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

Qiuzu Yang, Fei Xu, Yang Yang, Zhen Dai, Jiayi Wang

科研成果: 期刊稿件文章同行评审

17 引用 (Scopus)

摘要

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.

源语言英语
文章编号114514
期刊Ocean Engineering
279
DOI
出版状态已出版 - 1 7月 2023

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