Fast Optimization of a Linear Induction Motor by 3-Level Space Mapping Technique

For the design and analysis of a linear electrical motor, an analytical solution of electric and magnetic fields is barely achieved with the required accuracy, due to the end and edge effects, and the nonlinear characteristic of the materials. Optimal design with the finite element models (FEM) is often expensive, in terms of the computation time. The space-mapping techniques allow having an affordable computation cost with a minimum number of computationally expensive FEM evaluations. In this paper, based on a kriging model, a 2D FEM and a 3D FEM, a 3-level adapted output space-mapping technique is employed. The results show that the proposed algorithm allows saving a substantial amount of computation time compared to conventional 2-level output space-mapping technique.

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Comparison of three space mapping techniques on electromagnetic design optimization

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-11-2016-0512 ◽

2018 ◽

Vol 37 (2) ◽

pp. 565-580

Author(s):

Jinlin Gong ◽

Frédéric Gillon ◽

Nicolas Bracikowski

Keyword(s):

Induction Motor ◽

Kriging Model ◽

Poor Quality ◽

Space Mapping ◽

Content Type ◽

Linear Induction ◽

Linear Induction Motor ◽

Coarse Model ◽

Original Approach

PurposeThis paper aims to investigate three low-evaluation-budget optimization techniques: output space mapping (OSM), manifold mapping (MM) and Kriging-OSM. Kriging-OSM is an original approach having high-order mapping. Design/methodology/approachThe electromagnetic device to be optimally sized is a five-phase linear induction motor, represented through two levels of modeling: coarse (Kriging model) and fine.The optimization comparison of the three techniques on the five-phase linear induction motor is discussed. FindingsThe optimization results show that the OSM takes more time and iteration to converge the optimal solution compared to MM and Kriging-OSM. This is mainly because of the poor quality of the initial Kriging model. In the case of a high-quality coarse model, the OSM technique would show its domination over the other two techniques. In the case of poor quality of coarse model, MM and Kriging-OSM techniques are more efficient to converge to the accurate optimum. Originality/valueKriging-OSM is an original approach having high-order mapping. An advantage of this new technique consists in its capability of providing a sufficiently accurate model for each objective and constraint function and makes the coarse model converge toward the fine model more effectively.

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Proposal of a kriging output space mapping technique for electromagnetic design optimization

2016 IEEE Conference on Electromagnetic Field Computation (CEFC) ◽

10.1109/cefc.2016.7816229 ◽

2016 ◽

Author(s):

Jinlin Gong ◽

Frederic Gillon ◽

Julien Truong Canh ◽

Yanliang Xu

Keyword(s):

Design Optimization ◽

Space Mapping ◽

Mapping Technique ◽

Electromagnetic Design ◽

Output Space

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Proposal of a Bi-Objective Kriging Adapted Output Space Mapping Technique for Electromagnetic Design Optimization

IEEE Transactions on Magnetics ◽

10.1109/tmag.2019.2907877 ◽

2019 ◽

Vol 55 (6) ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

J. Gong ◽

H. Zhang ◽

B. Zhao ◽

Dongshan Fu ◽

F. Gillon

Keyword(s):

Design Optimization ◽

Space Mapping ◽

Mapping Technique ◽

Electromagnetic Design ◽

Output Space

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n-level output space mapping for electromagnetic design optimization

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-10-2012-0222 ◽

2014 ◽

Vol 33 (3) ◽

pp. 868-878

Author(s):

Ramzi Ben Ayed ◽

Stéphane Brisset

Keyword(s):

Optimization Problem ◽

Magnetic Energy ◽

Computing Time ◽

Computation Time ◽

Space Mapping ◽

Time Model ◽

Electromagnetic Design ◽

Content Type ◽

Magnetic Energy Storage ◽

Output Space

Purpose – The aim of this paper is to reduce the evaluations number of the fine model within the output space mapping (OSM) technique in order to reduce their computing time. Design/methodology/approach – In this paper, n-level OSM is proposed and expected to be even faster than the conventional OSM. The proposed algorithm takes advantages of the availability of n models of the device to optimize, each of them representing an optimal trade-off between the model error and its computation time. Models with intermediate characteristics between the coarse and fine models are inserted within the proposed algorithm to reduce the number of evaluations of the consuming time model and then the computing time. The advantages of the algorithm are highlighted on the optimization problem of superconducting magnetic energy storage (SMES). Findings – A major computing time gain equals to three is achieved using the n-level OSM algorithm instead of the conventional OSM technique on the optimization problem of SMES. Originality/value – The originality of this paper is to investigate several models with different granularities within OSM algorithm in order to reduce its computing time without decreasing the performance of the conventional strategy.

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