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.
Titel
Inductor design optimization using FEA supervised machine learning
Datum
2022-09
Veröffentlich in
Proceedings of EPE'22 ECCE Europe, 5-9 September 2022, Hannover, Germany
Verlag
Hannover, Germany, 5-9 September 2022
Umfang
11 p.
Vorgestellt auf
EPE'22 ECCE Europe, Hannover, Germany, 2022-09-05, 2022-09-09
Papiertyp
published full paper
Domaine
Ingénierie et Architecture
Ecole
HEIA-FR
Institut
Energy - Institut de recherche appliquée en systèmes énergétiques