Armature Electromagnetic Force Extrapolation Prediction Method for Electromagnetic Railgun at High Speed
Liang Jin, Dexin Gong, Yingang Yan, Chenyuan Zhang
Abstract
The analysis and calculation of the armature electromagnetic force is the premise of studying the dynamic characteristics of the electromagnetic railgun. Aiming at the problem of the numerical solution “pseudo-oscillation” at high speed, an extrapolation prediction method of armature electromagnetic force based on the Deep Belief Network-Deep Neural Network (DBN-DNN) is proposed. Firstly, the electromagnetic field control equation and armature dynamics equation, considering the influence of armature movement, are given, and the finite element simulation model of the electromagnetic railgun is established to analyze the dynamic characteristics and numerical solution stability of the armature electromagnetic force. Then, based on the stable numerical simulation data under different armature conductivities, a DBN-DNN method is proposed to realize the extrapolation prediction of the armature electromagnetic force under the standard conductance. Finally, the extrapolation prediction performance of the proposed method is tested by two electromagnetic railgun cases. Additionally, we further propose the training strategy of DBN-DNN parameters from solving armature electromagnetic force at low conductivity to standard conductivity. The armature electromagnetic force extrapolation prediction method for the whole launch process from low speed to high speed provides a new idea for the dynamic characteristic analysis of the high-speed electromagnetic railgun.