Parallel GPU-accelerated adaptive mesh refinement on two-dimensional phase-field lattice Boltzmann simulation of dendrite growth
Sakane, Shinji 1Aoki, Takayuki 2Takaki, Tomohiro1
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作者信息
1. Kyoto Inst Technol
2. Tokyo Inst Technol
折叠
Abstract
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.
NSTL
Elsevier