Parallel GPU-accelerated adaptive mesh refinement on two-dimensional phase-field lattice Boltzmann simulation of dendrite growth
Parallel GPU-accelerated adaptive mesh refinement on two-dimensional phase-field lattice Boltzmann simulation of dendrite growth
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DOI：
10.1016/j.commatsci.2022.111507
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发表时间：
2022-08
影响因子：
3.3
通讯作者：
S. Sakane;T. Aoki;T. Takaki
中科院分区：
文献类型：
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作者：
S. Sakane;T. Aoki;T. Takaki
涉及熔体对流和固体运动的枝晶凝固模拟通常需要比枝晶尺寸高得多的计算域，其在均匀网格下的计算效率极低。在这项研究中，加速这些二维模拟使用相场和格子玻尔兹曼（PF-LB）方法，我们开发了一种并行计算方法与多个图形处理单元（GPU）的自适应网格细化（AMR）方法与动态负载平衡（并行GPU AMR）。事实证明，当自适应网格中的网格点数量约为均匀网格中的40%或更少时，并行GPU AMR模拟比均匀网格的模拟更快。我们还表明，开发的并行GPU AMR可以大大加快PF-LB模拟熔体对流和固体运动的枝晶生长。
Simulations of dendritic solidification involving melt convection and solid motion usually require a considerably higher computational domain than the dendrite size, whose computational efficiency with a uniform mesh is extremely low. In this study, to accelerate those two-dimensional simulations using the phase-field and lattice Boltzmann (PF-LB) methods, we developed a parallel computing method with multiple graphics processing units (GPUs) for the adaptive mesh refinement (AMR) method with dynamic load balancing (parallel-GPU AMR). It was confirmed that parallel-GPU AMR simulations were faster than those with the uniform mesh when the number of grid points in the adaptive mesh was around 40% or less than those in the uniform mesh. We also demonstrate that the developed parallel-GPU AMR can greatly accelerate the PF-LB simulations of dendrite growth with melt convection and solid motion.