Purpose The multiscale magnetic domain energy model (MMDEM) is widely used for analyzing the magnetic properties of grain-oriented electrical steel sheets; however, its high computational cost significantly limits its efficiency under fine discretization. This study aims to propose a parallel computing-based acceleration method to improve computational efficiency while maintaining simulation accuracy. Design/methodology/approach A parallel acceleration framework is developed and applied to the magnetic characterization of grain-oriented electrical steel sheets. The sample is discretized into independent computational elements suitable for parallel execution. The demagnetization coefficient tensor of each element is computed in parallel and incorporated into the energy formulation. In addition, a parallel global optimization algorithm is used to minimize the total magnetic domain energy. The computational performance and accuracy are evaluated under different discretization levels. Findings The proposed method significantly reduces computation time compared with the conventional serial MMDEM, particularly at higher discretization levels. Meanwhile, the simulation accuracy is well preserved, and the results show good agreement with experimental data. Originality/value This work presents a parallel computing-based acceleration strategy for MMDEM, providing an efficient and scalable framework for magnetic domain modeling. The proposed method improves computational efficiency while maintaining accuracy, enhancing the applicability of MMDEM in large-scale and practical engineering problems.
Wu et al. (Wed,) studied this question.