GAMES Webinar 2020 – 162期(仿真模拟专题) | Yu Fang, Ziyin Qu (University of Pennsylvania), Wei Li (ShanghaiTech University)

【GAMES Webinar 2020-162期】(仿真模拟专题)

报告嘉宾1:Yu Fang, Ziyin Qu (University of Pennsylvania)


报告题目:IQ-MPM: An Interface Quadrature Material Point Method for Non-sticky Strongly Two-Way Coupled Nonlinear Solids and Fluids


We propose a novel scheme for simulating two-way coupled interactions between nonlinear elastic solids and incompressible fluids. The key ingredient of this approach is a ghost matrix operator-splitting scheme for strongly coupled nonlinear elastica and incompressible fluids through the weak form of their governing equations. This leads to a stable and efficient method handling large time steps under the CFL limit while using a single monolithic solve for the coupled pressure fields, even in the case with highly nonlinear elastic solids.


Yu Fang is a third-year Ph.D. student majoring in Computer and Information Science at University of Pennsylvania. He is advised by Prof. Chenfanfu Jiang. Yu Fang graduated from Tsinghua University in July 2018. His primary research interest is physically based simulation. Currently, he has been working on utilizing material point method to simulate different phenomena and developing novel technology to accelerate existing simulation framework.

Ziyin Qu us a Second year Ph.D student at University of Pennsylvania, under the supervision of Professor Chenfanfu Jiang. His reserach interests are mainly physics-based simulation for computer graphics, especially focusing on exploring new schemes in fluid simulation.


报告嘉宾2:Wei Li (ShanghaiTech University)


报告题目:Fast and Scalable Turbulent Flow Simulation with Two-Way Coupling


Despite their cinematic appeal, turbulent flows involving fluid-solid coupling remain a computational challenge in animation. At the root of this current limitation is the numerical dispersion from which most accurate Navier-Stokes solvers suffer: proper coupling between fluid and solid often generates artificial dispersion in the form of local, parasitic trains of velocity oscillations, eventually leading to numerical instability. While successive improvements over the years have led to conservative and detail-preserving fluid integrators, the dispersive nature of these solvers is rarely discussed despite its dramatic impact on fluid-structure interaction. In this paper, we introduce a novel low-dissipation and low-dispersion fluid solver that can simulate two-way coupling in an efficient and scalable manner, even for turbulent flows. In sharp contrast with most current CG approaches, we construct our solver from a kinetic formulation of the flow derived from statistical mechanics. Unlike existing lattice Boltzmann solvers, our approach leverages high-order moment relaxations as a key to controlling both dissipation and dispersion of the resulting scheme. Moreover, we combine our new fluid solver with the immersed boundary method to easily handle fluid-solid coupling through time adaptive simulations. Our kinetic solver is highly parallelizable by nature, making it ideally suited for implementation on single- or multi-GPU computing platforms. Extensive comparisons with existing solvers on synthetic tests and real-life experiments are used to highlight the multiple advantages of our work over traditional and more recent approaches, in terms of accuracy, scalability, and efficiency.


I am a PhD candidate in the School of Information Science & Technology of ShanghaiTech University in China, supervised by Prof. Xiaopei Liu, and majoring in computer graphics, with a particular focus on fluid simulations. Differing from traditional fluid simulation, my research is multi-disciplinary, covering many aspects in computer science and computational physics, as well as some artificial intelligence. During my PhD study, I have closely collaborated with Prof. Mathieu Desbrun from Caltech and Prof. Changxi Zheng from Columbia University for fluid simulation. I received my BS degree in computer science from Northwestern Polytechnical University, China, supervised by Prof. Yong Xia in 2015.




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