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Robust Shape Optimization of Electric Devices Based on Deterministic Optimization Methods and Finite Element Analysis With Affine Decomposition and Design Elements

Ion, Ion Gabriel and Bontinck, Zeger and Loukrezis, Dimitrios and Römer, Ulrich and Lass, Oliver and Ulbrich, Stefan and Schöps, Sebastian and De Gersem, Herbert :
Robust Shape Optimization of Electric Devices Based on Deterministic Optimization Methods and Finite Element Analysis With Affine Decomposition and Design Elements.
[Online-Edition: https://doi.org/10.1007/s00202-018-0716-6]
In: Electrical Engineering (Archiv für Elektrotechnik) ISSN 1432-0487
[Article] , (2018)

Official URL: https://doi.org/10.1007/s00202-018-0716-6

Abstract

In this paper, gradient-based optimization methods are combined with finite-element modeling for improving electric devices. Geometric design parameters are considered by affine decomposition of the geometry or by the design element approach, both of which avoid remeshing. Furthermore, it is shown how to robustify the optimization procedure, i.e., how to deal with uncertainties on the design parameters. The overall procedure is illustrated by an academic example and by the example of a permanent-magnet synchronous machine. The examples show the advantages of deterministic optimization compared to standard and popular stochastic optimization procedures such as, e.g., particle swarm optimization.

Item Type: Article
Erschienen: 2018
Creators: Ion, Ion Gabriel and Bontinck, Zeger and Loukrezis, Dimitrios and Römer, Ulrich and Lass, Oliver and Ulbrich, Stefan and Schöps, Sebastian and De Gersem, Herbert
Title: Robust Shape Optimization of Electric Devices Based on Deterministic Optimization Methods and Finite Element Analysis With Affine Decomposition and Design Elements
Language: English
Abstract:

In this paper, gradient-based optimization methods are combined with finite-element modeling for improving electric devices. Geometric design parameters are considered by affine decomposition of the geometry or by the design element approach, both of which avoid remeshing. Furthermore, it is shown how to robustify the optimization procedure, i.e., how to deal with uncertainties on the design parameters. The overall procedure is illustrated by an academic example and by the example of a permanent-magnet synchronous machine. The examples show the advantages of deterministic optimization compared to standard and popular stochastic optimization procedures such as, e.g., particle swarm optimization.

Journal or Publication Title: Electrical Engineering (Archiv für Elektrotechnik)
Uncontrolled Keywords: uncertainty, optimization, robust
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Electromagnetic Field Theory
18 Department of Electrical Engineering and Information Technology > Institute of Electromagnetic Field Theory > Computational Engineering (2019 umbenannt in Computational Electromagnetics)
Exzellenzinitiative
Exzellenzinitiative > Graduate Schools
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Date Deposited: 18 Aug 2018 17:35
DOI: 10.1007/s00202-018-0716-6
Official URL: https://doi.org/10.1007/s00202-018-0716-6
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