DocumentCode :
1529133
Title :
Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Performance Improvement Using Response Surface Methodology and Genetic Algorithms
Author :
Hasanien, Hany M. ; Abd-Rabou, Ahmed S. ; Sakr, Sohier M.
Author_Institution :
Electr. Power & Machines Dept., Ain Shams Univ., Cairo, Egypt
Volume :
25
Issue :
3
fYear :
2010
Firstpage :
598
Lastpage :
605
Abstract :
Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.
Keywords :
finite element analysis; genetic algorithms; linear motors; permanent magnet motors; electrodynamics vibrator; finite element computations; free-piston generator; genetic algorithms; motor weight; performance improvement; permanent magnet type transverse flux linear motors; power system; railway traction; response surface methodology; second-order analytical model; weight reduction; Analytical models; Design optimization; Electromagnetic devices; Genetic algorithms; Permanent magnet motors; Power system analysis computing; Power system modeling; Rail transportation; Response surface methodology; Traction motors; Finite element method (FEM); genetic algorithms (GAs); optimization; response surface methodology (RSM);
fLanguage :
English
Journal_Title :
Energy Conversion, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8969
Type :
jour
DOI :
10.1109/TEC.2010.2050591
Filename :
5504077
Link To Document :
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