Accelerated Method for Simulating the Solidification Microstructure of Continuous Casting Billets on GPUs

doi:10.3390/ma18091955

2025-04-25

DOI：10.3390/ma18091955

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摘要

En 中文

连续铸钢坯的微观结构模拟对于理解凝固机制和优化工艺参数至关重要。然而，常用的CA（元胞自动机）模型受限于网格各向异性，这影响了枝晶形貌模拟的准确性。虽然DCSA（偏心方形算法）能降低各向异性，但其由于使用细网格和动态液固界面追踪导致的高计算成本阻碍了大规模应用。为解决这一问题，我们提出了基于GPU（图形处理器）的高性能CA-DCSA方法。首先，对CA-DCSA算法进行重构，并在CPU-GPU异构架构上实现以实现高效加速。随后，提出关键优化措施，包括内存访问管理及warp发散减少，以提升GPU利用率。最后，通过工业实验验证了模拟结果，在65#钢中，等轴晶比例和平均二次枝晶臂间距的相对误差分别为2.5%和2.3%，在60#钢中分别为2.1%和0.7%。65#钢的最大温差为1.8°C。与串行实现相比，GPU加速方法使用两块GPU实现了1430倍的加速。本研究为连续铸钢坯的详细微观结构观察和工艺参数优化提供了有力工具。

Keyword:

GPU-accelerated method

parallel CA-DCSA algorithm

microstructure simulation

dendrite morphology

continuous casting

speedup

期刊

Materials

IF:

3.2

论文数:

5.7W

被引数:

15.1W

机构

shandong youth univ polit sci

学者数:

论文数: 9

被引数: 5

Northeastern Univ

学者数:

2.9K

论文数: 1.3K

被引数: 362

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