Parallel-GPU AMR implementation for phase-field lattice Boltzmann simulation of a settling dendrite
Shinji Sakane, Takayuki Aoki, Tomohiro Takaki
Abstract
The motion of the equiaxed grains plays an important role in macrosegregation. To simulate the motion of three-dimensional (3D) equiaxed dendrites growing in an undercooled melt, a large computational domain with small grids is required. In this study, we investigated the morphology and velocity of the settling dendrite and the growth velocity of its primary arms by implementing parallel computing with multiple graphics processing units (GPUs) for the adaptive mesh refinement (AMR) method (parallel-GPU AMR). A multiple-grid (MG) method that assigns different grid sizes to each field was also applied to the parallel-GPU AMR. A large-scale phase-field lattice Boltzmann (PF-LB) parallel-GPU AMR simulation of the settling dendrite was performed. The parallel GPU AMR method developed for the 3D PF-LB solidification model can evaluate the growth and motion of 3D equiaxed dendrite settling in an undercooled melt. The results are achieved with a significant acceleration of the 3D PF-LB simulations of settling dendrite and using a significantly reduced number of GPUs required for the simulation.