Predict the mass distribution of hypervelocity impact debris using deep learning

Efficiently predicting the evolution of hypervelocity impact（HVI） debris is crucial in the design of spacecraft protective structures. To this end two deep learning models, namely the de-convolutional architecture and the multi-layer perceptron architecture, are compared. Training data come from smoothed particle hydrodynamics （SPH） simulations. Deep learning models include four controllable labels, namely the projectile velocity （range 3–8 km/s） , the radius （range 2–8 mm）, target plate thickness （range 1–4 mm） and time instant （range 1–12 μs）. It is found that the two architectures are both accurate if the labels are confined within the range of training data. Out of the training data labels range, the de-convolutional architecture extrapolates poorly, though it can capture the granular property of the HVI debris. At the same time, the multi-layer perceptron architecture homogenizes the local mass distribution in the HVI debris and achieves much better extrapolation capability. By learning the mass distribution between time 1 μs and 12 μs, the multi-layer perceptron architecture can predict the mass distribution at time 24 μs with reasonable accuracy. Prediction time of the de-convolutional architecture is several milliseconds and prediction time of the multi-layer perceptron architecture is several seconds.