Adaptive Subdomain Model Order Reduction With Discrete Empirical Interpolation Method for Nonlinear Magneto-Quasi-Static Problems

Abstract

This paper presents a novel adaptive subdomain model order reduction (MOR) based on proper orthogonal decomposition (POD) and discrete empirical interpolation (DEI) methods for nonlinear magneto-quasi-static (MQS) problems. In this method, a nonlinear region is decomposed into two regions, where one of the regions includes all those finite elements that have a particularly strong saturation and the other region does not. MOR based on POD and DEI methods is applied only to the latter region. Both the regions are determined automatically at each time step. It is shown that this method can effectively reduce the computational time to solve the nonlinear MQS problems without losing the quality of accuracy.