SC19 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Poster 65: Comparing Granular Dynamics vs. Fluid Dynamics via Large DOF-Count Parallel Simulation on the GPU

Authors: Milad Rakhsha (University of Wisconsin), Conlain Kelly (Georgia Institute of Technology), Nicholas Olsen (University of Wisconsin), Lijing Yang (University of Wisconsin), Radu Serban (University of Wisconsin), Dan Negrut (University of Wisconsin)

Abstract: In understanding granular dynamics, the commonly-used discrete modeling approach that tracks the motion of all particles is computationally demanding, especially with large system size. In such cases, one can contemplate switching to continuum models that are computationally less expensive. In order to assess when such a discrete to continuum switch is justified, we compare granular and fluid dynamics that scales to handle more than 1 billion degrees of freedom (DOFs); i.e., two orders of magnitude higher than the state-of-the-art. On the granular side, we solve the Newton-Euler equations of motion; on the fluid side, we solve the Navier-Stokes equations. Both solvers leverage parallel computing on the GPU, and are publicly available on GitHub as part of an open-source code called Chrono. We report similarities and differences between the dynamics of the discrete, fully-resolved system and the continuum model via numerical experiments including both static and highly transient scenarios.

Best Poster Finalist (BP): no

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