Abstract

Detonation wave surface is composed of lead shock and reactive front, which are difficult to be measured simultaneously, so it is necessary to reconstruct the detonation surface. In this study, a reconstruction method is proposed for predicting lead shock from reactive front to obtain a full cellular detonation surface. The reconstruction uses a convolutional neural network (CNN) with the advantages of feature extraction and data dimensionality reduction, and the proposed method has been verified by data from numerical simulations in this work. The results indicate that this method performs much better than the traditional multi-layer perceptron (MLP), benefiting from the advanced architecture of CNN. Furthermore, effects of hyper-parameter choice have been tested, and the generalization capability of trained CNN for different activation-energy cases are also discussed.