An optimal inductor design methodology using dimensioning models derived from Finite Element Analysis (FEA) supervised Artificial Neural Networks (ANN) is presented. The efficiency of such trained ANN dimensioning models in terms of compromise between precision and computing time is demonstrated for the cylindrical inductor topology with air and magnetic material core including saturation.
Titre
Inductor design optimization using FEA supervised machine learning
Date
2022-09
Publié dans
Proceedings of EPE'22 ECCE Europe, 5-9 September 2022, Hannover, Germany
Publié par
Hannover, Germany, 5-9 September 2022
Pagination
11 p.
Présenté à
EPE'22 ECCE Europe, Hannover, Germany, 2022-09-05, 2022-09-09
Type de papier
published full paper
Domaine
Ingénierie et Architecture
Ecole
HEIA-FR
Institut
Energy - Institut de recherche appliquée en systèmes énergétiques