Tensor Decomposition‐Based Model Order Reduction Applied to Multi‐Parameter Electromagnetic Problems in the Context of Digital Twins

ABSTRACT Digital twin is considered the key technique for real‐time monitoring and life‐cycle management of electric equipment. To construct the digital twin model of electric equipment, a multi‐parameter electromagnetic analysis is needed to generate a large amount of high fidelity data under various working condition. However, repeated solving such multi‐parameter electromagnetic problems based on full order finite element method may lead to extreme scale calculations. To address this issue, a hybrid approach that combines tensor decomposition and proper orthogonal decomposition (POD) is introduced, which can effectively establish a reduced order model for multi‐parameter electromagnetic field problems. The performance of the proposed approach is illustrated through three numerical examples, namely an electrical motor, a transformer and a voice coil actuator including parameter variations of operating conditions, geometric parameters, and material parameters. The numerical results show that the proposed hybrid approach has significant advantage compared to conventional reduced order model in situation where the solution changes dramatically within the parameter variation range and even more apparent when the parameter dimension is high.