Abstract
In the phase-field simulation of dendrite growth during the solidification of an alloy, the computational cost becomes extremely high when the diffusion length is significantly larger than the curvature radius of a dendrite tip. In such cases, the adaptive mesh refinement (AMR) method is effective for improving the computational performance. In this study, we perform a three-dimensional dendrite growth phase-field simulation in which AMR is implemented via parallel computing using multiple graphics processing units (GPUs), which provide high parallel computation performance. In the parallel GPU computation, we apply dynamic load balancing to parallel computing to equalize the computational cost per GPU. The accuracy of an AMR refinement condition is confirmed through the single-GPU computations of columnar dendrite growth during the directional solidification of a binary alloy. Next, we evaluate the efficiency of dynamic load balancing by performing multiple-GPU parallel computations for three different directional solidification simulations using a moving frame algorithm. Finally, weak scaling tests are performed to confirm the parallel efficiency of the developed code.
Highlights
A solidification microstructure is generally composed of equiaxed and columnar structures, which are formed by the competitive growth of a massive number of dendrites
The developed multiple-graphics processing units (GPUs) parallel computing method for adaptive mesh refinement (AMR) is evaluated by simulating columnar dendrite growth during the directional solidification of a binary alloy
We evaluated the performance of the developed scheme by simulating columnar dendrite growth during the directional solidification of a binary alloy
Summary
A solidification microstructure is generally composed of equiaxed and columnar structures, which are formed by the competitive growth of a massive number of dendrites. A few physical simulations (Schive et al 2018; Watanabe and Aoki 2021) implemented AMR using parallel computing with multiple GPUs, PF simulations for dendrite growth have not yet applied AMR using GPUs. AMR is extremely effective in resolving problems such as a wide primary arm spacing compared to the curvature radius of a dendrite tip. AMR is extremely effective in resolving problems such as a wide primary arm spacing compared to the curvature radius of a dendrite tip In these conditions, the further acceleration of PF simulations via AMR is required to treat a large number of dendrites. The AMR method is implemented using multiple-GPU parallel computing to increase the computational scale and efficiency of 3D PF dendrite growth simulations. The developed multiple-GPU parallel computing method for AMR is evaluated by simulating columnar dendrite growth during the directional solidification of a binary alloy.
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