The University of Tokyo · 공학
광태오 교수 연구실은 라그랑주 기반 입자법, 특히 이동 입자 준확실험법(MPS)을 중심으로 자유면 유동, 다상유동, 기화 현상 등 복잡한 유체 역학 문제를 정확하고 안정적으로 시뮬레이션하는 데 중점을 두고 있습니다. 특히 입자 분포의 비균형성과 경계 조건에 의한 오차 문제를 해결하기 위해 보정 행렬 기반의 정합성 향상 기법과 자유면에서의 안정성 확보 전략을 개발하고 있습니다. 또한, 압력 포isson 방정식의 병렬 해법 최적화를 통한 계산 효율성 향상 연구도 진행 중입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Summary The Lagrangian moving particle semi‐implicit (MPS) method has potential to simulate free‐surface and multiphase flows. However, the chaotic distribution of particles can decrease accuracy and reliability in the conventional MPS method. In this study, a new Laplacian model is proposed by removing the errors associated with first‐order partial derivatives based on a corrected matrix. Therefore, a corrective matrix is applied to all the MPS discretization models to enhance computational acc
Modeling the boiling phase change is particularly challenging for Lagrangian particle methods due to a high density ratio and dramatic volume expansion. In this study, the incompressible moving particle semi-implicit (MPS) method and the weakly compressible smoothed particle hydrodynamics (SPH) method are coupled to develop an incompressible–compressible particle method for modeling a multiphase flow with boiling. The coupling strategies developed by Lind et al. (JCP, 2016) are adopted. A high s
Summary Corrective matrix that is derived to restore consistency of discretization schemes can significantly enhance accuracy for the inside particles in the Moving Particle Semi‐implicit method. In this situation, the error due to free surface and wall boundaries becomes dominant. Based on the recent study on Neumann boundary condition (Matsunaga et al, CMAME, 2020), the corrective matrix schemes in MPS are generalized to straightforwardly and accurately impose Neumann boundary condition. Howev
This study investigates the instability issue at a free surface when the consistent schemes based on variable differences are applied in semi-implicit particle methods. A semi-analytical error-analysis method is proposed to clarify how the incomplete/biased neighbor support triggers error accumulation and instability. Specifically, the discretization models are decomposed into the center-variable components (CVCs) and neighbor-variable components (NVCs). The influence of different components on
Viscosity is an important property of fluids but it is not easy to simulate, especially for flows where viscous forces are dominant or comparable with other forces, because numerical viscosity may interfere. The paper mainly discusses the effect of setting up time step and space step on the stability and accuracy of the viscosity term in the moving particle semi-implicit (MPS) method. Two principles, the stability condition of the viscosity term and the accuracy condition of the viscosity term,
Purpose – The purpose of this paper is to find the best solver for parallelizing particle methods based on solving Pressure Poisson Equation (PPE) by taking Moving Particle Semi-Implicit (MPS) method as an example because the solution for PPE is usually the most time-consuming part difficult to parallelize. Design/methodology/approach – To find the best solver, the authors compare six Krylov solvers, namely, Conjugate Gradient method (CG), Scaled Conjugate Gradient method (SCG), Bi-Conjugate Gra
