Abstract：With the rapidly development of deep learning technology, various models based on data-driven have been widely studied and used in Computational Solid Mechanics and computational fluid dynamics. Based on the deep learning method, this project proposes a data-driven model for debris cloud generation. By the combination of Conditional Variation-Auto-Encoder model and numerical simulation results of SPH method, a deep learning model for simulating hypervelocity impact debris cloud was constructed. Before training deep learning model, some data preprocessing steps are needed to improve the data distribution law, such as spatial grid division and quality aggregation, which are conducive to improving the training speed and generalization performance. Experimental results show that the deep learning model can make a good prediction and interpolation in the range of training data set and also showed this ability just near training data. It provided to be a potential way to realize the comprehensive utilization of existing experimental data and numerical simulation results. And also maybe a potential way to improve the accuracy of debris cloud model.
2020, Vol. 41 
