Predicting critical impact velocity in PBX-9501 using machine learning

Heterogeneous energetic materials (HEMs) can involve microstructural defects such as randomly distributed pores of varying size and shape. These heterogeneities introduce temperature spikes known as hotspots which affect their shock response. Current methods for obtaining detonation properties rely on experiments and computational models. However, accounting for each possible pore configuration requires an extensive number of experiments and computational simulations, making them unfeasible for this problem. Machine Learning (ML) offers an attractive approach to overcome these challenges. Towards this goal, we develop a ML model for predicting critical velocities in PBX-9501 samples with multiple pores of varying quantity, size, and spatial distribution. We used CTH to emulate the shock response of each sample when impacted by a flyer plate. To compute the resulting critical velocities, an automated bisection algorithm was developed. We then leveraged three ML models: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Graph Neural Network (GNN). The performance of each model, as they compare in prediction of critical velocity, is studied. The models are stress-tested for cases involving new spatial distributions, pore quantities, and pore sizes. It is expected that these models will directly predict the critical velocity for unseen pore structures without executing CTH. Ultimately, we believe this work paves the way towards ML-guided models to understand how various pore structures affect shock sensitivity in HEMs