Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm

Narges Taran; Vandana Rallabandi; Dan M. Ionel; Greg Heins; Dean Patterson; Ping Zhou

doi:10.1109/IEMDC.2019.8785088

Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm

Narges Taran, Vandana Rallabandi, Dan M. Ionel, Greg Heins, Dean Patterson, Ping Zhou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper discusses the multi-objective optimization of axial flux permanent magnet (AFPM) machines with ferrite spoke-type magnets, utilizing 3D finite element models. Three-dimensional finite element analysis is computationally expensive, and furthermore, substantial computation time is expended by optimization algorithms in evaluating low performing designs whose performance is far from the optimum if the search space is not specified correctly. In this regard, this work proposes two new methods for identifying the search space. The search is limited to ranges of input geometric variables where high performing designs are likely to be found. The optimization algorithm utilized is based on surrogate models and differential evolution. It is found that the combined use of these approaches drastically reduces the solution time.

Original language	English
Title of host publication	2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019
Pages	603-608
Number of pages	6
ISBN (Electronic)	9781538693490
DOIs	https://doi.org/10.1109/IEMDC.2019.8785088
State	Published - May 2019
Event	11th IEEE International Electric Machines and Drives Conference, IEMDC 2019 - San Diego, United States Duration: May 12 2019 → May 15 2019

Publication series

Name	2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019

Conference

Conference	11th IEEE International Electric Machines and Drives Conference, IEMDC 2019
Country/Territory	United States
City	San Diego
Period	5/12/19 → 5/15/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

ACKNOWLEDGMENT The support of Regal Beloit Corporation, University of Kentucky, the L. Stanley Pigman endowment and the SPARK program, and ANSYS Inc. is gratefully acknowledged.

Funders	Funder number
ANSYS, Inc.
Regal Beloit Corporation Australia
University of Kentucky

Keywords

Axial flux
Optimization
Search space
Sensitivity analysis
Spoke-type
Surrogate kriging model

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Mechanical Engineering

Access to Document

10.1109/IEMDC.2019.8785088

Cite this

Taran, N., Rallabandi, V., Ionel, D. M., Heins, G., Patterson, D., & Zhou, P. (2019). Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm. In 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019 (pp. 603-608). Article 8785088 (2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019). https://doi.org/10.1109/IEMDC.2019.8785088

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abstract = "This paper discusses the multi-objective optimization of axial flux permanent magnet (AFPM) machines with ferrite spoke-type magnets, utilizing 3D finite element models. Three-dimensional finite element analysis is computationally expensive, and furthermore, substantial computation time is expended by optimization algorithms in evaluating low performing designs whose performance is far from the optimum if the search space is not specified correctly. In this regard, this work proposes two new methods for identifying the search space. The search is limited to ranges of input geometric variables where high performing designs are likely to be found. The optimization algorithm utilized is based on surrogate models and differential evolution. It is found that the combined use of these approaches drastically reduces the solution time.",

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author = "Narges Taran and Vandana Rallabandi and Ionel, {Dan M.} and Greg Heins and Dean Patterson and Ping Zhou",

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Taran, N, Rallabandi, V, Ionel, DM, Heins, G, Patterson, D & Zhou, P 2019, Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm. in 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019., 8785088, 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019, pp. 603-608, 11th IEEE International Electric Machines and Drives Conference, IEMDC 2019, San Diego, United States, 5/12/19. https://doi.org/10.1109/IEMDC.2019.8785088

Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm. / Taran, Narges; Rallabandi, Vandana; Ionel, Dan M. et al.
2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019. 2019. p. 603-608 8785088 (2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Taran N, Rallabandi V, Ionel DM, Heins G, Patterson D, Zhou P. Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm. In 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019. 2019. p. 603-608. 8785088. (2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019). doi: 10.1109/IEMDC.2019.8785088

Design optimization of electric machines with 3D FEA and a new hybrid DOE-DE numerical algorithm

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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