使用递归神经网络在铁磁叠片铁芯的有限元模拟中考虑磁滞和涡流

中文摘要 AI 辅助

将磁滞和涡流纳入叠片铁芯电机的有限元模拟在计算上具有挑战性。在每个积分点和每次非线性迭代中求解叠片内部的场会导致计算成本比无磁滞模拟高出几个数量级，这使得此类方法在设计应用中不切实际。随着电机拓扑结构和运行条件的复杂性增加，仅考虑磁饱和的简化模型越来越不足。在此背景下，机器学习替代建模成为一种有前途的替代方法。本文中，训练了一个递归神经网络作为各向同性叠片铁芯材料模型的替代，并将其集成到基于磁矢量势公式的实际二维磁动力学有限元模拟中。该方法与参考叠片铁芯模型取得了极好的一致性，同时将计算成本限制在无磁滞模拟的两倍左右。通过在一组足够多样的人工生成磁场序列上训练递归神经网络，该方法可以很容易地应用于广泛的有限元模拟。此外，训练好的替代模型作为一个独立组件提供，可以很容易地纳入现有的计算框架。它可在这个https URL上公开获取。

英文摘要

Incorporating hysteresis and eddy currents into finite element simulations of laminated-core electrical machines is computationally challenging. Resolving the fields inside the laminations at each integration point and at every nonlinear iteration leads to computational costs several orders of magnitude higher than anhysteretic simulations, making such approaches impractical for design applications. Conversely, simplified models accounting only for magnetic saturation are becoming increasingly inadequate as electrical machine topologies and operating conditions grow in complexity. In this context, machine learning surrogate modeling has emerged as a promising alternative, offering efficient and accurate approximations of complex electromagnetic behaviors. In this paper, a recurrent neural network is trained as a surrogate of a laminated-core material model for an isotropic laminated core, and is integrated into realistic two-dimensional magnetodynamic finite element simulations based on a magnetic vector potential formulation. The proposed approach achieves excellent agreement with the reference laminated-core model while limiting the computational cost to about twice that of an anhysteretic simulation. By training the recurrent neural network on a sufficiently diverse set of artificially generated magnetic field sequences designed to mimic those encountered in electrical machine simulations, the proposed approach can be readily applied across a wide range of finite element simulations. Furthermore, the trained surrogate model is provided as a standalone component that can be easily incorporated into existing computational frameworks. It is publicly available at https://gitlab.onelab.info/getdp/lamnet.

发表机构

• Department of Electrical Engineering and Computer Science, University of Liege（电子工程与计算机科学系，列日大学）

机构由 AI 辅助整理，请以论文原文为准。