In this research, a Graphical Processing Unit (GPU) accelerated Discrete Element Method (DEM) code was developed and coupled with the Computational Fluid Dynamic (CFD) software MFiX to simulate granular and multiphase flows. The Fortran-based CFD solver was coupled with CUDA/C++ based DEM solver through inter-process pipes. The speedup to CPU is about 130 to 243 folds in the simulation of particle packings. In fluidized bed simulations, the DEM computation time is reduced from 91% to 17% with a speedup of 78 folds. The simulation of Geldart A particle fluidization revealed a similar level of importance of both fluid and particle coarse-graining. The filtered drag derived from the two-fluid model is suitable for Euler-Lagrangian simulations with both fluid and particle coarse-graining. It overcorrects the influence of sub-grid structures if used for simulations with only fluid coarse-graining.

在这项研究中，开发了图形处理单元 (GPU) 加速离散元法 (DEM) 代码，并结合计算流体动力学 (CFD) 软件 MFiX 来模拟颗粒流和多相流。基于 Fortran 的 CFD 求解器通过进程间管道与基于 CUDA/C++ 的 DEM 求解器相结合。在粒子堆积的模拟中，CPU 的加速大约是 130 到 243 倍。在流化床模拟中，DEM 计算时间从 91% 减少到 17%，速度提高了 78 倍。Geldart A 颗粒流化的模拟揭示了流体和颗粒粗粒化的相似程度。从双流体模型导出的过滤阻力适用于具有流体和颗粒粗粒度的 Euler-Lagrangian 模拟。