The University of Tokyo · Engineering
Professor Guangtao Duan's research lab specializes in the development and enhancement of meshfree particle methods, particularly the Moving Particle Semi-implicit (MPS) and Smoothed Particle Hydrodynamics (SPH) methods, for complex fluid dynamics simulations. The lab focuses on improving numerical accuracy and stability in simulating challenging multiphase flows, including free-surface flows, boiling phase change, and high-density ratio flows, by developing advanced corrective matrix schemes and consistent discretization models. Key research directions include boundary condition treatment, error analysis for particle instability, and efficient parallel computing for large-scale simulations.
Figures are computed from collected data and may differ slightly.
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
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