基于人工神经网络的三棱柱定向装药结构破片初速预测模型

The prismatic charge structure, as a typical asymmetric structure, exhibits a pronounced directional energy output. It is important to predict the velocity distribution of its fragments for the structural design and damage efficiency assessment of warhead. For the prismatic charge structure,a fragment velocity prediction model based on an artificial neural network (ANN) is proposed. To improve the predictive efficiency and accuracy of the network model,the key factors affecting the fragment velocity distribution are identified through theoretical analysis,and four input characteristic parameters are selected for the network model. The multiple sets of different numerical simulation conditions are established by adjusting the values of these characteristic parameters,and a dataset is provided for the network model through numerical simulation method. The trained network model is used to predict the test set,and the predicted results are in good agreement with the numerically simulated results. The results indicate that the proposed network model has high accuracy in predicting the fragment distribution of prismatic charge structure,and has a good generalization capability. The neural network model is characterized by fast computation speed, high predictive accuracy,and easy-to-modeling. It can accurately predict the fragment velocity distribution of prismatic structure under single-end initiation conditions,thus providing important data support for the structural design and damage efficiency assessment of warheads.