A faster than real-time heat transfer model for continuous steel casting

Abstract

In this paper, a three-dimensional faster than real-time heat transfer model is presented for the continuous casting process. The model features a high computational capability required for real-time prediction, optimization, and control of the casting process. The computational model is implemented with a multi-level acceleration method on GPUs to ultimately shrink the computational time cost. This acceleration method helps to build a proper mapping from numerical models to GPU hardware features and provides two acceleration strategies with slight accuracy loss. With the multi-level acceleration method, a 117× speedup with a max 1.74% relative error compared to a 14-threaded CPU implementation is achieved. Moreover, the accuracy of the model has been verified with the Stefan problem and validated with industry measurement. The relative computational time, which is required to be less than 1 in real-time simulations and significantly far less than 1 in dynamic optimization of the casting process, is adopted to evaluate the real-time ability of the model. The proposed model achieves a relative computational time between 0.0003 for a coarse mesh and 0.015 for a very fine mesh. Our demonstrated high-performance modeling tool herein is envisioned to dominate among the applications of advanced process control and optimization in continuous casting.