Nonlinear data-driven model order reduction applied to circuit-field magnetic problem - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Magnetics Année : 2021

Nonlinear data-driven model order reduction applied to circuit-field magnetic problem

Résumé

As in most of the domains in physics, finite element formulation is a very common method for electromagnetic fields computation. Since many years both proper orthogonal decomposition and empirical interpolation method are also often used in a model order reduction context. If these methods are efficients, their application is intrusive because it requires an access to the matrices and the assembly step. To avoid such an aspect, a data-driven model order reduction based on proper orthogonal decomposition with an approximation of the nonlinear terms by radial basis functions interpolation is applied to a magnetostatic problem coupled with circuit equations. The nonlinear reduced order model only needs solutions of a finite element analysis to be generated.
Fichier principal
Vignette du fichier
dataDriven2021ieee.pdf (3.4 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03601618 , version 1 (08-03-2022)

Identifiants

  • HAL Id : hal-03601618 , version 1

Citer

Antoine Pierquin, Thomas Henneron. Nonlinear data-driven model order reduction applied to circuit-field magnetic problem. IEEE Transactions on Magnetics, 2021. ⟨hal-03601618⟩
21 Consultations
70 Téléchargements

Partager

Gmail Facebook X LinkedIn More