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

Microstructure simulations of continuous casting billets are vital for understanding solidification mechanisms and optimizing process parameters. However, the commonly used CA (Cellular Automaton) model is limited by grid anisotropy, which affects the accuracy of dendrite morphology simulations. While the DCSA (Decentered Square Algorithm) reduces anisotropy, its high computational cost due to the use of fine grids and dynamic liquid/solid interface tracking hinders large-scale applications. To address this, we propose a high-performance CA-DCSA method on GPUs (Graphic Processing Units). The CA-DCSA algorithm is first refactored and implemented on a CPU-GPU heterogeneous architecture for efficient acceleration. Subsequently, key optimizations, including memory access management and warp divergence reduction, are proposed to enhance GPU utilization. Finally, simulated results are validated through industrial experiments, with relative errors of 2.5% (equiaxed crystal ratio) and 2.3% (average secondary dendrite arm spacing) in 65# steel, and 2.1% and 0.7% in 60# steel. The maximum temperature difference in 65# steel is 1.8 °C. Compared to the serial implementation, the GPU-accelerated method achieves a 1430× higher speed using two GPUs. This work has provided a powerful tool for detailed microstructure observation and process parameter optimization in continuous casting billets.