基于深度学习神经网络的水中爆炸靶板变形响应预测研究

The deformation of a target plate in underwater explosion is manifested as a complex nonlinear coupling interaction between the structure and the fluid under the impact of shock waves. In this paper, a deep learning neural network is designed and optimized to predict the dynamic deformation displacement data of the target plate under different conditions of target plate thickness, shock factor, explosive dosage, and explosion distance. The coefficient of determination and accuracy of prediction on the test set reach 0.99 and 0.95, respectively. Compared with 25 simulation conditions, the explosion deformation response analysis graph formed by 9 261 working conditions based on the prediction model can cover a more detailed range of characteristic parameters and the trend of maximum deformation variation, providing important reference for underwater weapon design and underwater protection applications.