【摘要】 水中爆炸靶板变形表现为结构与流体在冲击波作用下的复杂非线性耦合作用。文中通过设计和优化深度学习神经网络以预测输出不同靶板厚度、冲击因子、爆炸药量和爆炸距离条件下的靶板动态变形位移数据,测试集预测的决定系数和准确率达到0.99和0.95。与25个仿真工况数据相比,基于预测模型得到的9 261个工况数据形成的爆炸变形响应分析图能够覆盖更细致的特征参数范围和最大变形量变化趋势,可为水中武器设计及水下防护应用提供重要参考依据。更多还原

【Abstract】 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.更多还原