Magneto-static finite element (FE) simulations make numerical optimization of electrical machines very time-consuming and computationally intensive during the design stage. In this paper, we present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM). Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM by predicting intermediate FE measures. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators (KPIs), e.g., torque, shaft power, and material costs. We perform multi-objective optimization with both classical FE and a hybrid approach using a nature-inspired evolutionary algorithm. We show quantitatively that the hybrid approach maintains the quality of Pareto results better or close to conventional FE simulation-based optimization while being computationally very cheap.

翻译：磁静态有限元（FE）仿真使得电机在设计阶段的多目标数值优化过程极其耗时且计算密集。本文提出了一种混合数据与物理驱动的模型，并将其应用于永磁同步电机（PMSM）的数值优化中。在数据驱动的监督训练后，深度神经网络（DNN）作为元模型，通过预测中间有限元测量量来表征永磁同步电机的电磁行为。随后，利用多种物理模型对这些中间测量量进行后处理，以计算所需的关键性能指标（KPI），例如转矩、轴功率和材料成本。我们采用经典有限元方法与基于自然启发式进化算法的混合方法进行多目标优化。定量结果表明，混合方法与基于常规有限元仿真的优化相比，能够同样或更优地保持Pareto结果的质量，同时计算代价极低。