Model order reduction techniques applied to magnetodynamic T-Ω-formulation

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 39, pp. 1057–1069

Abstract

Purpose The purpose of this paper is to use different model order reduction techniques to cope with the computational effort of solving large systems of equations. By appropriate decomposition of the electromagnetic field problem, the number of degrees of freedom (DOF) can be efficiently reduced. In this contribution, the Proper Generalized Decomposition (PGD) and the Proper Orthogonal Decomposition (POD) are used in the frame of the T-Ω-formulation, and the feasibility is elaborated. Design/methodology/approach The POD and the PGD are two methods to reduce the model order. Particularly in the context of eddy current problems, conventional time-stepping algorithms can lead to many numerical simulations of the studied problem. To simulate the transient field, the T-Ω-formulation is used which couples the magnetic scalar potential and the electric vector potential. In this paper, both methods are studied on an academic example of an induction furnace in terms of accuracy and computational effort. Findings Using the proposed reduction techniques significantly reduces the DOF and subsequently the computational effort. Further, the feasibility of the combination of both methods with the T-Ω-formulation is given, and a fundamental step toward fast simulation of eddy current problems is shown. Originality/value In this paper, the PGD is combined for the first time with the T-Ω-formulation. The application of the PGD and POD and the following comparison illustrate the great potential of these techniques in combination with the T-Ω-formulation in context of eddy current problems.

Authors 5

1. Fabian Müller Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines, RWTH Aachen University, Aachen, Germany

2. Lucas Crampen Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines, RWTH Aachen University, Aachen, Germany

3. Thomas Henneron

   Université de Lille

   Affiliation as printed

   EEA, Université de Lille, Villeneuve-d'Ascq, France

4. Stephane Clenet

   Arts et Métiers

   Affiliation as printed

   L2EP, Arts et Métiers ParisTech – Centre de Lille, Lille, France

5. Kay Hameyer Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines, RWTH Aachen University, Aachen, Germany