Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations

Antoine Pierquin (1) , Thomas Henneron (1) , Stephane Clenet (1)

1 L2EP - Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697

Among the model order reduction techniques, the Proper Orthogonal Decomposition (POD) has shown its efficiency to solve magnetostatic and magneto-quasistatic problems in the time domain. However, the POD is intrusive in the sense that it requires the extraction of the matrix system of the full model to build the reduced model. To avoid this extraction, nonintrusive approaches like the Data Driven (DD) methods enable to approximate the reduced model without the access to the full matrix system. In this article, the DD-POD method is applied to build a low dimensional system to solve a magnetostatic problem coupled with electric circuit equations.

Mots clés: Data driven; finite element model; model order reduction; Data driven

Domaines: Analyse numérique [math.NA]; Electromagnétisme; Energie électrique

Antoine Pierquin, Thomas Henneron, Stephane Clenet. Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations. IEEE Transactions on Magnetics, 2017, 54 (3), pp.1-4. ⟨10.1109/TMAG.2017.2771358⟩. ⟨hal-01691190⟩