• 论文 •

基于神经网络及深层充电的电子通量反演模型

1. 南京航空航天大学 航天学院，南京211100
• 出版日期:2022-09-09 发布日期:2022-09-09

ANN inversion model for electron flux based on deep charging

ZHOU Hongtao，FANG Meihua

1. Institute of Aerospace，Nanjing University of Aeronautics and Astronautics，Nanjing 211100，China
• Online:2022-09-09 Published:2022-09-09

Abstract:  To realize the estimation of high-energy electron flux and the risk assessment of spacecraft deep charging and discharging，an artificial neural network （ANN）for the electron flux inversion with deep charging was built based on the relation between deep charging and electron flux.The detect currents of a deep charging detector and the electron energy were taken as the model inputs，while the electron fluxes were taken as the output.AE9 was used to train the network，and the MSE of this model was reduced to 0.04122.The deep charging data from Giove-A and electron flux from GOES were used to verify the model′s accuracy.Based on this model，another ANN model was built to calculate the maximum internal electric field of the typical dielectric of spacecraft from the detection current to realize the real-time assessment of charging risk in spacecraft.