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.
Title
Inductor design optimization using FEA supervised machine learning
Date
2022-09
Published in
Proceedings of EPE'22 ECCE Europe, 5-9 September 2022, Hannover, Germany
Publisher
Hannover, Germany, 5-9 September 2022
Pagination
11 p.
Presented at
EPE'22 ECCE Europe, Hannover, Germany, 2022-09-05, 2022-09-09
Paper type
published full paper
Faculty
Ingénierie et Architecture
School
HEIA-FR
Institute
Energy - Institut de recherche appliquée en systèmes énergétiques