Journal
COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING
Volume 31, Issue 3, Pages 958-971Publisher
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/03321641211209834
Keywords
Modelling; Electric motors; Simulation; Linear induction motor; Electromagnetic-thermal coupling model; Surrogate model; Surrogate-assisted multi-objective optimization
Ask authors/readers for more resources
Purpose - The purpose of this paper is to present a low evaluation budget optimization strategy for expensive simulation models, such as 3D finite element models. Design/methodology/approach - A 3D finite element electromagnetic model and a thermal model are developed and coupled in order to simulate the linear induction motor (LIM) to be conceived. Using the 3D finite element coupling model as a simulation model, a multi-objective optimization with a progressive improvement of a surrogate model is proposed. The proposed surrogate model is progressively improved using an infill set selection strategy which is well-suited for the parallel evaluation of the 3D finite element coupling model on an eight-core machine, with a maximum of four models running in parallel. Findings - The proposed strategy allows for a significant gain of optimization time. The 3D Pareto front composed of the finite element model evaluation results is obtained, which provides the designer with a set of optimal trade-off solutions for him/her to make the final decision for the engineering design. Originality/value - An infill set selection strategy is proposed, which allows the parallel evaluation of the finite element model, and at the same time guides the progressive construction of an improved surrogate model during the multi-objective optimization run. The paper may stand as a good reference for researchers/engineering designers who have to deal with optimal design problems implying costly simulation models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available