A Novel Neural Network Cell Method for Solving Nonlinear Electromagnetic Problems. Issue 12 (13th October 2021) - Zhu, Gaojia; Li, Longnv; Fu, Weinong; Xue, Ming; Liu, Tao; Zhu, Jianguo
Abstract: Effective analysis of nonlinear electromagnetic fields is essential for the accurate modeling of electromagnetic devices, such as transformers, generators, and motors. This paper proposes a novel approach of coupled neural network (NN) and cell method (CM) or NNCM for solving nonlinear electromagnetic problems with ferromagnetic domains. While the topologically linear relations of the cell complexes are mathematically assembled through a transformation in the Tonti diagram by the CM, and the constitutive nonlinear magnetic relations are dealt with by partially connected NN for the fast prediction of the permeability distribution inside the ferromagnetic domain. Since the construction of NN is directly related to the grid connections, a partially connected NN structure with a small number of neurons can reduce the computational cost of the training process. By using a compact NN, the proposed NNCM can effectively eliminate the time consuming iterations for determining the nonlinear permeability distribution, and improve the computational efficiency significantly. The NNCM is employed to analyze the transient electromagnetic field distribution inside a cylindrical ferromagnetic core. The results are compared with those obtained by the traditional iterative CM, which determines the nonlinear permeability distribution by lengthy numerical iterations, to verify the feasibility and effectiveness of the proposed NNCM. Abstract : A novel NNCM coupling neural network (NN) and cell method (CM) is proposed for the effective calculation of nonlinear electromagnetic fields in ferromagnetic regions. In NNCM, the topologically linear relations are assembled through transformations in Tonti diagram by CM, and the constitutive nonlinear relations are dealt with by partially connected NN. The proposed NNCM can improve the computational efficiency significantly.