• 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