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Desktop: HEIA_C20.05
2022
Cajander David
EPE'22 ECCE Europe, 2022
Summary:
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.
2011
Cajander David, Jean-Francois Emmenegger
Schweizer Statistiktage, 2011
2005
Cajander David, Hoang Le-Huy
Electrimacs, 8th International Conference on Modeling and Simulation of Electric Machines, Converters and Systems, 2005
2023
David Cajander, Davide Aguglia, Isabelle Viarouge, Philippe Viarouge
Proceedings of the IPAC'23 -14th International Particle Accelerator Conference, 7–12 May 2023, Venice, Italy
Link to the conference
This paper presents an efficient application of Machine Learning (ML) to derive models for accurately predicting the inductance value and mechanical constraints in widely used air-cored inductors in power electronics systems for accelerators. The ML is trained on Finite Elements Analyses (FEA) obtained data. The obtained Artificial Neural Network (ANN) based models are then used in a numerical optimization environment able to efficiently provide optimal solution in terms of speed and accuracy.
David Cajander, Isabelle Viarouge, Philippe Viarouge, Davide Aguglia
Proceedings of EPE'22 ECCE Europe, 5-9 September 2022, Hannover, Germany
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