Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning

Author

Arnoux, Pierre-Hadrien ; Caillard, Pierre ; Gillon, Frederic

Author_Institution

Lab. of Electr. Eng. & Power Electron., Ecole Centrale de Lille, Villeneuve d´Ascq, France

Volume

51

Issue

3

fYear

2015

fDate

Mar-15

Firstpage

1

Lastpage

4

Abstract

This paper proposes a method to the model constraints from different models to run an optimization over models with different granularities. Through machine learning, the proposed method has proven to be able to accurately map the constraints and minimize the number of call to the model. It handles both continuous and discrete variables and mixes design rules to statistic approach to create a surrogate of the model.

Keywords

electric machines; finite element analysis; learning (artificial intelligence); minimisation; power engineering computing; statistical analysis; continuous variables; discrete variables; electrical machine design optimization; finite element constraint model; machine learning; statistic approach; Algorithm design and analysis; Computational modeling; Entropy; Iron; Optimization; Prediction algorithms; Vegetation; Constraint modeling; finite-element (FE) model; machine learning; optimal design; random forest;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2014.2364031

Filename

7093402