydrodynamic drop-on-demand liquid metal additive manufacturing: System overview and modeling, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 155, 2018. doi.org/10.11159/ffhmt18.155

52-18 Michael Hilbinger, Claudia Stadelmann, Matthias List and Robert F. Singer, Temconex® – Kontinuierliche Pulverextrusion: Verbessertes Verständnis mit Hilfe der numerischen Simulation, Hochleistungsmetalle und Prozesse für den Leichtbau der Zukunft, Tagungsband 10. Ranshofener Leichtmetalltage, 13-14 Juni 2018, Linz, pp. 175-186, 2018.

38-18 Zhen Chen, Yu Xiang, Zhengying Wei, Pei Wei, Bingheng Lu, Lijuan Zhang and Jun Du, Thermal dynamic behavior during selective laser melting of K418 superalloy: numerical simulation and experimental verification, Applied Physics A, vol. 124, pp. 313, 2018. doi.org/10.1007/s00339-018-1737-8

19-18 Chenxiao Zhu, Jason Cheon, Xinhua Tang, Suck-Joo Na, and Haichao Cui, Molten pool behaviors and their influences on welding defects in narrow gap GMAW of 5083 Al-alloy, International Journal of Heat and Mass Transfer, vol. 126:A, pp.1206-1221, 2018. doi.org/10.1016/j.ijheatmasstransfer.2018.05.132

16-18 P. Schneider, V. Sukhotskiy, T. Siskar, L. Christie and I.H. Karampelas, Additive Manufacturing of Microfluidic Components via Wax Extrusion, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 162 – 165, 2018.

09-18 The Furlani Research Group, Magnetohydrodynamic Liquid Metal 3D Printing, Department of Chemical and Biological Engineering, © University at Buffalo, May 2018.

08-18 Benjamin Himmel, Dominik Rumschöttel and Wolfram Volk, Thermal process simulation of droplet based metal printing with aluminium, Production Engineering, March 2018 © German Academic Society for Production Engineering (WGP) 2018.

07-18 Yu-Che Wu, Cheng-Hung San, Chih-Hsiang Chang, Huey-Jiuan Lin, Raed Marwan, Shuhei Baba and Weng-Sing Hwang, Numerical modeling of melt-pool behavior in selective laser melting with random powder distribution and experimental validation, Journal of Materials Processing Tech. 254 (2018) 72–78.

60-17 Pei Wei, Zhengying Wei, Zhen Chen, Yuyang He and Jun Du, Thermal behavior in single track during selective laser melting of AlSi10Mg powder, Applied Physics A: Materials Science & Processing, 123:604, 2017. doi.org/10.1007/z00339-017-1194-9

51-17 Koichi Ishizaka, Keijiro Saitoh, Eisaku Ito, Masanori Yuri, and Junichiro Masada, Key Technologies for 1700°C Class Ultra High Temperature Gas Turbine, Mitsubishi Heavy Industries Technical Review, vol. 54, no. 3, 2017.

49-17 Yu-Che Wu, Weng-Sing Hwang, Cheng-Hung San, Chih-Hsiang Chang and Huey-Jiuan Lin, Parametric study of surface morphology for selective laser melting on Ti6Al4V powder bed with numerical and experimental methods, International Journal of Material Forming, © Springer-Verlag France SAS, part of Springer Nature 2017. doi.org/10.1007/s12289-017-1391-2.

37-17 V. Sukhotskiy, I. H. Karampelas, G. Garg, A. Verma, M. Tong, S. Vader, Z. Vader, and E. P. Furlani, Magnetohydrodynamic Drop-on-Demand Liquid Metal 3D Printing, Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference

15-17 I.H. Karampelas, S. Vader, Z. Vader, V. Sukhotskiy, A. Verma, G. Garg, M. Tong and E.P. Furlani, Drop-on-Demand 3D Metal Printing, Informatics, Electronics and Microsystems TechConnect Briefs 2017, Vol. 4

14-17 Jason Cheon and Suck-Joo Na, Prediction of welding residual stress with real-time phase transformation by CFD thermal analysis, International Journal of Mechanical Sciences 131–132 (2017) 37–51.

91-16 Y. S. Lee and D. F. Farson, Surface tension-powered build dimension control in laser additive manufacturing process, Int J Adv Manuf Technol (2016) 85:1035–1044, doi.org/10.1007/s00170-015-7974-5.

84-16 Runqi Lin, Hui-ping Wang, Fenggui Lu, Joshua Solomon, Blair E. Carlson, Numerical study of keyhole dynamics and keyhole-induced porosity formation in remote laser welding of Al alloys, International Journal of Heat and Mass Transfer 108 (2017) 244–256, Available online December 2016.

68-16 Dongsheng Wu, Xueming Hua, Dingjian Ye and Fang Li, Understanding of humping formation and suppression mechanisms using the numerical simulation, International Journal of Heat and Mass Transfer, Volume 104, January 2017, Pages 634–643, Published online 2016.

39-16 Chien-Hsun Wang, Ho-Lin Tsai, Yu-Che Wu and Weng-Sing Hwang, Investigation of molten metal droplet deposition and solidification for 3D printing techniques, IOP Publishing, J. Micromech. Microeng. 26 (2016) 095012 (14pp), doi: 10.1088/0960-1317/26/9/095012, July 8, 2016

29-16 Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Advances in Magnetohydrodynamic Liquid Metal Jet Printing, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

26-16 Y.S. Lee and W. Zhang, Modeling of heat transfer, fluid flow and solidification microstructure of nickel-base superalloy fabricated by laser powder bed fusion, S2214-8604(16)30087-2, doi.org/10.1016/j.addma.2016.05.003, ADDMA 86.

123-15 Koji Tsukimoto, Masashi Kitamura, Shuji Tanigawa, Sachio Shimohata, and Masahiko Mega, Laser welding repair for single crystal blades, Proceedings of International Gas Turbine Congress, pp. 1354-1358, 2015.

116-15 Yousub Lee, Simulation of Laser Additive Manufacturing and its Applications, Ph.D. Thesis: Graduate Program in Welding Engineering, The Ohio State University, 2015, Copyright by Yousub Lee 2015

103-15 Ligang Wu, Jason Cheon, Degala Venkata Kiran, and Suck-Joo Na, CFD Simulations of GMA Welding of Horizontal Fillet Joints based on Coordinate Rotation of Arc Models, Journal of Materials Processing Technology, Available online December 29, 2015

96-15 Jason Cheon, Degala Venkata Kiran, and Suck-Joo Na, Thermal metallurgical analysis of GMA welded AH36 steel using CFD – FEM framework, Materials & Design, Volume 91, February 5 2016, Pages 230-241, published online November 2015

86-15 Yousub Lee and Dave F. Farson, Simulation of transport phenomena and melt pool shape for multiple layer additive manufacturing, J. Laser Appl. 28, 012006 (2016). doi: 10.2351/1.4935711, published online 2015.

63-15 Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Magnetohydrodynamic Liquid Metal Jet Printing, TechConnect World Innovation Conference & Expo, Washington, D.C., June 14-17, 2015

46-15 Adwaith Gupta, 3D Printing Multi-Material, Single Printhead Simulation, Advanced Qualification of Additive Manufacturing Materials Workshop, July 20 – 21, 2015, Santa Fe, NM

25-15 Dae-Won Cho and Suck-Joo Na, Molten pool behaviors for second pass V-groove GMAW, International Journal of Heat and Mass Transfer 88 (2015) 945–956.

21-15 Jungho Cho, Dave F. Farson, Kendall J. Hollis and John O. Milewski, Numerical analysis of weld pool oscillation in laser welding, Journal of Mechanical Science and Technology 29 (4) (2015) 1715~1722, www.springerlink.com/content/1738-494x, doi.org/10.1007/s12206-015-0344-2.

82-14 Yousub Lee, Mark Nordin, Sudarsanam Suresh Babu, and Dave F. Farson, Effect of Fluid Convection on Dendrite Arm Spacing in Laser Deposition, Metallurgical and Materials Transactions B, August 2014, Volume 45, Issue 4, pp 1520-1529

59-14 Y.S. Lee, M. Nordin, S.S. Babu, and D.F. Farson, Influence of Fluid Convection on Weld Pool Formation in Laser Cladding, Welding Research/ August 2014, VOL. 93

18-14 L.J. Zhang, J.X. Zhang, A. Gumenyuk, M. Rethmeier, and S.J. Na, Numerical simulation of full penetration laser welding of thick steel plate with high power high brightness laser, Journal of Materials Processing Technology (2014), doi.org/10.1016/j.jmatprotec.2014.03.016.

36-13 Dae-Won Cho,Woo-Hyun Song, Min-Hyun Cho, and Suck-Joo Na, Analysis of Submerged Arc Welding Process by Three-Dimensional Computational Fluid Dynamics Simulations, Journal of Materials Processing Technology, 2013. doi.org/10.1016/j.jmatprotec.2013.06.017

12-13 D.W. Cho, S.J. Na, M.H. Cho, J.S. Lee, A study on V-groove GMAW for various welding positions, Journal of Materials Processing Technology, April 2013, doi.org/10.1016/j.jmatprotec.2013.02.015.

01-13 Dae-Won Cho & Suck-Joo Na & Min-Hyun Cho & Jong-Sub Lee, Simulations of weld pool dynamics in V-groove GTA and GMA welding, Weld World, doi.org/10.1007/s40194-012-0017-z, © International Institute of Welding 2013.

63-12 D.W. Cho, S.H. Lee, S.J. Na, Characterization of welding arc and weld pool formation in vacuum gas hollow tungsten arc welding, Journal of Materials Processing Technology, doi.org/10.1016/j.jmatprotec.2012.09.024, September 2012.

77-10 Lim, Y. C.; Yu, X.; Cho, J. H.; et al., Effect of magnetic stirring on grain structure refinement Part 1-Autogenous nickel alloy welds, Science and Technology of Welding and Joining, Volume: 15 Issue: 7, Pages: 583-589, doi.org/10.1179/136217110X12720264008277, October 2010

18-10 K Saida, H Ohnishi, K Nishimoto, Fluxless laser brazing of aluminium alloy to galvanized steel using a tandem beam–dissimilar laser brazing of aluminium alloy and steels, Welding International, 2010

58-09 Cho, Jung-Ho; Farson, Dave F.; Milewski, John O.; et al., Weld pool flows during initial stages of keyhole formation in laser welding, Journal of Physics D-Applied Physics, Volume: 42 Issue: 17 Article Number: 175502 ; doi.org/10.1088/0022-3727/42/17/175502, September 2009

57-09 Lim, Y. C.; Farson, D. F.; Cho, M. H.; et al., Stationary GMAW-P weld metal deposit spreading, Science and Technology of Welding and Joining, Volume: 14 Issue: 7 ;Pages: 626-635, doi.org/10.1179/136217109X441173, October 2009

1-09 J.-H. Cho and S.-J. Na, Three-Dimensional Analysis of Molten Pool in GMA-Laser Hybrid Welding, Welding Journal, February 2009, Vol. 88

52-07 Huey-Jiuan Lin and Wei-Kuo Chang, Design of a sheet forming apparatus for overflow fusion process by numerical simulation, Journal of Non-Crystalline Solids 353 (2007) 2817–2825.

50-07 Cho, Min Hyun; Farson, Dave F., Understanding bead hump formation in gas metal arc welding using a numerical simulation, Metallurgical and Mateials Transactions B-Process Metallurgy and Materials Processing Science, Volume: 38, Issue: 2, Pages: 305-319, doi.org/10.1007/s11663-007-9034-5, April 2007

49-07 Cho, M. H.; Farson, D. F., Simulation study of a hybrid process for the prevention of weld bead hump formation, Welding Journal Volume: 86, Issue: 9, Pages: 253S-262S, September 2007

48-07 Cho, M. H.; Farson, D. F.; Lim, Y. C.; et al., Hybrid laser/arc welding process for controlling bead profile, Science and Technology of Welding and Joining, Volume: 12 Issue: 8, Pages: 677-688, doi.org/10.1179/174329307X236878, November 2007

47-07 Min Hyun Cho, Dave F. Farson, Understanding Bead Hump Formation in Gas Metal Arc Welding Using a Numerical Simulation, Metallurgical and Materials Transactions B, Volume 38, Issue 2, pp 305-319, April 2007

36-06 Cho, M. H.; Lim, Y. C.; Farson, D. F., Simulation of weld pool dynamics in the stationary pulsed gas metal arc welding process and final weld shape, Welding Journal, Volume: 85 Issue: 12, Pages: 271S-283S, December 2006

Aluminum Integral Foam Molding Process

Aluminum Integral Foam Molding Process

This application note was contributed by Johannes Hartmann and Vera Jüchter, Department of Materials Science, Chair of Metals Science and Technology, University of Erlangen-Nuremberg

알루미늄 폼은 우수한 댐핑 및 높은 에너지 흡수율 및 굴곡 강성과 같은 예외적인 특성을 보여줍니다[1]. 강성은 특히 하중 지지 및 경량 구조에 사용하기에 특히 매력적입니다. 중량별 강성을 높이고 보다 우수한 하중 전달을 위해 알 Aluminum Foam Sandwiches (AFS)와 같은 컴팩트한 특성이 필요합니다 [2].

Erlangen-Nuremberg 대학의 금속 공학과 기술 위원장은 알루미늄 발포 특성을 점차적으로 생산하기 위해 다이캐스팅 공정인 Integral Foam Molding 개발하였습니다(그림 1 참조). 이 공정은 폴리머의 사출 성형으로 개발되었으며 따라서 컴팩트한 층을 가진 복잡한 폼을 비용 효율적으로 대량 생산에 적합합니다. 이 노트에 설명 된 시뮬레이션 기법은 프로세스 매개 변수를 선택하는데 도움을 주기 위한 모델링프로세스를 확인할 수 있습니다.

Figure 1. Cross section of an aluminum integral foam with a compact skin, a transition region with decreasing relative density and smaller pores, as well as a foamed core.

Aluminum Integral Foam Molding Technology

일정량의 발포제 (수소화 마그네슘, MgH2)가 러너 시스템에 배치되고 샷 챔버는 알루미늄 용융물로 채워진다 (공정은 그림 2에 묘사되어 있으며, 공정은 [3]에 자세히 설명되어있다). 피스톤이 진행됨에 따라, 분말은 난류 방식으로 주형에 이송된다. 기술 변형 “고압 일체형 폼 몰딩 (HP-IFM)”의 경우 표준 다이캐스팅 공정에서 알 수 있듯이 이 부품은 주변의 높은 압력에서 완전히 채워져 우수한 표면 품질을 보장합니다. 템퍼링된 금형 표면에서 시작하여 용융물은 일체형으로 고형화되기 시작합니다. 몇 밀리 초가 지나면 금형은 코어 풀러 시스템 위에 열리고 부피는 국부적으로 증가하고 압력은 감소하여 열분해 및 수소화 마그네슘 입자의 수소 방출로 인해 여전히 반고체 내부 영역에서 기공 성장을 시작합니다. 모든 발포제 입자는 이웃하는 공극의 역압에 의해 멈추어 질 때까지 공극의 성장을 지속합니다. 발포된 입자의 벽은 알루미늄 합금의 응고된 입자에 의해 안정화가 되며 이를 endogenous stabilization이라고 합니다[4].

Figure 2. Schematic process cycle of “High Pressure Integral Foam Molding (HP-IFM)” of aluminum.

주조 부품의 전체 부피에서 균일한 형태에 대한 전제조건은 분해 순간의 양호한 입자분포입니다. 또한, 발포제 유입시의 용융물의 온도는 수소화 마그네슘의 분해를 결정하며 (그림 3 참조), 게다가 발포시 solid phase의 양을 결정한다. 그러나 고상의 양이 너무 많으면 기공의 강성이 증가하고 현상 기공의 구형화를 방해하여 구조가 파괴된다 [2].

Microcellular Aluminum Integral Foams – Approaching the Process Limits

일체형 발포 성형 공정시뮬레이션은 새로운 부품 설계의 몰드 충진 특성을 조사하는 데 도움이 될 뿐만 아니라 입자 침투도 예측하고 비용을 절약할 수 있게 발포 공정 조건을 결정할 수 있는 강력한 도구입니다. 현재 연구의 목표는 다공성 수준을 일정하게 유지하면서 기공 크기를 줄이는 것입니다. 전산 유체 역학 (CFD) 시뮬레이션은 가능한 한 현재의 프로세스 한계에 가깝게 접근할 수 있습니다. 발포 형태의 개선은 기계적 물성에서 균질 한 구조를 유도 할뿐만 아니라 기계적 성질에 의해 더 얇은 부품의 생산이 가능할 것입니다. 이 목적은 용융물 내에서의 높은 입자 분포 밀도와 동시에 응집 현상의 감소와 함께 완전히 안정된 기공 성장에 의해서만 달성 될 수 있다.

Figure 3. Schematic curves of decomposition of magnesium hydride as a function of the melt temperature, calculated by the Johnson-Mehl-Avrami approach [2]

Figure 4. Adjustment of heat transfer by comparisons of a real solidification curve (black) to the growth rate of the solidified skin in simulation (red).

Adapting the Simulation Parameters to Practical Integral Foam Molding Experiments

입자 거동이나 온도장에 대한 신뢰성 있는 예측을 위한 CFD 시뮬레이션을 사용할 수 있으려면 실제 실험과 일치하도록 매개 변수를 결정해야 합니다. 이를 위해, 30-130 ms의 지연 시간을 갖는 일체형 발포 부품을 제작하였으며 성형 팽창 및 기공 성장 개시 순간에 고상분율 때문에 발포 형성이 불가능한 다른 밀도의 형상을 만들었습니다. 열 전달 계수 (완전한 액체 용융물과 완전 응고된 용융물)를 변화시켜 합금 AlSi9Cu3 (Fe)의 주조 사이클을 시뮬레이션하면 응고 곡선을 적용할 수 있습니다. 이러한 목표를 달성하기 위해 시뮬레이션을 피스톤 이동이 시작되기 전에 실제 온도분포를 묘사해야 합니다. 온도는 배치된 열에 의해 숏 챔버에서 국부적으로 측정되었으며 시뮬레이션 내 실제 데이터와 잘 일치하여 성공적으로 묘사 될 수 있었습니다. 금형 충진 중에 금형 표면에서 온도 측정을 참조 할 수도 있습니다. 시간 경과에 따른 그 변화는 시뮬레이션 결과와 잘 일치합니다.

표면장력이나 응고 항력계수와 같은 용융의 유동을 정의하는 추가 매개 변수 단계에서는 다른 설정과 시뮬레이션을 비교하여 조정됩니다. 시뮬레이션 내에서 용융물의 흐름이 실제 시험과 일치하는 즉시 매개 변수가 설정됩니다

Figure 5. Adjustment of melt flow defining parameters such as the surface tension by comparisons of real experiments (left) to simulations (right)

냉각 및 용해 흐름 특성을 정의한 후 입자의 유입을 시뮬레이션 합니다. 입자 / 유체 의 상호 작용에 대한 시뮬레이션을 조정하기 위해 매개 변수계수의 X 선 샘플과 비교가 되며 구리선 입자에서는 수산화 마그네슘보다 높은 함량 입자가 적용됩니다. (그림 6 참조). 시뮬레이션 결과는 실험과 매우 잘 어울리므로 프로세스 매개 변수의 함수로서 입자 분포의 신뢰할 수 있습니다.

Figure 6. Adjustment of parameters influencing particle/melt-interactions by comparisons of x-rayed samples left); produced by the entrainment of copper particles) to simulations (right)

Conclusion

전체적으로 FLOW-3D는 실제 생산 전에 새로운 부품 제조의 잠재적 결함을 조사하는 중요한 수단이 될 수 있다는 것을 증명할 수 있었습니다. 이러한 방식으로, 차가운 흐름 또는 데드 존이 없는 성공적인 충전 및 발포제 분포가 보장 될 수 있다. 또한, 예상되는 온도 필드의 정확한 묘사로, 수소화 마그네슘의 분해 특성 및 기공형성을 예측할 수 있습니다. 이는 일체형 폼 구조와 관련하여 고객의 요구를 충족시키기 위한 공정 변수를 정의 할 수 있는 가능성을 제공합니다

¹ Criterion is the solid phase fraction where the shear strength and therefore the resistance to pore evolution increases drastically.

References

[1] C. Körner, R. F. Singer, Adv. Eng. Mater. 2000, 2 (4), pp. 159-165.
[2] C. Körner, in Integral Foam Molding of Light Metals – Technology, Foam Physics and Foam Simulation, Springer, Berlin, Heidelberg, Germany 2008.
[3] H. Wiehler, C. Körner, R. F. Singer, Adv. Eng. Mater. 2008, 10 (3), pp. 171-178.
[4] J. Hartmann, A. Trepper, C. Körner, Adv. Eng. Mater. 2011, 13 (11), pp. 1050-1055.

Learn more about the versatility and power of modeling metal casting processes with FLOW-3D Cast>

CFD에 대해서

What You Should Know About CFD Modeling when Selecting a CFD Package

유체 흐름 및 열 전달 해석용 소프트웨어 패키지에는 여러 형태가 있습니다. 물리적 근사와 수치 해법의 기법이 패키지마다 크게 다르기 때문에 적절한 패키지를 선택하는 것은 매우 어렵습니다. 다음 설명에서는 열유동 시뮬레이션 소프트웨어를 선택할 때 고려해야 할 중요한 몇 가지를 소개합니다.

Software packages for fluid flow and heat transfer analysis come in many forms. These packages differ greatly in their physical approximations and numerical solution techniques, which makes the selection of a suitable package a challenging proposition. The following discussion covers some important items to consider when choosing flow simulation software.

Meshing and Geometry

유한 요소 또는 “body-fitted coordinates”를 채용하고 있는 수치해석 방법은 유체 영역의 기하학적 형상에 적합한 격자를 생성해야 합니다. 정확한 수치 근사치를 얻기 위해 허용 할 수 있는 요소 크기 및 형상에서 이러한 격자를 생성하는 것은 매우 중요한 작업입니다.

복잡한 경우에는 이와 같은 방법으로 격자를 생성하면 며칠 또는 몇 주가 걸릴 수 있습니다. 어떤 프로그램은 사각형의 격자 요소만을 사용함으로써 문제를 해결하려고 하지만, 그럴 경우에는 경계부분에 계단이 생기고 흐름과 열전달 특성이 달라지는 문제에 직면하게 됩니다.

FLOW-3D는 FAVOR™(면적율 / 부피 비율)법 을 사용하여 지오메트리의 특성을 원활하게 포함하므로써, 간단한 사각형 격자만으로도 두 문제를 해결할 수 있습니다. 또한, 간단하고 강력한 솔리드 모델러가 FLOW-3D 패키지에 기본 포함되어 있으며, CAD 프로그램에서 생성한 기하형상 데이터를 가져올 수 있습니다.

Solution methods that employ finite-element or “body-fitted coordinates” require the generation of a solution grid that conforms to the geometry of the flow region. It is a non-trivial task to generate these grids with acceptable element sizes and shapes for accurate numerical approximations. In complicated cases this type of grid generation may consume days or even weeks of effort. Some programs attemptto eliminate this generation problem by using only rectangular grid elements, but then they must contend with “stair-step” boundaries that alter flow and heat-transfer properties. FLOW-3D solves both problems by using easy-to-generate rectangular grids in which geometric features are smoothly embedded using the FAVOR™ (fractional area/volume) method. A simple and powerful solids modeler is packaged with FLOW-3D or users may import geometric data from a CAD program.

Momentum Equation vs. Approximate Flow Models

유체 운동량의 정확한 처리가 중요한 몇 가지 이유가 있습니다. 첫째, 이것은 복잡한 기하학적 형상에서 유체가 어떻게 흐르는지를 예측하는 유일한 방법입니다. 둘째, 액체에 의하여 걸린 동적인 힘(압력)은 운동량에서만 계산할 수 있습니다. 마지막으로, 열 에너지의 대류 수송을 계산하려면 다른 유체 입자 및 경계에 대한 개별 유체 입자의 상대적인 움직임을 정확하게 파악하는 것이 필요합니다. 이것은 운동량의 정확한 처리를 의미합니다. 운동량 보존을 대충 근사하기만 한 CFD 모델은 FLOW-3D에서는 사용되지 않습니다. 이러한 모델은 현실적인 유체 구성 및 온도 분포 예측에 사용할 수 없기 때문입니다.

An accurate treatment of fluid momentum is important for several reasons. First, it is the only way to predict how fluid will flow through complicated geometry. Second, the dynamic forces (i.e., pressures) exerted by the fluid can only be computed from momentum considerations. Finally, to compute the convective transport of thermal energy, it is necessary to have an accurate picture of how individual fluid particles move in relation to other fluid particles and confining boundaries. This implies an accurate treatment of momentum. Simplified flow models that only crudely approximate the conservation of momentum are not used in FLOW-3D because they cannot be used to predict realistic fluid configurations and temperature distributions.

Liquid-Solid Heat Transfer Area

액체와 고체 사이 (금속 주형 등)의 열전달은 경계면 면적의 정확한 추정이 필요합니다. 경계가 계단 모양으로 되어 있는 경우, 보통 이 면적이 크게 추정됩니다. 예를 들어, 실린더의 표면적은 약 27 %정도 크게 추정됩니다. FLOW-3D의 경우 정확한 경계면 면적은 FAVOR™법에 따라 FLOW-3D 전처리기에서 컨트롤 볼륨마다 자동으로 계산됩니다.

Heat transfer between a liquid and a solid (e.g., metal-to-mold) requires an accurate estimate of the interfacial area. Stair-step boundaries over-estimate this area; for example, the surface area of a cylinder would be over-estimated by a factor of 27%. Accurate interfacial areas are automatically computed by the FAVOR™ method for each control volume in the FLOW-3D pre-processor.

Control Volume Effects on Liquid-Solid Heat Transfer

컨트롤 볼륨의 크기가 액체와 고체 사이에서 교환되는 열 비율과 양에 영향을 줄 수 있습니다. 이것은 열이 액체와 고체의 경계면을 포함하는 컨트롤 볼륨을 흐를 필요가 있기 때문입니다. FLOW-3D는 액체와 고체의 경계면에 걸쳐 열 전달률을 계산할 때 컨트롤 볼륨의 크기와 전도율이 고려됩니다.

The size of control volumes can influence the rate and amount of heat exchanged between a liquid and solid because heat must also flow in the control volumes containing the liquid/solid interface. In FLOW-3D control volume sizes and their conductivities are accounted for when computing heat transfer rates across liquid-solid interfaces.

Implicitness and Accuracy

비선형 방정식과 결합 방정식의 Implicit 방법은 반복 될 때마다 under-relaxation 특성을 갖는 반복적 해법이 필요합니다. 이 동작은 상황에 따라 심각한 오류 (또는 수렴 속도의 급격한 하락)가 발생할 수 있습니다. 예를 들어, 비율이 큰 컨트롤 볼륨을 사용하는 경우나, 실제로는 중요하지 않은 효과를 예상하고 암시적인 해법을 사용하는 경우 등입니다. FLOW-3D는 가능한 명시적인 수치해법이 사용되고 있습니다. 이것은 필요한 계산량이 적고, 수치 안정성의 요구 사항이 요구된 정밀도에 상응하기 때문입니다. 자세한 내용은 “암시적인 수치해법과 명시적인 수치해법“을 참조하십시오.

Implicit methods for nonlinear and coupled equations require iterative solution methods that have the character of an under-relaxation in each iteration. This behavior can cause significant errors (or very slow convergence) in some situations, for example, when using control volumes with large aspect ratios or when the implicitness is used in anticipation of an effect that is not actually significant. In FLOW-3D explicit numerical methods are used whenever possible because they require less computational effort, and their numerical stability requirements are equivalent to accuracy requirements. Read more in the Implicit vs. Explicit Numerical Methods article.

Implicit Numerical Methods For Convective Transport

모든 크기의 타임 스텝 크기를 계산에 사용할 수 있는 암시적인 수치 기법은 CPU 시간을 줄이기 위해 많이 사용되는 방법입니다. 불행하게도, 이 방법은 대류 현상 해석에 대해 정확하지 않습니다. 암시적인 해법은 근사 방정식에 확산 효과를 도입함으로써 시간 단계의 독립성을 획득합니다. 수치 확산을 물리적 확산 (열전도 등)에 추가해도 확산율이 변경될 뿐이므로 심각한 문제가 되지 않을 수 있습니다. 그러나 수치 확산(발산)을 대류 과정에 추가하면 모델링 대상의 물리 현상의 특성은 완전히 다르게 됩니다. FLOW-3D는 시간의 정확한 근사치를 보장하기 위해 프로그램에 의해 time step이 자동으로 제어됩니다.

Implicit numerical techniques that allow arbitrarily large time-step sizes to be used in calculations are a popular way to reduce CPU time requirements. Unfortunately, these methods are not accurate for convective processes. Implicit methods gain their time-step independence by introducing diffusive effects into the approximating equations. The addition of numerical diffusion to physical diffusion, e.g., to heat conduction, may not cause a serious problem as it only modifies the diffusion rate. However, adding numerical diffusion to convective processes completely changes the character of the physical phenomena being modeled. In FLOW-3D time steps are automatically controlled by the program to ensure time-accurate approximations.

Relaxation and Convergence Parameters

암시적으로 근사치를 사용하는 수치법은 하나 이상의 수렴 및 완화(이완)의 매개 변수를 선택해야 합니다. 이러한 매개 변수를 신중하게 선택하지 않으면 발산하거나 수렴에 시간이 걸리는 경우가 있습니다. FLOW-3D를 융합하는 매개 변수와 완화(이완) 매개 변수를 하나씩만 사용하여 두 매개 변수는 프로그램에 의해 동적으로 선택됩니다. 수치 해법을 제어하는 매개 변수를 사용자가 설정할 필요는 없습니다.

Numerical methods that use implicit approximations also require the selection of one or more convergence and relaxation parameters. Making poor choices for these parameters can lead to either divergences or slow convergence rates. Only one convergence and one relaxation parameter are used in FLOW-3D, and both parameters are dynamically selected by the program. Users are not required to set any parameters controlling the numerical solver.

Free-Surface Tracking

액체와 기체의 경계면 (자유 표면 등)의 모델링에 사용되는 방법은 두 가지가 있습니다. 하나는 액체, 기체 두 영역의 흐름을 계산하고 경계면을 유체 밀도의 급격한 변화로 처리하는 방법입니다.

일반적으로 밀도의 불연속은 고차 수치 근사를 사용하여 모델링됩니다. 불행하게도 이 프로세스는 소수의 격자 셀에서 경계면이 평탄화되고, 이러한 경계면에 보통 존재하는 유체흐름의 접선 속도의 급격한 변화는 고려되지 않습니다.

기체가 계산 영역에 들어가는 액체로 대체되는 경우에는 이 방법에는 기체의 출구 포트 또는 출구 싱크도 보충 할 필요가 있습니다. 또한 이러한 방법은 일반적으로 유체의 비압축성를 충족하기 위해 더 많은 노력이 필요합니다. 이것이 발생하는 기체 영역에 거의 균일 한 압력 조정이 필요하며, 이를 통해 계산 수렴 시간이 소요되기 때문입니다.

FLOW-3D는 VOF (Volume-of-Fluid) 법 이라는 독창적인 방법이 사용되고 있습니다. 이것은 진정한 3 차원 경계면 추적 방식으로, 경계면을 3 차원 인터페이스로 추적하는 체계입니다. 또한 옵션의 표면 장력을 포함한 일반적인 접선 응력 경계 조건은 경계면에 적용됩니다. 기체 영역은 모델에 포함하도록 사용자가 요청하지 않는 한 계산되지 않습니다.

There are two methods used to model liquid-gas interfaces (i.e., free surfaces). One of these is to compute flow in both the liquid and gas regions and to treat the interface as a sharp change in fluid density. Typically, the density discontinuity is modeled using higher-order numerical approximations. Unfortunately, this treatment allows the interface to smooth out over a few grid cells and does not account for a corresponding sharp change in tangential flow velocity that generally exists at such interfaces. This technique must also be supplemented with escape ports or sinks for the gas if it is to be replaced by liquid entering a computational region. Further, such methods must typically work harder to satisfy the incompressibility of the fluids. This happens because gas regions must have nearly uniform pressure adjustments which tend to slow down the solution convergence rate. A different technique, the Volume-of-Fluid (VOF) method, is used in FLOW-3D. This is a true three-dimensional interface tracking scheme in which the interface is closely maintained as a step discontinuity. Moreover, normal and tangential stress boundary conditions, including optional surface tension forces, are applied at the interface. Gas regions are not computed unless the user requests these regions to be included in the model.

이전 페이지

다음 페이지

FLOW-3D CAST Suites

FLOW-3D CAST v5 comes in Suites of relevant casting processes:

HIGH PRESSURE DIE CASTING SUITE

Process Workspace

High Pressure Die Casting

Features

Thermal Die Cycling
– Cooling/heating channels
– Spray cooling
Filling
– Shot sleeve with Plunger
– Shot motion
– Ladles, stoppers
– Venting efficiency
– PQ^2 analysis
– HPDC machine database
Solidification
– Squeeze pins
Cooling

PERMANENT MOLD CASTING SUITE

Process Workspaces

Permanent Mold Casting
Low Pressure Die Casting
Tilt Pour Casting

Features

Thermal Die Cycling
– Cooling/heating channels
Filling
– Tilt pouring
Solidification
– Squeeze pins
Cooling

SAND CASTING SUITE

Process Workspaces

Sand Casting
Low Pressure Sand Casting

Features

Filling
– Permeable molds
– Moisture evaporation in molds
– Gas generation in cores
– Ladle model
Solidification
– Exothermic sleeves
– Chills
– Cast iron solidification
Cooling

LOST FOAM CASTING SUITE

Process Workspaces

Lost Foam
Sand Casting
Low Pressure Sand Casting

Features

Filling
– Permeable molds
– Moisture evaporation in molds
– Gas generation in cores
– Ladle model
– Lost foam pattern evaporation models (Fast model and Full model)
– Lost foam defect prediction
Solidification
– Exothermic sleeves
– Chills
– Cast iron solidification
Cooling

ALL SUITES INCLUDE THESE CORE FEATURES:

Solver Engine

TruVOF – The most accurate filling simulation tool in the industry
Heat transfer and solidification
Shrinkage – Rapid Shrinkage model and Shrinkage with flow model
Temperature dependent properties
Multi-block meshing including conforming meshes
Turbulence models
Non-Newtonian viscosity (shear thinning/thickening, thixotropic)
Flow tracers
Active Simulation Control with Global Conditions
Surface tension model
Thermal stress analysis with warpage
General moving geometry w/6 DOF

FlowSight

Multi-case analysis
Porosity analysis tool

Defect Prediction Tools

Gas entrainment model
Thermal Modulus output
Hot Spot identification
Micro and macro porosity prediction
Surface defect prediction
Shrinkage
Cavitation and Cavitation Potential
Particle models (Inclusion modeling, collapsed bubble tracking)

User Conveniences

Process-oriented workspaces
Configurable Simulation Monitor
Metal and solid material databases
Heat transfer database
Filter database
Remote solving queues
Quick Analyze/Display tool

Computational Analysis of Drop Formation and Detachment

Introduction and Problem Statement

신속, 반복, 작은 물방울의 생성 및 증착, 작은 형상의 프린팅 또는 패터닝 (예 : l = 10-3-1 mm), 스프레이로 균일한 두께의 박막 형성은 다양한 산업에 매우 중요합니다(1-5). 액체 이동과 액적 형성 / 증착 공정은 복잡한 자유 표면 흐름, 자연적인 모세관운동 형성, thinning, pinch-off를 수반한다 (1-5). 단순한 뉴턴 및 비탄성 유체에 대해 액적 생성 및 액적 이동을 분석하기위한 실험적, 이론적 및 1 차원 시뮬레이션 연구가 진행되었지만 프린팅 또는 패터닝에 대한 기계론적인 이해는 여전히 과제로 남아 있습니다. 현재의 계산에 대한 주된 목표는 뉴턴 유체의 pinch-off에 대한 기계론적 이해를 얻기 위해 FLOW-3D에 내장된 VOF(volume-of-fluid) 접근법으로 시험하는 것입니다. 전산해석은 모세관, 관성, 점성 응력의 복잡한 상호 작용을 포착하여 자기유사 모세관의 thinning and pinch-off를 결정합니다. 뉴턴 유체의 물방울 형성 및 분리현상은 전산해석으로부터 얻어진 자기유사 모세관현상 이론, 보편적인 축소화 기법인 1D 시뮬레이션 (1-7)과 실험 (1, 2, 8-12)을 이용하여 설명될 수 있음을 보여준다. 이러한 우리가 진행한 원형흐름 시뮬레이션은 유한한 시간의 비선형 역학, 위성 낙하현상, 복잡한 형상의 프린팅과 같이 어려운 전산해석의 기반이 될 것 입니다.

방울 형성의 전산 분석 — 그림 1 : FLOW-3D를 사용하여 시뮬레이션 한 저점도 유체의 드롭 형성 및 분리에 대한 전산해석 : (a) 5개의 저점도 유체에 대한 물방울의 necking에 대한 반경이 시간변화에 따라 표시됩니다. 물방울 necking의 반지름이 오른쪽에서 왼쪽으로 시간에 따른 전개를 보여줍니다. 마찬가지로 스냅 샷은 necking의 반경이 오른쪽에서 왼쪽으로 줄어듭니다. 속도의 크기 (단위 : cm/s) 와 화살표의 방향에 대한 컬러 맵을 사용하면 변형장을 결정할 수 있으며 Fluid 5 (표 1 참조)의 경우에는 순식간에 신장이됩니다. 이미지 II에 캡처 된 pinch-off 하기 전에 형성된 원추형 necking은 실험을 통해 얻은 necking 모양과 유사합니다.

Modeling Approach and Parameter Space

표면 장력 및 중력 모델을 적용한 FLOW-3D 에서 균일한 메쉬 크기를 사용하여 노즐에서 드롭 형성 및 분리에 대한 시뮬레이션을 수행하였습니다. 유한 체적의 유체를 떨어뜨리거나 분리하는 일은 물방울의 성장과 드롭, 노즐에 연결되는 모세관 현상, 관성, 점도 및 중력에 대한 상호 작용을 수반합니다. 시뮬레이션에서 스테인레스 강 노즐 ( ${{D} _ {0}} = 2 {{R} _ {0}} = 1.7 \, \ text {mm}$ ) 에서 유한 체적의 뉴턴 유체가 발생합니다. 표면 장력이 중력을 겪으면 새로 형성된 액적 분리가 발생합니다 ( $mg> 2 \ pi \ sigma {{R} _ {0}}$ ). 시뮬레이션은 유체점도의 영향을 설명하기 위해 두 그룹으로 나누어져 있습니다: 저점도 유체 (글리세롤 함량이 40 % 미만인 물과 글리세롤/물 혼합물) 및 점도가 높은 유체 (예 : 글리세롤과 글리세롤/물 혼합물 점도 > 100x 물 점도). 두 그룹의 유체 특성은 각각 표 1과 2에 나와 있습니다.

계산 분석 드롭 형성 저점도

그림 2 : FLOW-3D를 사용하여 시뮬레이션 한 저점도 유체의 드롭형성 및 분리에 대한 전산 해석 : 반경 플롯에서 4개의 고점도 뉴톤유체에 대해 necking 반경을 시간변화에 따라 표시합니다. 낙하 분리 중 모세관 현상이 스냅 샷으로 표시됩니다. 컬러 맵은 Fluid 8의 속도 크기 (단위 : cm/s)의 변화를 포착합니다 (표2 참조). 화살표는 성장하는 물방울과 얇아지는 물방울내에서 흐름방향을 나타냅니다. FLOW-3D 시뮬레이션으로 얻은 necking 모양은 고점도의 뉴턴유체에 대한 특징인 원통형 유체요소로 이어집니다.

<표 1 : FLOW-3D를 사용하여 시뮬레이션 된 저점도 유체의 특성>

Fluid Property	Fluid 1	Fluid 2	Fluid 3	Fluid 4	Fluid 5
Viscosity [Pa · s]	0.05	0.02	0.01	0.0075	0.005
Surface Tension [mN / m]	68	68	68	68	68
Density [g / cm ³ ]	1	1	1	1	1
Ohnesorge Number	0.21	0.08	0.04	0.03	0.021

저점도 유체 (표 1의 유체 2) 가 노즐에서 떨어지는 것을 시뮬레이션 합니다. 색상변수는 속도크기 (단위 : cm / s)이며 속도벡터가 표시됩니다.

<표 2 : FLOW-3D를 사용하여 시뮬레이션 된 고점도 유체의 특성>

Fluid Property	Fluid 6	Fluid 7	Fluid 8	Fluid 9
Viscosity [Pa · s]	1.5	0.8	0.5	0.25
Surface Tension [mN / m ]	68	68	68	68
Density [g / cm ³ ]	1	1	1	1
Ohnesorge Number	6.24	3.33	2.08	1.04

고점도 유체 (표 2의 유체 8) 가 노즐에서 떨어지는 것을 시뮬레이션 합니다. 색상변수는 속도크기 (단위 : cm / s) 이며 속도 벡터가 표시됩니다.

Discussion of the Simulation Results

드롭 형성 및 분리는 표1과 표2에 열거 된 유체에 대해 FLOW-3D 를 사용하여 시뮬레이션 하였고, 시간 경과에 따른 necking 모양, 반경을 분석하였습니다. 물방울의 necking 모양과 저점도에서의 necking에 대한 역학(그림 1 참조)은 실험, 흐름 이론, 1D 시뮬레이션, 자기유사 관성에 대한 모세현상의 특성을 나타냅니다 (1, 2, 6, 7, 13) :

(1) $\ displaystyle \ frac {{R (t)}} {{{{R} _ {0}}}} \ approx 0.8 R {{{{왼쪽} {R} {0} 3}}} 오른쪽}) ^ {{{{frac {1} {3}}} {{왼쪽 {{{{왼쪽}}} {2} {3}}}}$

여기서 R (t)가 necking의 순간 반경이고, R₀는 노즐의 외부반경이며, $\ displaystyle \ sigma$ 는 표면 장력, $\ displaystyle \ rho$ 는 유체의 밀도 t_C 는 pinch-off 시간이다. 마찬가지로, 이러한 더 높은 점도의 뉴턴유체에 대한 반경 변화데이터는 시간에 따른 반경의 감소를 나타내는 것이며, Papageorgiou’s visco-capillary scaling (8, 9)은 아래의 식으로 표현된다.

(2) $\ {0 \} {} {} {} {} {} {} {} {} {} {} {} {} { } ({{t} _ {p}} - t)$

모세관 속도(표면 장력과 점도의 비)의 측정 값은 McKinley와 Tripathi (8)에 의해 Capillary Break-Up Extensional Rheometer (CaBER)라고 불리는 상업적으로 이용 가능한 장비를 사용하여 얻은 값과 모세관 속도는 공칭 표면 장력과 점도를 사용하여 계산됩니다.

FLOW-3D 는 물방울의 necking부분을 속도 벡터로 시각화하여 유체의 흐름을 나타낼 수 있습니다. 또한, 이는 그림 1과 같이 전단, 확장을 겪은 후 얇아지는 물방울이 흐르는 과정의 순간을 결정할 수 있는 가능성을 줍니다. 추가로, 낮은 점도의 뉴턴유체는 높은 점도의 뉴턴 유체에 비해 질적으로 다른 거동을 보여준다(그림 2참조). 낮은 점도의 뉴턴 유체에 대한 necking 프로파일은 이론(6,13)에 따라 자기 유사성이 됩니다.

Conclusions, Outlook and Ongoing work

우리의 예비결과는 FLOW-3D 기반의 전산해석이 액적 형성과 탈착의 기초가 되는 프로토타입의 자유 표면흐름을 시뮬레이션하는데 사용될 수 있음을 보여줍니다 . 시뮬레이션된 반경변화 프로파일이 실험적으로 관찰된 높은 유체 및 이론적으로 예측된 유체인 스케일링 법칙 및 pinch-off dynamics과 일치하는 것을 발견하였습니다.

자주 사용되는 1D 또는 2D 모델과 달리 FLOW-3D 는 기본 응력 및 확장 유동장 (균일도 및 크기)의 강도와 얇은 액체 필라멘트 내 흐름에 대한 시각화를 나타낼 수 있습니다(그림1과 2 참조). 확장 유동장과 연관된 흐름 방향 속도 구배는 모세관현상이 나타나는 물방울의 얇은 부분 내에서 발생합니다. 유동학적으로 복잡한 유체에서 non Newtonian shear 및 신장, 점도뿐만 아니라 그외의 탄성 응력이 nonlinear pinch-off dynamics을 급격하게 변화시킵니다(2, 10-12). 우리는 현재 점탄성과 non-Newtonian 유동학을 사용하여 FLow-3D에 복합 유체의 처리 성능평가를 위한 강력한 연산 프로토콜을 개발하고 있습니다.

References

J. Eggers, Nonlinear dynamics and breakup of free-surface flows. Rev. Mod. Phys. 69, 865-929 (1997).
G. H. McKinley, Visco-elasto-capillary thinning and break-up of complex fluids. Rheology Reviews, 1-48 (2005).
B. Derby, Inkjet Printing of Functional and Structural Materials: Fluid Property Requirements, Feature Stability, and Resolution. Annual Review of Materials Research 40, 395-414 (2010).
O. A. Basaran, H. Gao, P. P. Bhat, Nonstandard Inkjets. Annual Review of Fluid Mechanics 45, 85-113 (2013).
S. Kumar, Liquid Transfer in Printing Processes: Liquid Bridges with Moving Contact Lines. Annual Review of Fluid Mechanics 47, 67-94 (2014).
R. F. Day, E. J. Hinch, J. R. Lister, Self-similar capillary pinchoff of an inviscid fluid. Phys. Rev. Lett. 80, 704-707 (1998).
J. Eggers, M. A. Fontelos, Singularities: Formation, Structure, and Propagation. (Cambridge University Press, Cambridge, UK, 2015), vol. 53.
G. H. McKinley, A. Tripathi, How to extract the Newtonian viscosity from capillary breakup measurements in a filament rheometer. J. Rheol. 44, 653-670 (2000).
D. T. Papageorgiou, On the breakup of viscous liquid threads. Phys. Fluids 7, 1529-1544 (1995).
J. Dinic, L. N. Jimenez, V. Sharma, Pinch-off dynamics and dripping-onto-substrate (DoS) rheometry of complex fluids. Lab on a Chip 17, 460-473 (2017).
J. Dinic, Y. Zhang, L. N. Jimenez, V. Sharma, Extensional Relaxation Times of Dilute, Aqueous Polymer Solutions. ACS Macro Letters 4, 804-808 (2015).
V. Sharma et al., The rheology of aqueous solutions of Ethyl Hydroxy-Ethyl Cellulose (EHEC) and its hydrophobically modified Analogue (hmEHEC): Extensional flow response in capillary break-up, jetting (ROJER) and in a cross-slot extensional rheometer. Soft Matter 11, 3251-3270 (2015).
J. R. Castrejón-Pita et al., Plethora of transitions during breakup of liquid filaments. Proc. Natl. Acad. Sci. U.S.A. 112, 4582-4587 (2015).

[FLOW-3D 물리모델] Surface Tension / 표면 장력

Surface Tension / 표면 장력

표면장력은 기체와 액체 사이 또는 두 섞이지 않는 액체 사이에서 뚜렷한 경계면에 접한 평면에 작용하는 힘이다. 이 힘은 두 물질 사이 분자간 힘의 차이에 의해 발생한다. FLOW-3D 에서 표면장력은 1 또는 2유체의 유동에서 모델링 될 수 있으며 항상 General → Interface tracking 에서 활성화된 Free surface or sharp interface 모델과 함께 사용 되어야 한다.

이 모델은 Physics → Surface tension 과 Activate surface tension model 를 체크함으로써 활성화된다. 표면장력 계수는(Surface tension coefficient, SIGMA) 또는 물성치 트리에서 지정된다: Fluids → Surface Tension → Surface Tension Coefficient. 여기서 정의되는 전반적 Contact angle은 경계면이 고체벽 경계와 고체요소를 만날 때 습윤거동을 조절한다. 접촉각 변수는 0.0(완전습윤)과 1.0(완전 비습윤) 사이의 값을 취한다. 추가로 각 요소의 개별적 접촉각이 Meshing and Geometry → Component Properties → Surface properties → Component Contact Angle에서 지정될 수 있다. 그렇지 않은 경우 전반적인 Contact angle로 기본값이 지정된다.

기본설정은 90도의 접촉각이다. 표면장력이 활성화되면 벽 접착이 활성화된다; 어떤 요소에서는 벽 접착을 해제시킬 수 없다. 모든 유체는 고체표면에서 접착 거동을 보여주며 습윤이거나 비습윤에 상관 없이 그 거동을 나타내기 위해 거동이 접촉각을 지정하는 것이 필요하다.

표면장력 계수는 온도의 함수이다. 온도와 표면장력간의 단순한 선형 관계식을 위해 다음에 따르는 Temperature Dependence (Fluids 내 Temperature sensitivity)에 대한 값을 준다.

where: 여기서

σ 는 계산된 표면 장력계수
σ₀는 사용자 정의된 Surface tension coefficient값
는 Temperature Dependence
T 는 지역온도이며
T 는 사용자 지정 Reference Temperature (Fluids → Properties에서 지정)

이 값들은 Physics → Surface tension 대화상자나 Fluids → Surface Tension에서 입력된다. 온도 의존 모델을 사용하기 위해서 유체 내 열 전달을 활성화 시키는 것이 필요하다.(Physics → Heat transfer→ Fluid internal energy advection).

표면장력이 온도의 선형함수가 아닌 경우, Surface tension coefficient in Fluids → Surface Tension 옆 Tabular 버튼을 선택함으로써 표면장력 및 온도와 관련된 표 형식의 데이터를 입력할 수 있다.

자유표면이 부서지고 변형되는 유동에서 작은 유체방울이 간혹 표면장력 압력 계산의 수치적 잡음 때문에 발생될 수 있다. 이 방울들은 작은 표면 곡률 때문에 표면 압력 분포에 바람직하지 않은 변화를 일으킬 수 있다. 이런 방울 들은 이러한 수치적 부 정확성을 감소시키기 위해 제거될 수 있다. 이 옵션은 Fluid fraction cleanup in Numerics → Volume-of-fluid advection → Advanced options의 값을 입력함으로써 조절된다.

이 변수에 양수 값을 지정하면 유체분율 제거옵션이 활성화된다. 한 셀과 그 주변 셀의 유체분율 값이 이보다 작으면 유체는 그 셀에서 완전히 제거될 것이다.

제거된 체적은 누적 체적 에러로 기록되고 시간의 함수로 모사 후에 그려진다.; 이는 Fluid fraction cleanup 선택이 유체 체적이 크게 변경된 경우 보여진다. 2유체 표면장력 문제의 Fluid fraction cleanup 기본값은 0.05이다. 다른 모사의 경우 이의 기본값은0이다.

표면장력 모델은 곡률, 즉, 2차 미분 값에 의존하기 때문에 코드내의 다른 모델보다 불규칙한 격자에 더 민감하다. 가능하면 정육면체(2차원의 정사각형)에 가까운 제어체적을 사용하는 것을 추천한다.

표면 장력 / Surface Tension

FLOW-3D에 추가 된 최초의 물리 모델 중 하나는 표면 장력이었습니다.

이 모델은 잉크젯, 무중력 환경에서의 액체 연료 거동 및 다양한 MEMS (마이크로 전자 기계 시스템) 장치와 같이 다양한 종류의 응용 분야에서 수년 동안 널리 사용되어 왔습니다. 이 후에 모델의 개선 및 확장에 대한 많은 사용자 요청이 처리되었습니다.
표면 장력에 대해 보다 나은 성능개선을 위해 FLOW-3D 버전 11에 대한 새로운 모델이 개발되었습니다. 이 모델은 계산된 모든 표면 장력의 정확성과 임의 형상의 솔리드 표면을 잡아 당기는 접착력의 정확성을 향상시킵니다. 또한 이 새로운 모델은 다공성 물질의 모세관 압력과 비 균일한 표면 장력으로 인한 접선 표면 장력을 가지고 있습니다.

새로운 모델의 예는 무중력에 포함된 원형 벽을 적시는 단순한 문제입니다.

그림 1은 실린더와 접촉각이 0 도인 물로 채워진 0.25m 직경의 실린더 75 %의 경우를 보여줍니다. 버블은 10 초 전에 벽에서 깨끗하게 분리되어 탱크를 가로 질러 움직입니다. 비 구형은 기포 표면에서 모세관 파가 전파되기 때문입니다.

그림 1. 0.0, 2.5, 5.0 및 10.0 초에 무중력에서 접촉 각이 0 인 실린더 표면의 유체 (적색) 습윤 표면.

다른 예가 그림5에 도시되어 있습니다. 2에서 서로 다른 밀도의 2 개의 초기 구형 방울이 (플롯의 색으로 표시됨) 단단한 벽을 향해 아래로 이동합니다. 플롯의 시간은 0.0, 0.01, 0.02 및 0.03 초입니다. 방울은 직경이 0.0017m, 밀도가 다르지만 표면 장력 계수는 1.872 뉴턴 / m입니다.

그림 2. 접시쪽으로 움직이는 구형의 물방울. 새로운 표면 장력 모델로 시뮬레이션. 색상은 밀도를 나타냅니다.

표면 장력 모델에 대해 자세히 알아보십시오.

Download the Flow Science Report on Surface Tension

Download Surface Tension Validation – Simple Test Problems

FSR_01-12_Air-Entrainment-Report [공기 혼입 모델 분석]

Overview
In free-surface flows the turbulence in the liquid may be sufficient to disturb the surface to the point of entraining air into the flow. This process is important, for example, in water treatment where air is needed to sustain microorganisms for water purification and in rivers and streams for sustaining a healthy fish population. Air entrainment is typically engineered into spillways downstream of hydropower plants to reduce the possibility of cavitation damage at the base of the spillway. Situations where air entrainment is undesirable are in the sprue and runner systems used by metal casters, and in the filling of liquid containers used for consumer products.
The importance of being able to predict the amount and distribution of entrained air at a free liquid surface has led to the development of a unique model in FLOW-3D®. The model has two options. One option, to be used when the volume fraction of entrained air is relatively low, uses a passive scalar variable to record and transport the air volume fraction. This model is passive in that it does not alter the dynamics of the flow.
The second air-entrainment model option is based on a variable density formulation. This model includes the “bulking” of fluid volume by the addition of air and the buoyancy effects associated with entrained air. This dynamically coupled model cannot, however, be used in conjunction with heat transport and natural (thermal) convection.
In addition, when using the variable density formulation, the model can include a relative drifting of air in water, the possible escape of air if it rises to the surface of the water and the removal or addition of air to trapped bubble regions represented as adiabatic bubbles.
The same basic entrainment process is used in both options. It is based on a competition between the stabilizing forces of gravity and surface tension and the destabilizing effects of surface turbulence.
Because turbulence is the main cause of entrainment, a turbulence-transport model must be used in connection with the air-entrainment model. It is recommended that the RNG version of the more traditional k-epsilon turbulence model be employed. All the validation tests reported in this Technical Note were performed using the RNG model.

[다운로드]

FSR_01-12_Air-Entrainment-Report

Salt dissolution model [소금 용해 모델]

Introduction
Dissolution of salt in liquid is of interest in several applications – from solution mining to food processing to medical applications. This article describes a new model in FLOW-3D1 version 10.0 for dissolving salt in fluids and tracking the solute in the brine.
The dissolution of salt increases the density of the fluid and thus may affect the flow. In addition, as salt is dissolved, the flow domain increases. It is of interest, therefore, to predict these changes in the flow as well as the transport of the dissolved salt in the fluid.
The model accounts for the basic physical phenomena, such as mass transfer at the interface between salt and fluid, the change of volume and shape of the solid salt, diffusion and convection of dissolved salt in fluid and, finally, the change in fluid density, viscosity and surface tension coefficient.

Simulating the Residue left by Evaporating Drops

Background
The “coffee ring” effect is the name given to a well known observation where the evaporative drying of a drop of coffee leaves behind a ring of dark material at the edge of the original drop. On first thought one would expect that the coffee particles, which are uniformly distributed in the drop, would simply be deposited uniformly over the area wetted by the drop. It has only been in recent years that researchers have uncovered the mechanisms that produce the ring effect (Deegan, R.D., et al).
As currently understood, the edges of drops can become pinned because of roughness or chemical elements on the surface on which they lie. Heat transfer to the drops from the substrate or the air induces evaporation, which is usually greater near the drop edge. Surface tension forces then adjust the curvature of the remaining liquid consistent with the pinned edge, which results in a net flow of liquid toward the edge. This flow replenishes the evaporative loss but also moves solute to the edge where it is concentrated by evaporation. Eventually, this mechanism builds up a ring deposit of solute at the original edge of the drop.
The residue from dried drops has implications for many useful applications, including general coating processes, formation of pixel arrays of organic materials for video displays and for a variety of micro-electro-mechanical (MEMS) devices.
Because many factors control the distribution of dried residue it is desirable to have some means to model the fluid dynamics of the process to aid engineers in making the best choices for each specific application. Such a capability has been incorporated into FLOW-3D1 making it possible to computationally investigate the influence of such parameters as the initial solute concentration, fluid viscosity, volatility of the solvent, evaporation rate, surface tension and initial shape of the drop.
This technical note presents a brief description of the residue formation model and illustrates it with several computations of an evaporating drop subject to different physical conditions.

Modeling Turbulent Entrainment of Air at a Free Surface

Overview
In free-surface flows the turbulence in the liquid may be sufficient to disturb the surface to the point of entraining air into the flow. This process is important, for example, in water treatment where air is needed to sustain microorganisms for water purification and in rivers and streams for sustaining a healthy fish population. Air entrainment is typically engineered into spillways downstream of hydropower plants to reduce the possibility of cavitation damage at the base of the spillway. Other situations where air entrainment is undesirable are in the sprue and runner systems used by metal casters, and in the filling of liquid containers used for consumer products.
The importance of being able to predict the amount and distribution of entrained air at a free liquid surface has led to the development of a unique model that can be easily inserted into FLOW-3D® as a user customization. The model has two options. One option, to be used when the volume fraction of entrained air is relatively low, uses a scalar variable to record the air volume fraction. This model is passive in that it does not alter the dynamics of the flow.
A second air-entrainment model, option two, is based on a variable density formulation. This model includes the “bulking” of fluid volume by the addition of air and the buoyancy effects associated with entrained air. However, this dynamically coupled model cannot be used in connection with heat transport and natural (thermal) convection.
In both model options the same basic entrainment process is used that is based on a competition between the stabilizing forces of gravity and surface tension and the destabilizing effects of surface turbulence. The model is described in the next section. Because turbulence is the main cause of entrainment, a turbulence-transport model must be used in connection with the air-entrainment model (i.e., ifvis=3 or 4). It is recommended that the RNG version of the more traditional k-epsilon turbulence model be employed. All the validation tests reported in this Technical Note were performed using the RNG turbulence model.

Surface Tension Validation Tests

Modeling surface tension phenomena is computationally difficult because it requires the evaluation of second derivatives.
This is particulary true in the FLOW-3D program where the capability to represent highly complicated and multiple free surfaces difficulties are further compounded in three-dimensional calculations because one is often forced, for reasons of economy, to use marginal numerical resolution.

Simulating the Wetting and Drying of Shallow Flows [얕은 흐름의 습윤 및 건조 시뮬레이션]

Introduction
Shallow flows, characterized by having a thickness much smaller than their lateral extent, can often be modeled by a depth-averaged (shallow-water or 2.5 dimensional) approximation.
Average fluid velocities are computed in the layer and the top fluid surface is free to move, which leads to a changing fluid-layer thickness. The advantages of this approach are its speed
and simplicity over full three-dimensional simulations.
One complication, however, is how to efficiently account for dynamic contact-line effects at lateral boundaries of the fluid. These boundaries are free to move over the underlying solid
surface. Furthermore, the fluid contact angle at these boundaries depends on the local dynamic flow conditions.
In this paper we present a new shallow-flow computational method based on the Volume-of-Fluid (VOF) technique, which conserves fluid mass, while allowing for general wetting and
drying behavior. Non-uniform surface tension and fluid-substrate interactions, defined by a static contact angle, are included in the model. No special prescriptions are needed to locate
contact line locations or define dynamic contact angles.

A Surface Tension Model Update [표면장력 모델 업데이트]

PURPOSE AND BACKGROUND
The modeling of surface tension forces is computationally difficult because it requires the evaluation of surface curvatures, i.e., second derivatives of the surface location. This is
particularly true in FLOW-3D® since it uses a regular rectangular grid that does not conform to surface shapes. Although this simple grid structure makes it more difficult to evaluate surface
slopes and curvatures, it is this feature that also gives the strength needed to simulate coalescence and breakup of fluid blobs.
Evaluation of surface slope and curvature in FLOW-3D® is done by determining which coordinate direction is closest to the outward normal vector to the surface. Then fluid in a 3 by 3
by 3 set of grid cells surrounding a given cell is summed up in the cell columns parallel to the normal. This, in effect, gives a discrete representation of the surface height in nine (3×3)
columns, which can be used to compute slopes and curvatures.
In most cases this procedure works quite well, but when normal directions in the grid are near 45° the surface may be too steep for this procedure to work accurately. A consequence of this
loss of accuracy is the introduction of spurious pressures or perturbations that sometimes generate undesirable capillary waves (i.e., kinetic energy noise). Occasionally, these
perturbations can even destroy a computation.
A summary of the original surface tension model was given in Technical Note TN6, “Surface Tension Validation Tests,” (1987). Since that Note there have been a number of major improvements:

1. Wall adhesion sensitive to slope of wall,
2. Static contact angle as an obstacle property,
3. Two-fluid interfacial surface tension,
4. Thermocapillary (i.e., tangential) surface forces (see TN47).

In this Technical Note we document another improvement that has been made. In particular, we have improved the accuracy of the column summation technique for the computation of surface
curvatures. As the following examples will show, this improvement is quite dramatic in many cases where the earlier model experienced substantial difficulties.

물리 모델 소개

FLOW-3D 는 고도의 정확성이 필요한 항공, 자동차, 수자원 및 환경, 금속 산업분야의 세계적인 선진 기업에서 사용됩니다.

FLOW-3D의 광범위한 다중 물리 기능(multiphysics )은 자유 표면 흐름, 표면 장력, 열전달, 난류, 움직이는 물체, 단순 변형 고체, 전기 기계, 캐비테이션, 탄/소성, 점성, 가소성, 입자, 고체 연료, 연소 및 위상 변화를 포함합니다.
이러한 모델은 FLOW-3D를 사용하는 사용자들이 기술 및 과학의 광범위한 문제를 해결하도록 설계를 최적화하고 복잡한 프로세스 흐름에 대한 통찰력을 얻을 수 있도록 합니다.

Physics Models

Air EntrainmentCavitation
Cavitation
Combustible Objects
Core Gas
Defect Tracking
Dissolving Objects
Drift Flux
Granular Flow
Lost Foam
Moisture
Moving Objects
Porous Media
Sediment Scour
Shallow Water
Thermal Die Cycling
Solidification
Wind Effects

Flow/Fluid Modes

Incompressible and Compressible Flows
Constant/Varying Density
Fluid Sources
Non-Inertial Frame Reference
Laminar/Turbulent Flow
Elastic Stresses
Electro-Mechanics
Heat Transfer
Particle Tracking
Surface Tension
Wall Contact Time
Phase Change

Materials Databases

Fluids Database
Solids Database

매우 정확한
시뮬레이션 결과

FAVOR™, 으로 알려진 특별한 메쉬 프로세스는 데카르트 구조의 단순함을 유지하면서 복잡한 형상을 효율적으로 구현합니다.

Optimized Setup
and Workflow

TruVOF 표면 추적 방법은 유동시뮬레이션을 위해 알려진 유체 체적을 사용하는 동안 가장 높은 정확도를 제공합니다.

FlowSight
Postprocessing

산업계에서 최고의 시각화 postprocessor인 FlowSight 는 사용자에게 2차원 및 3차원에 대한 심층 분석 기능을 제공합니다.

Microfluidics Bibliography

다음은 Microfluidics Bibliography의 기술 문서 모음입니다.
이 모든 논문은 FLOW-3D 결과를 특징으로 합니다. 미세 유체 공정 및 장치 를 성공적으로 시뮬레이션하기 위해 FLOW-3D 를 사용 하는 방법에 대해 자세히 알아보십시오 .

2023년 3월 10일 Update

64-22 Babatunde Aramide, Computational modelling of electrohydrodynamic jetting (Taylor cone formation, dripping & jet evolution): Case study of electrospinning, Thesis, University College London, 2022.

138-21 Enver Guler, Mine Eti, Aydin Cihanoglu, Esra Altiok, Kadriye Ozlem Hamaloglu, Burcu Gokcal, Ali Tuncel, Nalan Kabay, Ion exchange membranes with enhanced antifouling properties to produce energy from renewable sources, Proceedings of the 6th International Symposium on Green and Smart Technologies for a Sustainable Society, Santander, Cantabria, Spain, December 9-10, 2021.

45-21 Navid Tonekaboni, Mahdi Feizbahr, Nima Tonekaboni, Guang-Jun Jiang, Hong-Xia Chen, Optimization of solar CCHP systems with collector enhanced by porous media and nanofluid, Mathematical Problems in Engineering, 2021; 9984940, 2021. doi.org/10.1155/2021/9984840

40-21 B. Hayes, G.L. Whiting, R. MacCurdy, Modeling of contactless bubble–bubble interactions in microchannels with integrated inertial pumps, Physics of Fluids, 33.4; 042002, 2021. doi.org/10.1063/5.0041924

Below is a collection of technical papers in our Microfluidics Bibliography. All of these papers feature FLOW-3D results. Learn more about how FLOW-3D can be used to successfully simulate microfluidic processes and devices.

14-21 Jian-Chiun Liou, Chih-Wei Peng, Philippe Basset, Zhen-Xi Chen, DNA printing integrated multiplexer driver microelectronic mechanical system head (IDMH) and microfluidic flow estimation, Micromachines, 12.1; 25, 2021. doi.org/10.3390/mi12010025

08-20 Li Yong-Qiang, Dong Jun-Yan and Rui Wei, Numerical simulation for capillary driven flow in capsule-type vane tank with clearances under microgravity, Microgravity Science and Technology, 2020. doi.org/10.1007/s12217-019-09773-z

89-19 Tim Dreckmann, Julien Boeuf, Imke-Sonja Ludwig, Jorg Lumkemann, and Jorg Huwyler, Low volume aseptic filling: impact of pump systems on shear stress, European Journal of Pharmeceutics and Biopharmeceutics, in press, 2019. doi:10.1016/j.ejpb.2019.12.006

88-19 V. Amiri Roodan, J. Gomez-Pastora, C. Gonzalez-Fernandez, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, CFD analysis of the generation and manipulation of ferrofluid droplets, TechConnect Briefs, pp. 182-185, 2019. TechConnect World Innovation Conference & Expo, Boston, Massachussetts, USA, June 17-19, 2019.

55-19 Julio Aleman, Sunil K. George, Samuel Herberg, Mahesh Devarasetty, Christopher D. Porada, Aleksander Skardal, and Graça Almeida‐Porada, Deconstructed microfluidic bone marrow on‐a‐chip to study normal and malignant hemopoietic cell–niche interactions, Small, 2019. doi: 10.1002/smll.201902971

37-19 Feng Lin Ng, Miniaturized 3D fibrous scaffold on stereolithography-printed microfluidic perfusion culture, Doctoral Thesis, Nanyang Technological University, Singapore, 2019.

32-19 Jenifer Gómez-Pastora, Ioannis H. Karampelas, Eugenio Bringas, Edward P. Furlani, and Inmaculada Ortiz, Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions, Nature: Scientific Reports, Vol. 9, No. 7265, 2019. doi: 10.1038/s41598-019-43827-x

01-19 Jelena Dinic and Vivek Sharma, Computational analysis of self-similar capillary-driven thinning and pinch-off dynamics during dripping using the volume-of-fluid method, Physics of Fluids, Vol. 31, 2019. doi: 10.1063/1.5061715

75-18 Tobias Ladner, Sebastian Odenwald, Kevin Kerls, Gerald Zieres, Adeline Boillon and Julien Bœuf, CFD supported investigation of shear induced by bottom-mounted magnetic stirrer in monoclonal antibody formulation, Pharmaceutical Research, Vol. 35, 2018. doi: 10.1007/s11095-018-2492-4

53-18 Venoos Amiri Roodan, Jenifer Gómez-Pastora, Aditi Verma, Eugenio Bringas, Inmaculada Ortiz and Edward P. Furlani, Computational analysis of magnetic droplet generation and manipulation in microfluidic devices, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 154, 2018. doi: 10.11159/ffhmt18.154

35-18 Jenifer Gómez-Pastora, Cristina González Fernández, Marcos Fallanza, Eugenio Bringas and Inmaculada Ortiz, Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies, Chemical Engineering Journal, vol. 344, pp. 487-497, 2018. doi: 10.1016/j.cej.2018.03.110

15-18 J. Gómez-Pastora, I.H. Karampelas, A.Q. Alorabi, M.D. Tarn, E. Bringas, A. Iles, V.N. Paunov, N. Pamme, E.P. Furlani, I. Ortiz, CFD analysis and experimental validation of magnetic droplet generation and deflection across multilaminar flow streams, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 182-185, 2018.

14-18 J. Gómez-Pastora, C. González-Fernández, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, Design of Magnetic Blood Cleansing Microdevices through Experimentally Validated CFD Modeling, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 170-173, 2018.

10-18 A. Gupta, I.H. Karampelas, J. Kitting, Numerical modeling of the formation of dynamically configurable L2 lens in a microchannel, Biotech, Biomaterials and Biomedical TechConnect Briefs, Vol. 3, pp. 186 – 189, 2018.

17-17 I.H. Karampelas, J. Gómez-Pastora, M.J. Cowan, E. Bringas, I. Ortiz and E.P. Furlani, Numerical Analysis of Acoustophoretic Discrete Particle Focusing in Microchannels, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

16-17 J. Gómez-Pastora, I.H. Karampelas, E. Bringas, E.P. Furlani and I. Ortiz, CFD analysis of particle magnetophoresis in multiphase continuous-flow bioseparators, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

102-16 J. Brindha, RA.G. Privita Edwina, P.K. Rajesh and P.Rani, “Influence of rheological properties of protein bio-inks on printability: A simulation and validation study,” Materials Today: Proceedings, vol. 3, no.10, pp. 3285-3295, 2016. doi: 10.1016/j.matpr.2016.10.010

99-16 Ioannis H. Karampelas, Kai Liu, Fatema Alali, and Edward P. Furlani, Plasmonic Nanoframes for Photothermal Energy Conversion, J. Phys. Chem. C, 2016, 120 (13), pp 7256–7264

98-16 Jelena Dinic and Vivek Sharma, Drop formation, pinch-off dynamics and liquid transfer of simple and complex fluids, http://meetings.aps.org/link/BAPS.2016.MAR.B53.12, APS March Meeting 2016, Volume 61, Number 2, March 14–18, 2016, Baltimore, Maryland

67-16 Vahid Bazargan and Boris Stoeber, Effect of substrate conductivity on the evaporation of small sessile droplets, PHYSICAL REVIEW E 94, 033103 (2016), doi: 10.1103/PhysRevE.94.033103

57-16 Ioannis Karampelas, Computational analysis of pulsed-laser plasmon-enhanced photothermal energy conversion and nanobubble generation in the nanoscale, PhD Dissertation: Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, July 2016

44-16 Takeshi Sawada et al., Prognostic impact of circulating tumor cell detected using a novel fluidic cell microarray chip system in patients with breast cancer, EBioMedicine, Available online 27 July 2016, doi: 10.1016/j.ebiom.2016.07.027.

30-16 Ioannis H. Karampelas, Kai Liu and Edward P. Furlani, Plasmonic Nanocages as Photothermal Transducers for Nanobubble Cancer Therapy, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

02-16 Stephen D. Hoath (Editor), Fundamentals of Inkjet Printing: The Science of Inkjet and Droplets, ISBN: 978-3-527-33785-9, 472 pages, February 2016 (see chapters 2 and 3 for FLOW-3D results)

125-15 J. Berthier, K.A. Brakke, E.P. Furlani, I.H. Karampelas, V. Poher, D. Gosselin, M. Cubinzolles and P. Pouteau, Whole blood spontaneous capillary flow in narrow V-groove microchannels, Sensors and Actuators B: Chemical, 206, pp. 258-267, 2015.

77-15 Ho-Lin Tsai, Weng-Sing Hwang, Jhih-Kai Wang, Wen-Chih Peng and Shin-Hau Chen, Fabrication of Microdots Using Piezoelectric Dispensing Technique for Viscous Fluids, Materials 2015, 8(10), 7006-7016. doi: 10.3390/ma8105355

46-15 Adwaith Gupta, 3D Printing Multi-Material, Single Printhead Simulation, Advanced Qualification of Additive Manufacturing Materials Workshop, July 20 – 21, 2015, Santa Fe, NM

28-15 Yongqiang Li, Mingzhu Hu, Ling Liu, Yin-Yin Su, Li Duan, and Qi Kang, Study of Capillary Driven Flow in an Interior Corner of Rounded Wall Under Microgravity, Microgravity Science and Technology, June 2015

20-15 Pamela J. Waterman, Diversity in Medical Simulation Applications, Desktop Engineering, May 2015, pp 22-26,

16-15 Saurabh Singh, Ann Junghans, Erik Watkins, Yash Kapoor, Ryan Toomey, and Jaroslaw Majewski, Effects of Fluid Shear Stress on Polyelectrolyte Multilayers by Neutron Scattering Studies, © 2015 American Chemical Society, DOI: 10.1021/acs.langmuir.5b00037, Langmuir 2015, 31, 2870−2878, February 17, 2015

11-15 Cheng-Han Wu and Weng-Sing Hwang, The effect of process condition of the ink-jet printing process on the molten metallic droplet formation through the analysis of fluid propagation direction, Canadian Journal of Physics, 2015. doi: 10.1139/cjp-2014-0259

03-15 Hanchul Cho, Sivasubramanian Somu, Jin Young Lee, Hobin Jeong and Ahmed Busnaina, High-Rate Nanoscale Offset Printing Process Using Directed Assembly and Transfer of Nanomaterials, Adv. Materials, doi: 10.1002/adma.201404769, February 2015

122-14 Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastián D’hers and Noel M Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Research Gate, doi: 10.1007/s13346-014-0198-7, July 2014

113-14 Cihan Yilmaz, Arif E. Cetin, Georgia Goutzamanidis, Jun Huang, Sivasubramanian Somu, Hatice Altug, Dongguang Wei and Ahmed Busnaina, Three-Dimensional Crystalline and Homogeneous Metallic Nanostructures Using Directed Assembly of Nanoparticles, 10.1021/nn500084g, © 2014 American Chemical Society, April 2014

110-14 Koushik Ponnuru, Jincheng Wu, Preeti Ashok, Emmanuel S. Tzanakakis and Edward P. Furlani, Analysis of Stem Cell Culture Performance in a Microcarrier Bioreactor System, Nanotech, Washington, D.C., June 15-18, 2014

109-14 Ioannis H. Karampelas, Young Hwa Kim and Edward P. Furlani, Numerical Analysis of Laser Induced Photothermal Effects using Colloidal Plasmonic Nanostructures, Nanotech, Washington, D.C., June 15-18, 2014

108-14 Chenxu Liu, Xiaozheng Xue and Edward P. Furlani, Numerical Analysis of Fully-Coupled Particle-Fluid Transport and Free-Flow Magnetophoretic Sorting in Microfluidic Systems, Nanotech, Washington, D.C., June 15-18, 2014

95-14 Cheng-Han Wu, Weng-Sing Hwang, The effect of the echo-time of a bipolar pulse waveform on molten metallic droplet formation by squeeze mode piezoelectric inkjet printing, Accepted November 2014, Microelectronics Reliability (2014) , © 2014 Elsevier Ltd. All rights reserved.

61-14 Chenxu Liu, A Computational Model for Predicting Fully-Coupled Particle-Fluid Dynamics and Self-Assembly for Magnetic Particle Applications, Master’s Thesis: State University of New York at Buffalo, 2014, 75 pages; 1561583, http://gradworks.umi.com/15/61/1561583.html

41-14 Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastian D’hers, and Noel M. Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Drug Deliv. and Transl. Res., DOI 10.1007/s13346-014-0198-7, # Controlled Release Society 2014. Available for purchase online at SpringerLink.

21-14 Suk-Hee Park, Ung Hyun Koh, Mina Kim, Dong-Yol Yang, Kahp-Yang Suh and Jennifer Hyunjong Shin, Hierarchical multilayer assembly of an ordered nanofibrous scaffold via thermal fusion bonding, Biofabrication 6 (2014) 024107 (10pp), doi:10.1088/1758-5082/6/2/024107, IOP Publishing, 2014. Available for purchase online at IOP.

17-14 Vahid Bazargan, Effect of substrate cooling and droplet shape and composition on the droplet evaporation and the deposition of particles, Ph.D. Thesis: Department of Mechanical Engineering, The University of British Columbia, March 2014, © Vahid Bazargan, 2014

73-13 Oliver G. Harlen, J. Rafael Castrejón-Pita, and Arturo Castrejon-Pita, Asymmetric Detachment from Angled Nozzles Plates in Drop-on Demand Inkjet Printing, NIP & Digital Fabrication Conference, 2013 International Conference on Digital Printing Technologies. Pages 253-549, pp. 277-280(4)

63-13 Fatema Alali, Ioannis H. Karampelas, Young Hwa Kim, and Edward P. Furlani, Photonic and Thermofluidic Analysis of Colloidal Plasmonic Nanorings and Nanotori for Pulsed-Laser Photothermal Applications, J. Phys. Chem. C, Article ASAP, DOI: 10.1021/jp406986y, Copyright © 2013 American Chemical Society, September 2013.

25-13 Sudhir Srivastava, Theo Driessen, Roger Jeurissen, Herma Wijshoff, and Federico Toschi, Lattice Boltzmann Method to Study the Contraction of a Viscous Ligament, International Journal of Modern Physics © World Scientific Publishing Company, May 2013.

11-13 Li-Chieh Hsu, Yong-Jhih Chen, Jia-Huang Liou, Numerical Investigation in the Factors on the Pool Boiling, Applied Mechanics and Materials Vol. 311 (2013) pp 456-461, © (2013) Trans Tech Publications, Switzerland, doi:10.4028/www.scientific.net/AMM.311.456. Available for purchase online at Scientific.Net.

10-13 Pamela J. Waterman, CFD: Shaping the Medical World, Desktop Engineering, April 2013. Full article available online at Desktop Engineering.

90-12 Charles R. Ortloff and Martin Vogel, Spray Cooling Heat Transfer- Test and CFD Analysis, Electronics Cooling, June 2012. Available online at Electronics Cooling.

79-12 Daniel Parsaoran Siregar, Numerical simulation of evaporation and absorption of inkjet printed droplets, Ph.D. Thesis: Technische Universiteit Eindhoven, September 18, 2012, Copyright 2012 by D.P. Siregar, ISBN: 978-90-386-3190-5.

71-12 Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, Seungwan Lee, and Woonbae Kim, Varifocal liquid lens based on microelectrofluidic technology, Optics Letters, Vol. 37, Issue 21, pp. 4377-4379 (2012) http://dx.doi.org/10.1364/OL.37.004377

70-12 Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, and Seunwan Lee, Microelectrofluidic Iris for Variable Aperture, Proc. SPIE 8252, MOEMS and Miniaturized Systems XI, 82520O (February 9, 2012); doi:10.1117/12.906587

69-12 Jong-hyeon Chang, Eunsung Lee, Kyu-Dong Jung, Seungwan Lee, Minseog Choi, and Woonbae Kim, Microelectrofluidic Lens for Variable Curvature, Proc. SPIE 8486, Current Developments in Lens Design and Optical Engineering XIII, 84860X (October 11, 2012); doi:10.1117/12.925852.

61-12 Biddut Bhattacharjee, Study of Droplet Splitting in an Electrowetting Based Digital Microfluidic System, Thesis: Doctor of Philosophy in the College of Graduate Studies (Applied Sciences), The University of British Columbia, September 2012, © Biddut Bhattacharjee.

55-12 Hejun Li, Pengyun Wang, Lehua Qi, Hansong Zuo, Songyi Zhong, Xianghui Hou, 3D numerical simulation of successive deposition of uniform molten Al droplets on a moving substrate and experimental validation, Computational Materials Science, Volume 65, December 2012, Pages 291–301. Available for purchase online at SciVerse.

54-12 Edward P. Furlani, Anthony Nunez, Gianmarco Vizzeri, Modeling Fluid Structure-Interactions for Biomechanical Analysis of the Human Eye, Nanotech Conference & Expo, June 18-21, 2012, Santa Clara, CA.

53-12 Xinyun Wu, Richard D. Oleschuk and Natalie M. Cann, Characterization of microstructured fibre emitters in pursuit of improved nano electrospray ionization performance, The Royal Society of Chemistry 2012, http://pubs.rsc.org, DOI: 10.1039/c2an35249d, May 2012

25-12 Edward P. Furlani, Ioannis H. Karampelas and Qian Xie, Analysis of Pulsed Laser Plasmon-assisted Photothermal Heating and Bubble Generation at the Nanoscale, Lab on a Chip, 10.1039/C2LC40495H, Received 01 May 2012, Accepted 07 Jun 2012. First published on the web 13 Jun 2012.

22-12 R.A. Sultanov, D. Guster, Numerical Modeling and Simulations of Pulsatile Human Blood Flow in Different 3D-Geometries, Book chapter #21 in Fluid Dynamics, Computational Modeling and Applications (2012), ISBN: 978-953-51-0052-2, p. 475 [18 pages]. Available online at INTECH.

21-12 Guo-Wei Huang, Tzu-Yi Hung, and Chin-Tai Chen, Design, Simulation, and Verification of Fluidic Light-Guide Chips with Various Geometries of Micro Polymer Channels, NEMS 2012, Kyoto, Japan, March 5-8, 2012. Available for purchase online at IEEE.

103-11 Suk-Hee Park, Development of Three-Dimensional Scaffolds containing Electrospun Nanofibers and their Applications to Tissue Regeneration, Ph.D. Thesis: School of Mechanical, Aersospace and Systems Engineering, Division of Mechanical Engineering, KAIST, 2011.

81-11 Xinyun Wu, Modeling and Characterization of Microfabricated Emitters-In Pursuit of Improved ESI-MS Performance, thesis: Department of Chemistry, Queen’s University, December 2011, Copyright © Xinyun Wu, 2011

77-11 Ge Bai, W. Thomas Leach, Computational fluid dynamics (CFD) insights into agitation stress methods in biopharmaceutical development, International Journal of Pharmaceutics, Available online 8 December 2011, ISSN 0378-5173, 10.1016/j.ijpharm.2011.11.044. Available online at SciVerse.

72-11 M.R. Barkhudarov, C.W. Hirt, D. Milano, and G. Wei, Comments on a Comparison of CFD Software for Microfluidic Applications, Flow Science Technical Note #93, FSI-11-TN93, December 2011

45-11 Chang-Wei Kang, Jiak Kwang Tan, Lunsheng Pan, Cheng Yee Low and Ahmed Jaffar, Numerical and experimental investigations of splat geometric characteristics during oblique impact of plasma spraying, Applied Surface Science, In Press, Corrected Proof, Available online 20 July 2011, ISSN 0169-4332, DOI: 10.1016/j.apsusc.2011.06.081. Available to purchase online at SciVers

33-11 Edward P. Furlani, Mark T. Swihart, Natalia Litchinitser, Christopher N. Delametter and Melissa Carter, Modeling Nanoscale Plasmon-assisted Bubble Nucleation and Applications, Nanotech Conference and Expo 2011, Boston, MA, June 13-16, 2011

32-11 Lu, Cong and Mills, James K., Three cell separation design for realizing automatic cell injection, Complex Medical Engineering (CME), 2011 IEEE/ICME, pp: 599 – 603, Harbin, China, 10.1109/ICCME.2011.5876811, June 2011. Available online at IEEEXplore.

25-11 Issam M. Bahadur, James K. Mills, Fluidic vacuum-based biological cell holding device with piezoelectrically induced vibration, Complex Medical Engineering (CME), 2011 IEEE/ICME International Conference on, 22-25 May 2011, pp: 85 – 90, Harbin, China. Available online at: IEEE Xplore.

14-11 Edward P. Furlani, Roshni Biswas, Alexander N. Cartwright and Natalia M. Litchinitser, Antiresonant guiding optofluidic biosensor, doi:10.1016/j.optcom.2011.04.014, Optics Communication, April 2011

05-11 Hyeju Eom and Keun Park, Integrated numerical analysis to evaluate replication characteristics of micro channels in a locally heated mold by selective induction, International Journal of Precision Engineering and Manufacturing, Volume 12, Number 1, 53-60, DOI: 10.1007/s12541-011-0007-x, 2011. Available online at: SpringerLink.

70-10 I.N. Volnov, V.S. Nagornyi, Modeling Processes for Generation of Streams of Monodispersed Fluid Droplets in Electro-inkjet Applications, Science and Technology News, St. Petersburg State Polytechnic University, 4, pp 294-300, 2010. In Russian.

62-10 F. Mobadersani, M. Eskandarzade, S. Azizi and S. Abbasnezhad, Effect of Ambient Pressure on Bubble Growth in Micro-Channel and Its Pumping Effect, ESDA2010-24436, pp. 577-584, doi:10.1115/ESDA2010-24436, ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis (ESDA2010), Istanbul, Turkey, July 12–14, 2010. Available online at the ASME Digital Library.

58-10 Tsung-Yi Ho, Jun Zeng, and Chakrabarty, K, Digital microfluidic biochips: A vision for functional diversity and more than moore, Computer-Aided Design (ICCAD), 2010 IEEE/ACM International Conference on, DOI: 10.1109/ICCAD.2010.5654199, © IEEE, November 2010. Available online at IEEE Explore.

51-10 Regina Bleul, Marion Ritzi-Lehnert, Julian Höth, Nico Scharpfenecker, Ines Frese, Dominik Düchs, Sabine Brunklaus, Thomas E. Hansen-Hagge, Franz-Josef Meyer-Almes, Klaus S. Drese, Compact, cost-efficient microfluidics-based stopped-flow device, Anal Bioanal Chem, DOI 10.1007/s00216-010-4446-5, Available online at Springer, November 2010

22-10 Krishendu Chakrabarty, Richard B. Fair and Jun Zeng, Design Tools for Digital Microfluidic Biochips Toward Functional Diversification and More than Moore, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 29, No. 7, July 2010

14-10 E. P. Furlani and M. S. Hanchak, Nonlinear analysis of the deformation and breakup of viscous microjets using the method of lines, International Journal for Numerical Methods in Fluids (2010), © 2010 John Wiley & Sons, Ltd., Published online in Wiley InterScience. DOI: 10.1002/fld.2205

55-09 R.A. Sultanov, and D. Guster, Computer simulations of pulsatile human blood flow through 3D models of the human aortic arch, vessels of simple geometry and a bifurcated artery, Proceedings of the 31st Annual International Conference of the IEEE EMBS (Engineering in Medicine and Biology Society), Minneapolis, September 2-6, 2009, p.p. 4704-4710.

30-09 Anurag Chandorkar and Shayan Palit, Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method, Sensors & Transducers journal, ISSN 1726-5479 © 2009 by IFSA, Vol.7, Special Issue “MEMS: From Micro Devices to Wireless Systems,” October 2009, pp. 136-149.

13-09 E.P. Furlani, M.C. Carter, Analysis of an Electrostatically Actuated MEMS Drop Ejector, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

12-09 A. Chandorkar, S. Palit, Simulation of Droplet-Based Microfluidics Devices Using a Volume-of-Fluid Approach, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

3-09 Christopher N. Delametter, FLOW-3D Speeds MEMS Inkjet Development, Desktop Engineering, January 2009

42-08 Tien-Li Chang, Jung-Chang Wang, Chun-Chi Chen, Ya-Wei Lee, Ta-Hsin Chou, A non-fluorine mold release agent for Ni stamp in nanoimprint process, Microelectronic Engineering 85 (2008) 1608–1612

26-08 Pamela J. Waterman, First-Pass CFD Analyses – Part 2, Desktop Engineering, November 2008

09-08 M. Ren and H. Wijshoff, Thermal effect on the penetration of an ink droplet onto a porous medium, Proc. Eurotherm2008 MNH, 1 (2008)

04-08 Delametter, Christopher N., MEMS development in less than half the time, Small Times, Online Edition, May 2008

02-08 Renat A. Sultanov, Dennis Guster, Brent Engelbrekt and Richard Blankenbecler, 3D Computer Simulations of Pulsatile Human Blood Flows in Vessels and in the Aortic Arch – Investigation of Non-Newtonian Characteristics of Human Blood, The Journal of Computational Physics, arXiv:0802.2362v1 [physics.comp-ph], February 2008

01-08 Herman Wijshoff, thesis: University of Twente, Structure- and fluid dynamics in piezo inkjet printheads, ISBN 978-90-365-2582-4, Venlo, The Netherlands January 2008.

30-07 A. K. Sen, J. Darabi, and D. R. Knapp, Simulation and parametric study of a novel multi-spray emitter for ESI–MS applications, Microfluidics and Nanofluidics, Volume 3, Number 3, June 2007, pp. 283-298(16)

28-07 Dan Soltman and Vivek Subramanian, Inkjet-Printed Line Morphologies and Temperature Control of the Coffee Ring Effect, Langmuir; 2008; ASAP Web Release Date: 16-Jan-2008; (Research Article) DOI: 10.1021/la7026847

23-07 A K Sen and J Darabi, Droplet ejection performance of a monolithic thermal inkjet print head, Journal of Micromechanical and Microengineering,vol.17, pp.1420-1427 (2007) doi:10.1088/0960-1317/17/8/002; Abstract only.

18-07 Herman Wisjhoff, Better Printheads Via Simulation, Desktop Engineering, October 2007, Vol. 13, Issue 2

17-07 Jos de Jong, Ph.D. Thesis: University of Twente, Air entrapment in piezo inkjet printing, ISBN 978-90-365-2483-4, April 2007

15-07 Krishnendu Chakrabarty and Jun Zeng, (Ed.), Design Automation Methods and Tools for Microfluidics-Based Biochips, Springer, September 2006.

14-07 Fei Su and Jun Zeng, Computer-aided design and test for digital microfluidics, IEEE Design & Test of Computers, 24(1), 2007, 60-70.

13-07 Jun Zeng, Modeling and simulation of electrified droplets and its application to computer-aided design of digital microfluidics, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(2), 2006, 224-233.

12-07 Krishnendu Chakrabarty and Jun Zeng, (2005), Automated top-down design for microfluidic biochips, ACM Journal on Emerging Technologies in Computing Systems, 1(3), 2005, 186–223.

01-07 Wijshoff, Herman, Drop formation mechanisms in piezo-acoustic inkjet, NSTI-Nanotech 2007, ISBN 1420061844 Vol. 3, 2007)

23-06 John J. Uebbing, Stephan Hengstler, Dale Schroeder, Shalini Venkatesh, and Rick Haven, Heat and Fluid Flow in an Optical Switch Bubble, Journal of Microelectromechanical Systems, Vol. 15, No. 6, December 2006

21-06 Wijshoff, Herman, Manipulating Drop Formation in Piezo Acoustic Inkjet, Proc. IS&T’s NIP22, 79 (2006)

20-06 J. de Jong, H. Reinten, M. van den Berg, H. Wijshoff, M. Versluis, G. de Bruin, A. Prosperetti and D. Lohse, Air entrapment in piezo-driven inkjet printheads, J. Acoust. Soc. Am. 120(3), 1257 (2006)

11-06 A. K. Sen, J. Darabi, D. R. Knapp and J. Liu, Modeling and Characterization of a Carbon Fiber Emitter for Electrospray Ionization, 1 MEMS and Microsystems Laboratory, Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA, 2 Department of Pharmacology, Medical University of South Carolina, Charleston, SC

5-06 E. P. Furlani, B. G. Price, G. Hawkins, and A. G. Lopez, Thermally Induced Marangoni Instability of Liquid Microjets with Application to Continuous Inkjet Printing, Proceedings of NSTI Nanotech Conference 2006, Vol. 2, pp 534-537.

28-05 O B Fawehinmi, P H Gaskell, P K Jimack, N Kapur, and H M Thompson, A combined experimental and computational fluid dynamics analysis of the dynamics of drop formation, May 2005. DOI: 10.1243/095440605X31788

5-05 E. P. Furlani, Thermal Modulation and Instability of Newtonian Liquid Microjets, presented at Nanotech 2005, Anaheim, CA, May 8-12, 2005.

1-05 C.W. Hirt, Electro-Hydrodynamics of Semi-Conductive Fluids: With Application to Electro-Spraying, Flow Science Technical Note #70, FSI-05-TN70

19-04 G. F. Yao, Modeling of Electroosmosis Without Resolving Physics Inside a Electric Double Layer, Flow Science Technical Note (FSI-04-TN69)

12-04 Jun Zeng and Tom Korsmeyer, Principles of Droplet Electrohydrodynamics for Lab-on-a-Chip, Lab. Chip. Journal, 2004, 4(4), 265-277

9-04 Constantine N. Anagnostopoulos, James M. Chwalek, Christopher N. Delametter, Gilbert A. Hawkins, David L. Jeanmaire, John A. Lebens, Ali Lopez, and David P. Trauernicht, Micro-Jet Nozzle Array for Precise Droplet Metering and Steering Having Increased Droplet Deflection, Proceedings of the 12th International Conference on Solid State Sensors, Actuators and Microsystems, sponsored by IEEE, Boston, June 8-12, 2003, pp. 368-71

8-04 Christopher N. Delametter, David P. Trauernicht, James M. Chwalek, Novel Microfluidic Jet Deflection – Significant Modeling Challenge with Great Application Potential, Technical Proceedings of the 2002 International Conference on Modeling and Simulation of Microsystems sponsored by NSTI, San Juan, Puerto Rico, April 21-25, 2002, pp. 44-47

6-04 D. Vadillo*, G. Desie**, A Soucemarianadin*, Spreading Behavior of Single and Multiple Drops, *Laboratoire des Ecoulements Geophysiques et Industriels (LEGI), and **AGFA-Gevaert Group N.V., XXI ICTAM, 15-21 August 2004, Warsaw, Poland

2-04 Herman Wijshoff, Free Surface Flow and Acousto-Elastic Interaction in Piezo Inkjet, Nanotech 2004, sponsored by the Nano Science & Technology Institute, Boston, MA, March 2004

30-03 D Souders, I Khan and GF Yao, Alessandro Incognito, and Matteo Corrado, A Numerical Model for Simulation of Combined Electroosmotic and Pressure Driven Flow in Microdevices, 7th International Symposium on Fluid Control, Measurement and Visualization

27-03 Jun Zeng, Daniel Sobek and Tom Korsmeyer, Electro-Hydrodynamic Modeling of Electrospray Ionization – CAD for a µFluidic Device-Mass Spectrometer Interface, Agilent Technologies Inc, paper presented at Transducers 2003, June 03 Boston (note: Reference #10 is to FLOW-3D)

17-03 John Uebbing, Switching Fiber-optic Circuits with Microscopic Bubbles, Sensors Magazine, May 2003, Vol 20, No 5, p 36-42

16-03 CFD Speeds Development of MEMS-based Printing Technology, MicroNano Magazine, June 2003, Vol 8, No 6, p 16

3-03 Simulation Speeds Design of Microfluidic Medical Devices, R&D Magazine, March 2003, pp 18-19

1-03 Simulations Help Microscopic Bubbles Switch Fiber-Optic Circuits, Agilent Technologies, Fiberoptic Product News, January 2003, pp 22-23

27-02 Feng, James Q., A General Fluid Dynamic Analysis of Drop Ejection in Drop-on-Demand Ink Jet Devices, Journal of Imaging Science and Technology®, Volume 46, Number 5, September/October 2002

1-02 Feixia Pan, Joel Kubby, and Jingkuang Chen, Numerical Simulation of Fluid Structure Interaction in a MEMS Diaphragm Drop Ejector, Xerox Wilson Research Center, Institute of Physics Publishing, Journal of Micromechanics and Microengineering, 12 (2002), PII: SO960-1317(02)27439-2, pp. 70-76

48-01 Rainer Gruber, Radial Mass Transfer Enhancement in Bubble-Train Flow, PhD thesis in Engineering Sciences, Rheinisch- Westf alischen Technische Hochschule Aachen, December 2001.

34-01 Furlani, E.P., Delametter, C.N., Chwalek, J.M., and Trauernicht, D., Surface Tension Induced Instability of Viscous Liquid Jets, Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

12-01 C. N. Delametter, Eastman Kodak Company, Micro Resolution, Mechanical Engineering, Col 123/No 7, July 2001, pp 70-72

11-01 C. N. Delametter, Eastman Kodak Company, Surface Tension Induced Instability of Viscous Liquid Jets, Technical Proceeding of the Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

9-01 Aman Khan, Unipath Limited Research and Development, Effects of Reynolds Number on Surface Rolling in Small Drops, PVP-Col 431, Emerging Technologies for Fluids, Structures and Fluids, Structures and Fluid Structure Interaction — 2001

2-00 Narayan V. Deshpande, Significance of Inertance and Resistance in Fluidics of Thermal Ink-Jet Transducers, Journal of Imaging Science and Technology, Volume 40, Number 5, Sept./Oct. 1996, pp.457-461

4-98 D. Deitz, Connecting the Dots with CFD, Mechanical Engineering Magazine, pp. 90-91, March 1998

14-94 M. P. O’Hare, N. V. Deshpande, and D. J. Drake, Drop Generation Processes in TIJ Printheads, Xerox Corporation, Adv. Imaging Business Unit, IS&T’s Tenth International Congress on Advances in Non-Impact Printing, Tech. 1994

14-92 Asai, A.,Three-Dimensional Calculation of Bubble Growth and Drop Ejection in a Bubble Jet Printer, Journal of Fluids Engineering Vol. 114 December 1992:638-641

FLOW-3D/MP Features List

FLOW-3D/MP Features

FLOW-3D/MP v6.1 은 FLOW-3D v11.1 솔버에 기초하여 물리 모델, 특징 및 그래픽 사용자 인터페이스가 동일합니다. FLOW-3D v11.1의 새로운 기능은 아래 파란색으로 표시되어 있으며 FLOW-3D/MP v6.1 에서 사용할 수 있습니다. 새로운 개발 기능에 대한 자세한 설명은 FLOW-3D v11.1에서 새로운 기능을 참조하십시오.

Meshing & Geometry

Structured finite difference/control volume meshes for fluid and thermal solutions
Finite element meshes in Cartesian and cylindrical coordinates for structural analysis
Multi-Block gridding with nested, linked, partially overlapping and conforming mesh blocks
Fractional areas/volumes (FAVOR™) for efficient & accurate geometry definition
Mesh quality checking
Basic Solids Modeler
Import CAD data
Import/export finite element meshes via Exodus-II file format
Grid & geometry independence
Cartesian or cylindrical coordinates

Flow Type Options

Internal, external & free-surface flows
3D, 2D & 1D problems
Transient flows
Inviscid, viscous laminar & turbulent flows
Hybrid shallow water/3D flows
Non-inertial reference frame motion
Multiple scalar species
Two-phase flows
Heat transfer with phase change
Saturated & unsaturated porous media

Physical Modeling Options

Fluid structure interaction
Thermally-induced stresses
Plastic deformation of solids
Granular flow
Moisture drying
Solid solute dissolution
Sediment transport and scour
Cavitation (potential, passive tracking, active tracking)
Phase change (liquid-vapor, liquid-solid)
Surface tension
Thermocapillary effects
Wall adhesion
Wall roughness
Vapor & gas bubbles
Solidification & melting
Mass/momentum/energy sources
Shear, density & temperature-dependent viscosity
Thixotropic viscosity
Visco-elastic-plastic fluids
Elastic membranes & walls
Evaporation residue
Electro-mechanical effects
Dielectric phenomena
Electro-osmosis
Electrostatic particles
Joule heating
Air entrainment
Molecular & turbulent diffusion
Temperature-dependent material properties
Spray cooling

Flow Definition Options

General boundary conditions
- Symmetry
- Rigid and flexible walls
- Continuative
- Periodic
- Specified pressure
- Specified velocity
- Outflow
- Grid overlay
- Hydrostatic pressure
- Volume flow rate
- Non-linear periodic and solitary surface waves
- Rating curve and natural hydraulics
- Wave absorbing layer
Restart from previous simulation
Continuation of a simulation
Overlay boundary conditions
Change mesh and modeling options
Change model parameters

Thermal Modeling Options

Natural convection
Forced convection
Conduction in fluid & solid
Fluid-solid heat transfer
Distributed energy sources/sinks in fluids and solids
Radiation
Viscous heating
Orthotropic thermal conductivity
Thermally-induced stresses

Turbulence Models

RNG model
Two-equation k-epsilon model
Two-equation k-omega model
Large eddy simulation

Metal Casting Models

Thermal stress & deformations
Iron solidification
Sand core blowing
Sand core drying
Permeable molds
Solidification & melting
Solidification shrinkage with interdendritic feeding
Micro & macro porosity
Binary alloy segregation
Thermal die cycling
Surface oxide defects
Cavitation potential
Lost-foam casting
Semi-solid material
Core gas generation
Back pressure & vents
Shot sleeves
PQ² diagram
Squeeze pins
Filters
Air entrainment
Temperature-dependent material properties
Cooling channels
Fluid/wall contact time

Numerical Modeling Options

TruVOF Volume-of-Fluid (VOF) method for fluid interfaces
First and second order advection
Sharp and diffuse interface tracking
Implicit & explicit numerical methods
GMRES, point and line relaxation pressure solvers
User-defined variables, subroutines & output
Utilities for runtime interaction during execution

Fluid Modeling Options

One incompressible fluid – confined or with free surfaces
Two incompressible fluids – miscible or with sharp interfaces
Compressible fluid – subsonic, transonic, supersonic
Stratified fluid
Acoustic phenomena
Mass particles with variable density or diameter

Shallow Flow Models

General topography
Raster data interface
Subcomponent-specific surface roughness
Wind shear
Ground roughness effects
Laminar & turbulent flow
Sediment transport and scour
Surface tension
Heat transfer
Wetting & drying

Advanced Physical Models

General Moving Object model with 6 DOF–prescribed and fully-coupled motion
Rotating/spinning objects
Collision model
Tethered moving objects (springs, ropes, mooring lines)
Flexing membranes and walls
Porosity
Finite element based elastic-plastic deformation
Finite element based thermal stress evolution due to thermal changes in a solidifying fluid
Combusting solid components

Chemistry Models

Stiff equation solver for chemical rate equations
Stationary or advected species

Porous Media Models

Saturated and unsaturated flow
Variable porosity
Directional porosity
General flow losses (linear & quadratic)
Capillary pressure
Heat transfer in porous media
Van Genunchten model for unsaturated flow

Discrete Particle Models

Massless marker particles
Mass particles of variable size/mass
Linear & quadratic fluid-dynamic drag
Monte-Carlo diffusion
Particle-Fluid momentum coupling
Coefficient of restitution or sticky particles
Point or volumetric particle sources
Charged particles
Probe particles

Two-Phase & Two-Component Models

Liquid/liquid & gas/liquid interfaces
Variable density mixtures
Compressible fluid with a dispersed incompressible component
Drift flux
Two-component, vapor/non-condensable gases
Phase transformations for gas-liquid & liquid-solid
Adiabatic bubbles
Bubbles with phase change
Continuum fluid with discrete particles
Scalar transport
Homogeneous bubbles
Super-cooling

Coupling with Other Programs

Geometry input from Stereolithography (STL) files – binary or ASCII
Direct interfaces with EnSight^®, FieldView^® & Tecplot^® visualization software
Finite element solution import/export via Exodus-II file format
PLOT3D output
Neutral file output
Extensive customization possibilities
Solid Properties Materials Database

Data Processing Options

State-of-the-art post-processing tool, FlowSight™
Batch post-processing
Report generation
Automatic or custom results analysis
High-quality OpenGL-based graphics
Color or B/W vector, contour, 3D surface & particle plots
Moving and stationary probes
Measurement baffles
Arbitrary sampling volumes
Force & moment output
Animation output
PostScript, JPEG & Bitmap output
Streamlines
Flow tracers

User Conveniences

Active simulation control (based on measurement of probes)
Mesh generators
Mesh quality checking
Tabular time-dependent input using external files
Automatic time-step control for accuracy & stability
Automatic convergence control
Mentor help to optimize efficiency
Change simulation parameters while solver runs
Launch and manage multiple simulations
Automatic simulation termination based on user-defined criteria
Run simulation on remote servers using remote solving

Multi-Processor Computing

FLOW-3D Features

The features in blue are newly-released in FLOW-3D v12.0.

Meshing & Geometry

Structured finite difference/control volume meshes for fluid and thermal solutions
Finite element meshes in Cartesian and cylindrical coordinates for structural analysis
Multi-Block gridding with nested, linked, partially overlapping and conforming mesh blocks
Conforming meshes extended to arbitrary shapes
Fractional areas/volumes (FAVOR™) for efficient & accurate geometry definition
Closing gaps in geometry
Mesh quality checking
Basic Solids Modeler
Import CAD data
Import/export finite element meshes via Exodus-II file format
Grid & geometry independence
Cartesian or cylindrical coordinates

Flow Type Options

Internal, external & free-surface flows
3D, 2D & 1D problems
Transient flows
Inviscid, viscous laminar & turbulent flows
Hybrid shallow water/3D flows
Non-inertial reference frame motion
Multiple scalar species
Two-phase flows
Heat transfer with phase change
Saturated & unsaturated porous media

Physical Modeling Options

Fluid structure interaction
Thermally-induced stresses
Plastic deformation of solids
Granular flow
Moisture drying
Solid solute dissolution
Sediment transport and scour
Sludge settling
Cavitation (potential, passive tracking, active tracking)
Phase change (liquid-vapor, liquid-solid)
Surface tension
Thermocapillary effects
Wall adhesion
Wall roughness
Vapor & gas bubbles
Solidification & melting
Mass/momentum/energy sources
Shear, density & temperature-dependent viscosity
Thixotropic viscosity
Visco-elastic-plastic fluids
Elastic membranes & walls
Evaporation residue
Electro-mechanical effects
Dielectric phenomena
Electro-osmosis
Electrostatic particles
Joule heating
Air entrainment
Molecular & turbulent diffusion
Temperature-dependent material properties
Spray cooling

Flow Definition Options

General boundary conditions
- Symmetry
- Rigid and flexible walls
- Continuative
- Periodic
- Specified pressure
- Specified velocity
- Outflow
- Outflow pressure
- Outflow boundaries with wave absorbing layers
- Grid overlay
- Hydrostatic pressure
- Volume flow rate
- Non-linear periodic and solitary surface waves
- Rating curve and natural hydraulics
- Wave absorbing layer
Restart from previous simulation
Continuation of a simulation
Overlay boundary conditions
Change mesh and modeling options
Change model parameters

Thermal Modeling Options

Natural convection
Forced convection
Conduction in fluid & solid
Fluid-solid heat transfer
Distributed energy sources/sinks in fluids and solids
Radiation
Viscous heating
Orthotropic thermal conductivity
Thermally-induced stresses

Numerical Modeling Options

TruVOF Volume-of-Fluid (VOF) method for fluid interfaces
Steady state accelerator for free-surface flows
First and second order advection
Sharp and diffuse interface tracking
Implicit & explicit numerical methods
Immersed boundary method
GMRES, point and line relaxation pressure solvers
User-defined variables, subroutines & output
Utilities for runtime interaction during execution

Fluid Modeling Options

One incompressible fluid – confined or with free surfaces
Two incompressible fluids – miscible or with sharp interfaces
Compressible fluid – subsonic, transonic, supersonic
Stratified fluid
Acoustic phenomena
Mass particles with variable density or diameter

Shallow Flow Models

General topography
Raster data interface
Subcomponent-specific surface roughness
Wind shear
Ground roughness effects
Manning’s roughness
Laminar & turbulent flow
Sediment transport and scour
Surface tension
Heat transfer
Wetting & drying

Turbulence Models

RNG model
Two-equation k-epsilon model
Two-equation k-omega model
Large eddy simulation

Advanced Physical Models

General Moving Object model with 6 DOF–prescribed and fully-coupled motion
Rotating/spinning objects
Collision model
Tethered moving objects (springs, ropes, breaking mooring lines)
Flexing membranes and walls
Porosity
Finite element based elastic-plastic deformation
Finite element based thermal stress evolution due to thermal changes in a solidifying fluid
Combusting solid components

Chemistry Models

Stiff equation solver for chemical rate equations
Stationary or advected species

Porous Media Models

Saturated and unsaturated flow
Variable porosity
Directional porosity
General flow losses (linear & quadratic)
Capillary pressure
Heat transfer in porous media
Van Genunchten model for unsaturated flow

Discrete Particle Models

Massless marker particles
Multi-species material particles of variable size and mass
Solid, fluid, gas particles
Void particles tracking collapsed void regions
Non-linear fluid-dynamic drag
Added mass effects
Monte-Carlo diffusion
Particle-fluid momentum coupling
Coefficient of restitution or sticky particles
Point or volumetric particle sources
Initial particle blocks
Heat transfer with fluid
Evaporation and condensation
Solidification and melting
Coulomb and dielectric forces
Probe particles

Two-Phase & Two-Component Models

Liquid/liquid & gas/liquid interfaces
Variable density mixtures
Compressible fluid with a dispersed incompressible component
Drift flux with dynamic droplet size
Two-component, vapor/non-condensable gases
Phase transformations for gas-liquid & liquid-solid
Adiabatic bubbles
Bubbles with phase change
Continuum fluid with discrete particles
Scalar transport
Homogeneous bubbles
Super-cooling
Two-field temperature

Coupling with Other Programs

Geometry input from Stereolithography (STL) files – binary or ASCII
Direct interfaces with EnSight^®, FieldView^® & Tecplot^® visualization software
Finite element solution import/export via Exodus-II file format
PLOT3D output
Neutral file output
Extensive customization possibilities
Solid Properties Materials Database

Data Processing Options

State-of-the-art post-processing tool, FlowSight™
Batch post-processing
Report generation
Automatic or custom results analysis
High-quality OpenGL-based graphics
Color or B/W vector, contour, 3D surface & particle plots
Moving and stationary probes
Visualization of non-inertial reference frame motion
Measurement baffles
Arbitrary sampling volumes
Force & moment output
Animation output
PostScript, JPEG & Bitmap output
Streamlines
Flow tracers

User Conveniences

Active simulation control (based on measurement of probes)
Mesh generators
Mesh quality checking
Tabular time-dependent input using external files
Automatic time-step control for accuracy & stability
Automatic convergence control
Mentor help to optimize efficiency
Units on all variables
Custom units
Component transformations
Moving particle sources
Change simulation parameters while solver runs
Launch and manage multiple simulations
Automatic simulation termination based on user-defined criteria
Run simulation on remote servers using remote solving
Copy boundary conditions to other mesh blocks

Multi-Processor Computing

Shared memory computers
Distributed memory clusters

FlowSight

Particle visualization
Velocity vector fields
Streamlines & pathlines
Iso-surfaces
2D, 3D and arbitrary clips
Volume render
Probe data
History data
Vortex cores
Link multiple results
Multiple data views
Non-inertial reference frame
Spline clip

MEMS/WELD 분야

Microfluidics

Microfluidics는 집적 회로 산업에서 사용되는 것과 유사한 공정을 사용하여 소형 기기의 제조에 급격하게 성장하는 기술입니다. Microfluidics 기술은 0.1 미크론에서 1mm에 이르기까지 매우 작은 장치로 기계, 유체, 광학, 전자 기능을 통합 할 수있는 방법을 제공합니다. Microfluidics는 기존의 방법과 비교하면 두 가지 중요한 장점이 있습니다. 첫째, 대량으로 제조 될 수 있으므로, 생산의 비용이 실질적으로 감소 될 수 있습니다. 둘째, 집적 회로에 통합 될 수 있어서 다른 기술보다 훨씬 더 복잡한 시스템으로 제조 될 수 있습니다.

Chip packaging simulation. Results generated by FLOW-3D/MP, FLOW-3D‘s HPC solution.

엔지니어 및 과학자가 설계, 시험 제작하고 그 성능을 최적화하기 위해 장치를 재 설계하는 등, 다른 제조 방법에서와 같이 microfluidics 설계 프로세스는 매우 고가 일 수 있습니다. 그러나, 수치 시뮬레이션은 전자, 기계, 화학, 열 과학 및 유체 과학 등의 분야에 걸쳐 정량 분석과 중요한 통찰력을 제공 할 수 있습니다.

laser-sintering

Downloads

FLOW-3D Microfluidics Brochure

Bibliography

Models

Case Studies

자동차 분야

Automotive

Nozzle filling simulation. Courtesy Reutter Group

FLOW-3D는 자동차 산업에서 직면할 수 있는 많은 문제에 대한 해법을 제공하는 포괄적인 CFD 소프트웨어입니다. FLOW-3D는 과도적인 흐름 동역학(자유 표면과 한정된 유체 모두), 유체와 고체 간의 열전달, 상 변화, 고체의 6자유도 운동, 기계적 및 열로 유도된 응력에 대한 결합된 유한 요소 해석 등을 할 수 있습니다. 자세한 내용은 FLOW-3D의 모델링 기능의 전체 목록을 살펴보십시오.

자동차 분야의 시뮬레이션 대상 분야로는 연료 탱크 슬로 싱, 언더 후드 열 관리, 분사 제어, 조기 연료 차단, 자동차 부품의 도장, 용기의 가스 제거, 파워 트레인 부품의 유체 저항, 자동차 부품 주조 등의 주조품 및 주조 공정의 더 나은 설계를 위해 도움을 줄 수 있는 몇 가지 영역들이 있습니다.

자동차분야 해석 사례

Bibliography

Models

항공/우주 분야

Aerospace

항공 우주 분야에서 연구하는 엔지니어를 위해 FLOW-3D는 정확한 액체/가스 인터페이스(자유 표면) 모델링, 열 솔루션을 사용하여 연료 안정성 확보, 극저온 온도 조절, PMD(Propellent management devices), 캐비테이션 및 전하 분포에 대한 귀중한 통찰력을 제공합니다. 위상 및 정전기 물리 모델을 사용합니다.

항공 우주 분야에서 FLOW-3D의 성공적인 사용을 보여주는 기술 문서로 이동하기

Aerospace Simulations

FLOW-3D 는 sloshing, 무중력 유체역학(zero gravity fluid dynamics), 다상유동(multi-phase fluids), 탄성 멤브레인(elastic membranes), 음속 및 초음속 상태에서 노즐(nozzles in subsonic and supersonic conditions), 유체구조의 상호 작용(fluid structure interactions) 등 항공분야에서 볼 수 있는 자연현상을 정확하게 표현하기 위해 자유표면 알고리즘을 고려하고 있습니다.

Bibliography

Models

Conference Proceedings

[태그:] Surface Tension

ABSTRACT

키워드

BIBLIOGRAPHY

Coupled thermodynamic-fluid-dynamic solution for a liquid-hydrogen tank

Introduction

Modeling of Mesh Screen for Use in Surface TensionTankUsing Flow-3d Software

ABSTRACT

초 록

Key Words

서론

참 고 문 헌

Optimization of processing parameters for pulsed laser welding of dissimilar metal interconnects

Abstract

References

Effects of process parameters and cooling gas on powder formation during the plasma rotating electrode process

Highlights

Abstract

Keywords

Introduction

References

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

Abstract

Keywords

ABSTRACT

CONCLUSION

REFERENCES

Abstract

HIGHLIGHTS

Keywords

INTRODUCTION

METHOD

Experimental setup

Numerical simulation

RESULTS AND DISCUSSION

Experimental results

Numerical results

CONCLUSION

DATA AVAILABILITY STATEMENT

REFERENCES

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

Abstract

Keywords

References

Abstract

References

낮은 레이놀즈 수 유압 점프의 수치 모델링에서 OpenFOAM 및 FLOW-3D의 성능 평가

Abstract

Keywords

References

3차원 셀별 보수 미분할 기하학적 VOF 체계

Abstract

Keywords

전산 유체 역학을 사용하여 GMAW에 대한 Marangoni 흐름에서 슬래그 이동의 수치 시뮬레이션

Abstract

Abstract

References

알루미늄 리본과 구리 시트의 레이저 용접에 대한 조사 : 용접성, 미세 구조, 기계적 및 전기적 특성

Introduction

본문 내용 생략 : 문서 하단부의 원문보기를 참고하시기 바랍니다.

분사성 작동 조건을 최적화하기 위한 결합된 CFD-Response Surface Method(RSM)

소개

기판 냉각 및 액적 모양 및 조성이 액적 증발 및 입자 증착에 미치는 영향

Bibliography

A Study on the Simulation of the Resolution for Ink-Jet Printing

초록

자유 표면 모델링 방법

Lagrangian Grid Methods

라그랑주 격자 법

Surface Height Method

표면 높이 법

Marker-and-Cell (MAC) Method

MAC 방법

Surface Marker Method

표면 마커 법

Volume-of-Fluid (VOF) Method

VOF (Volume-of-Fluid) 법

Variable-Density Approximation to the VOF Method

VOF 법의 가변 밀도 근사

Summary