High-Order Moment-Encoded Kinetic Simulation of Turbulent Flows

Wei Li, Tongtong Wang, Zherong Pan, Xifeng Gao, Kui Wu, Mathieu Desbrun
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Abstract

Kinetic solvers for incompressible fluid simulation were designed to run efficiently on massively parallel architectures such as GPUs. While these lattice Boltzmann solvers have recently proven much faster and more accurate than the macroscopic Navier-Stokes-based solvers traditionally used in graphics, it systematically comes at the price of a very large memory requirement: a mesoscopic discretization of statistical mechanics requires over an order of magnitude more variables per grid node than most fluid solvers in graphics. In order to open up kinetic simulation to gaming and simulation software packages on commodity hardware, we propose a HighOrder Moment-Encoded Lattice-Boltzmann-Method solver which we coined HOME-LBM, requiring only the storage of a few moments per grid node, with little to no loss of accuracy in the typical simulation scenarios encountered in graphics. We show that our lightweight and lightspeed fluid solver requires three times less memory and runs ten times faster than state-of-the-art kinetic solvers, for a nearly-identical visual output.
湍流的高阶动量编码动力学模拟
不可压缩流体模拟的动力学求解器是为了在gpu等大规模并行架构上高效运行而设计的。虽然这些晶格玻尔兹曼解算器最近被证明比传统图形中使用的基于navier - stokes的宏观解算器更快更准确,但它系统地以非常大的内存需求为代价:统计力学的介观离散化比图形中的大多数流体解算器在每个网格节点上需要超过一个数量级的变量。为了将动力学仿真开放给商品硬件上的游戏和仿真软件包,我们提出了一种高阶矩编码晶格-玻尔兹曼方法求解器,我们创造了HOME-LBM,只需要存储每个网格节点的几个矩,在图形中遇到的典型仿真场景中几乎没有精度损失。我们表明,我们的轻量级和光速流体求解器需要比最先进的动力学求解器少三倍的内存,运行速度快十倍,几乎相同的视觉输出。
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