LAPSE:2023.13209
Published Article
LAPSE:2023.13209
On the Use of Topology Optimization for Synchronous Reluctance Machines Design
February 28, 2023
Synchronous reluctance (SynRel) machines are considered one of the promising and cost-effective solutions to many industrial and mobility applications. Nonetheless, achieving an optimal design is challenging due to the complex correlation between geometry and magnetic characteristics. In order to expand the limits formed by template-based geometries, this work approaches the problem by using topology optimization (TO) through the density method (DM). Optimization settings and their effects on results, both in terms of performance and computation time, are studied extensively by performing optimizations on the rotor of a benchmark SynRel machine. In addition, DM-based TO is applied to an existing rotor geometry to assess its use and performance as a design refinement tool. The findings are presented, highlighting several insights into how to apply TO to SynRel machine design and its limitations, boundaries for performance improvements and related computational cost.
Keywords
density method, synchronous reluctance machine, topology optimization
Suggested Citation
Korman O, Di Nardo M, Degano M, Gerada C. On the Use of Topology Optimization for Synchronous Reluctance Machines Design. (2023). LAPSE:2023.13209
Author Affiliations
Korman O: Power Electronics, Machines and Control (PEMC) Research Group, University of Nottingham, Nottingham NG7 2RD, UK [ORCID]
Di Nardo M: Power Electronics, Machines and Control (PEMC) Research Group, University of Nottingham, Nottingham NG7 2RD, UK [ORCID]
Degano M: Power Electronics, Machines and Control (PEMC) Research Group, University of Nottingham, Nottingham NG7 2RD, UK [ORCID]
Gerada C: Power Electronics, Machines and Control (PEMC) Research Group, University of Nottingham, Nottingham NG7 2RD, UK [ORCID]
Journal Name
Energies
Volume
15
Issue
10
First Page
3719
Year
2022
Publication Date
2022-05-19
Published Version
ISSN
1996-1073
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Original Submission
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PII: en15103719, Publication Type: Journal Article
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LAPSE:2023.13209
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doi:10.3390/en15103719
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Feb 28, 2023
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CC BY 4.0
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