Abstract

Model order reduction (MOR) methods are more and more applied on many different fields of physics in order to reduce the number of unknowns and thus the computational time of large-scale systems. However, their application is quite recent in the field of computational electromagnetics. In the case of electrical machine, the numerical model has to take into account the nonlinear behaviour of ferromagnetic materials, motion of the rotor, circuit equations and mechanical coupling. In this context, we propose to apply the proper orthogonal decomposition combined with the (Discrete) empirical interpolation method in order to reduce the computation time required to study the start-up of an electrical machine until it reaches the steady state. An empirical offline/online approach based on electrical engineering is proposed in order to build an efficient reduced model accurate on the whole operating range. Finally, a 2D example of a synchronous machine is studied with a reduced model deduced from the proposed approach.

Highlights

  • Modeling electrical machines using the the finite element method (FEM) has proved to be an efficient approach since it allows to solve magnetostatic and magnetodynamic problems with complex geometries

  • We propose to apply a Model order reduction (MOR) approach to the problem (14)–(17) through the proper orthogonal decomposition combined with the (Discrete) empirical interpolation method

  • Proper orthogonal decomposition The proper orthogonal decomposition (POD) approach allows to significantly reduce the number of unknowns of the equation system

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Summary

Introduction

Modeling electrical machines using the the finite element method (FEM) has proved to be an efficient approach since it allows to solve magnetostatic and magnetodynamic problems with complex geometries. The numerical model of the nonlinear magnetostatic problem based on the vector potential formulation coupled with electrical and mechanical equations is presented. The proper orthogonal decomposition approach, which allows to project the full model in a reduced-basis of small size, is combined with the (Discrete) empirical interpolation method in order to compute nonlinear terms efficiently.

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