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Optimum Design Process of Coaxial Magnetic Gear Using 3D Performance Prediction Method Considering Axial Flux Leakage

Authors
Lee, Seung-HunIm, So-YeonRyu, Jun-YeolLim, Myung-Seop
Issue Date
Mar-2024
Publisher
Institute of Electrical and Electronics Engineers
Keywords
Axial flux leakage; coaxial magnetic gear (CMG); Computational modeling; Electromagnetic compatibility; equivalent magnetic circuit (EMC); Finite element analysis; Magnetic gears; multi-objective optimization; Rotors; Three-dimensional displays; Torque
Citation
IEEE Transactions on Industry Applications, v.60, no.2, pp 3075 - 3085
Pages
11
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Industry Applications
Volume
60
Number
2
Start Page
3075
End Page
3085
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/193282
DOI
10.1109/TIA.2023.3335894
ISSN
0093-9994
1939-9367
Abstract
This paper proposes a rapid and optimum design method that considers axial flux leakage for a coaxial magnetic gear (CMG). Conventional CMG optimum design methods perform two-dimensional (2D) finite element analysis (FEA) to evade the long computational time of three-dimensional (3D) FEA. However, conventional design methods do not consider axial flux leakage and are therefore less accurate than computationally exhaustive optimum design through 3D FEA. Therefore, as an alternative to reduce computational time, this study proposes a method that considers axial flux leakage for predicting the CMG performance combining correlation coefficients and 2D FEA instead of 3D FEA. The correlation coefficient is determined as the ratio of the square of the radial direction air gap flux in the equivalent magnetic circuit (EMC) that considers axial flux leakage to the square of the radial direction air gap flux in the EMC that does not consider axial flux leakage. The electromagnetic performance of the CMG predicted by the proposed method is used to develop a surrogate model for reducing computational time during the optimum design process. Based on the surrogate model, the optimum CMG design is analyzed to fabricate a prototype, and the proposed design process is validated through experiments.
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