4.7 Article

A GPU-accelerated adaptive particle refinement for multi-phase flow and fluid-structure coupling SPH

期刊

OCEAN ENGINEERING
卷 279, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2023.114514

关键词

Smoothed Particle Hydrodynamics; Adaptive particle refinement; GPU; Multi-phase flow; Fluid-structure coupling

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In this paper, a GPU implementation of adaptive particle refinement (APR) in the SPH framework is used to simulate multi-phase flow and fluid-structure coupling. The authors present SPH models for multi-phase flow and fluid-structure coupling based on the Riemann solver and use a local particle refinement technique to split particles. They propose a particle shifting technique to regularize the particle distribution and use a dynamic resource management algorithm for efficient GPU-accelerated APR. Precision analysis shows the effect of fine and coarse particle smooth length ratio on numerical accuracy. Tests demonstrate that the proposed GPU-accelerated APR is accurate and stable with low computational cost and comparable precision compared to uniform particles.
In this paper, a graphical processing unit (GPU) implementation of adaptive particle refinement (APR) in the Smoothed Particle Hydrodynamics (SPH) framework is used to simulate the multi-phase flow and fluid-structure coupling problems. The multi-phase flow and fluid-structure coupling SPH models based on the Riemann solver are presented and the local particle refinement technique based on the axis-aligned square refinement pattern is adopted to split particles. A particle shifting technique constructing virtual fine particles (VFP-PST) is proposed to regularize the particle distribution for numrical accuracy and stability. To facilitate the device memory throughput efficiently, a dynamic resource management algorithm is used to implement GPU-accelerated adaptive particle refinement. Precision analysis shows the effect of the smooth length ratio of fine and coarse particles on numerical accuracy. Different tests have demonstrated that the proposed GPU-accelerated IAPR is accurate and stable and provides a promising approach to simulate the multi-phase flow and fluid-structure problems with low computational cost and comparable precision when compared with uniform particles.

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