Modeling meso-scale energy localization in shocked HMX, Part II: training machine-learned surrogate models for void shape and void-void interaction effects

TL;DR

This paper develops machine-learned surrogate models to understand how non-cylindrical void shapes and void interactions influence hotspot ignition and growth in shocked HMX, extending previous models to more realistic meso-structures.

Contribution

It introduces Bayesian Kriging surrogate models for void shape and interaction effects based on reactive collapse simulations, capturing complex dependencies on shape, orientation, and void fraction.

Findings

Void aspect ratio and orientation significantly affect ignition and growth rates.

Void fraction influences hotspot evolution through void-void interactions.

Surrogate models effectively predict effects of void morphology on sensitivity.

Abstract

Surrogate models for hotspot ignition and growth rates were presented in Part I, where the hotspots were formed by the collapse of single cylindrical voids. Such isolated cylindrical voids are idealizations of the void morphology in real meso-structures. This paper therefore investigates the effect of non-cylindrical void shapes and void-void interactions on hotspot ignition and growth. Surrogate models capturing these effects are constructed using a Bayesian Kriging approach. The training data for machine learning the surrogates are derived from reactive void collapse simulations spanning the parameter space of void aspect ratio (AR), void orientation ($\theta$), and void fraction ($\phi$). The resulting surrogate models portray strong dependence of the ignition and growth rates on void aspect ratio and orientation, particularly when they are oriented at acute angles with respect to the imposed shock. The surrogate models for void interaction effects show significant changes in hotspot ignition and growth rates as the void fraction increases. The paper elucidates the physics of hotspot evolution in void fields due to the creation and interaction of multiple hotspots. The results from this work will be useful not only for constructing meso-informed macro-scale models of HMX, but also for understanding the physics of void-void interactions and sensitivity due to void shape and orientation.