A highly accurate GPU Lattice Boltzmann method with directional interpolation for the probability distribution functions,International Journal for Numerical Methods in Fluids

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A highly accurate GPU Lattice Boltzmann method with directional interpolation for the probability distribution functions
International Journal for Numerical Methods in Fluids ( IF 1.7 ) Pub Date : 2020-05-10 , DOI: 10.1002/fld.4848
Arturo Delgado‐Gutiérrez ₁ , Pier Marzocca ₂ , Diego Cárdenas ₃ , Oliver Probst ₁

Affiliation

In this article, a highly accurate and graphics processing unit (GPU)‐accelerated Lattice Boltzmann Method (LBM) is presented. The methodology is derived from a combination of conventional and recent LBM algorithms, mainly focusing on reducing the computational time, memory allocation, and complexity of existing algorithms. The general implementation focuses on accelerating the overall methodology using GPGPU technology based on Compute Shaders from OpenGL and avoids the storage of the distribution function components to reduce the memory allocation size. Furthermore, an efficient spatial interpolation of the probability distribution function components is described, based on a directional interpolation, without unnecessary control points for the reconstruction of virtual nodes data. The present methodology, tested for spatial accuracy via two‐ and three‐dimensional Lid‐Driven Cavity benchmark cases, shows excellent agreement with the results reported in the literature. Additionally, time efficiency is analyzed by comparing different configurations for the construction of virtual streaming points.

中文翻译：

具有方向插值的高精度GPU Lattice Boltzmann方法用于概率分布函数

本文介绍了一种高度精确的图形处理单元（GPU）加速的格子Boltzmann方法（LBM）。该方法是从传统LBM算法和最新LBM算法的组合中得出的，主要集中在减少计算时间，内存分配和现有算法的复杂性上。一般的实现方式着重于使用基于OpenGL的Compute Shaders的GPGPU技术加速总体方法，并避免存储分配功能组件以减小内存分配大小。此外，基于定向插值，描述了概率分布函数分量的有效空间插值，而没有用于虚拟节点数据的重构的不必要的控制点。目前的方法通过二维和三维盖驱动腔基准案例对空间精度进行的测试表明，与文献报道的结果非常吻合。此外，通过比较虚拟流点构建的不同配置来分析时间效率。

更新日期：2020-05-10

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