Efficient Reluctance Network Formulation for Electrical Machine Design Using Optimization

The aim of this paper is to discuss electrical machines building and computation while taking into account the change of reluctance network (RN) topology required in an optimization process. Indeed, as electrical machine RN changes during an electrical period, this model has to consider several roto...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2014-02, Vol.50 (2), p.869-872
Main Authors: Hoa Nguyen-Xuan, Dogan, Hussein, Perez, Sylvain, Gerbaud, Laurent, Garbuio, Lauric, Wurtz, Frederic
Format: Article
Language:English
Subjects:
Algorithms
Angular modeling
Atmospheric modeling
Computational modeling
Cross-disciplinary physics: materials science
rheology
Electric power
electrical machine
Engineering Sciences
Exact sciences and technology
Finite element analysis
Magnetism
Materials science
moving band concept
Optimization
Other topics in materials science
Physics
reluctance network (RN)
Rotors
sizing by optimization
Topology
Torque
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Summary:The aim of this paper is to discuss electrical machines building and computation while taking into account the change of reluctance network (RN) topology required in an optimization process. Indeed, as electrical machine RN changes during an electrical period, this model has to consider several rotor positions to analyze harmonic influence on machine performance (e.g., its torque and losses). Such a requirement can also be important in applications where machine geometry changes shortly during sizing process by optimization. These two cases are addressed in this paper. Using established independent meshes of the initial RN, this paper proposes an algorithm to deal with all topologies necessary for an optimization process. This aims to reduce the computation time, increase the model, and improve the solving stability. An output selectivity approach is also applied on the RN modeling to improve the computation time of the optimization process. The simulation results are compared to finite-element methods and the optimization performance is presented.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2013.2282407