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Journal Articles SN Applied Sciences Year : 2021

A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors

Abstract

A novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of performance and implementation of electric machines concerning industrial applications such as the noise, vibration, and harshness in electric motors. The approach allows to avoid the long-time simulations needed to analyze the electric machine at different operation points. In addition, it facilitates the computation and coupling of the motor model in other physical subsystems. Specifically, we propose a novel formulation of the sparse proper generalized decomposition procedure, combining it with a reduced basis approach, which is used to fit correctly the reduced order model with the numerical simulations as well as to obtain a further data compression. This technique can be applied to construct a regression model from high-dimensional data. These data can come, for example, from finite element simulations. As will be shown, an excellent agreement between the results of the proposed approach and the finite element method models are observed.
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Dates and versions

hal-03269735 , version 1 (24-06-2021)

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Cite

Abel Sancarlos, Elias Cueto, Francisco Chinesta, Jean-Louis Duval. A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors. SN Applied Sciences, 2021, 3, pp.1-19. ⟨10.1007/s42452-021-04310-3⟩. ⟨hal-03269735⟩
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