返回
A fast method based on GPU for solidification structure simulation of continuous casting billets
DOI:10.1016/j.jocs.2020.101265.png)
摘要
En 中文
The present paper develops a fast method to simulate the solidification structure of continuous billets with Cellular Automaton (CA) model. Traditional solution of the CA model on single CPU takes a long time for the massive datasets and complicated calculations, making it unrealistic to optimize the parameters through numerical simulation. In this paper, a parallel method based on Graphics Processing Units (GPU) was proposed to accelerate the calculation, which developed new algorithms for the solute redistribution and neighbor capture to avoid data race in parallel computing. This new method was applied to simulate the solidification structure of Fe0.64C alloy, and the simulating results were in good agreement with the experiment results with the same parameters. The absolute computational time for the fast method implemented on Tesla P100 GPU is 277 s, while the traditional method implemented on Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40 GHz with single core is 24.57 h. The speedup, ratio between the absolute computational time of GPU-CA and CPU-CA, varies from 300 to 400 with the increase of the grids.
Keyword:
Fast method
Graphics processing units
Cellular automaton
Solidification structure
Parallel algorithms
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.0K
机构
引用论文
A Review of the Friction Stir Welding of Dissimilar Materials between Aluminum Alloys and Copper
Metals
IF0
A phase field investigation of dendrite morphology and solute distributions under transient conditions in an Al-Cu welding molten poolAl-cu焊接熔池中瞬态条件下枝晶形态和溶质分布的相场研究
Large-scale parallel lattice Boltzmann-cellular automaton model of two-dimensional dendritic growth二维枝晶生长的大规模平行格子Boltzmann-元胞自动机模型

