Chinese Space Science and Technology ›› 2022, Vol. 42 ›› Issue (6): 134-139.doi: 10.16708/j.cnki.1000-758X.2022.0092

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Dielectric surface charging potential inversion method based on BP neural network

ZHANG Chengyue,QUAN Ronghui,ZHANG Haicheng   

  1. School of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Published:2022-12-25 Online:2022-11-09

Abstract: To realize the comprehensive and real-time risk analysis of in-orbit satellites' charge-discharge, a BP neural network for the surface potential inversion of dielectrics commonly used on satellites with the Kapton surface potential was built based on the relation between surface charging of different materials in the same environment.The Kapton surface potential and the materials thickness were taken as the inputs, while the surface potential of other dielectric materials were taken as the model outputs.By using the surface charging model established by COMSOL to train the neural network, the inversion error was reduced to less than 10%.The accuracy of the inversion model was verified by the surface-charge-experiment data of Kapton and Teflon.The results show that the relative error between the inversion value and the experimental value is less than 16%.

Key words:  , surface charging, BP neural network, numerical simulation, inversion, satellite