Volume 50 Issue 6
Jun.  2024
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XU J Q,LIN H P,GUO H. Multi-layer wave-shaped topology and thermal design method for aero-electric propulsion motors[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(6):1806-1818 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0498
Citation: XU J Q,LIN H P,GUO H. Multi-layer wave-shaped topology and thermal design method for aero-electric propulsion motors[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(6):1806-1818 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0498

Multi-layer wave-shaped topology and thermal design method for aero-electric propulsion motors

doi: 10.13700/j.bh.1001-5965.2022.0498
Funds:  National Natural Science Foundation of China through Yeqisun Science Foundation (U2141226); National Natural Science Foundation of China (52177028); Aeronautical Science Foundation of China (201907051002)
More Information
  • Corresponding author: E-mail:guohong@buaa.edu.cn
  • Received Date: 17 Jun 2022
  • Accepted Date: 24 Jun 2022
  • Publish Date: 04 Jul 2022
  • In view of the serious heat dissipation problem of aero-electric propulsion motors, an efficient heat dissipation design method of aero-electric propulsion motors based on multi-layer wave-shaped heat dissipation topology was proposed. The aero-electric propulsion motor adopted a multi-layer wave-shaped heat dissipation topology. In addition, the equivalent thermal network model of the motor was established, and important parameters such as contact thermal resistance and convection heat transfer coefficient were determined. The motor temperature was precisely calculated, and the accuracy and effectiveness of the thermal network model of the motor were verified by CFD simulation. On this basis, the effects of traditional fin-shaped heat dissipation topologies and multi-layer wave-shaped heat dissipation topologies on the power density of the motor were compared. Based on the equivalent thermal network model of the multi-layer wave-shaped heat dissipation topology, the genetic learning particle swarm optimization (GL-PSO) algorithm was used to optimize the efficient heat dissipation design of the aero-electric propulsion motor. The optimization results show that compared with the initial scheme, the weight of motor housing in the optimized scheme is reduced by 15.1%, and the power density of the motor is increased by 0.06 kW/kg.

     

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