Modeling meso-scale energy localization in shocked HMX, Part I: machine- learned surrogate model for effect of loading and void size

This work presents the procedure for constructing a machine learned surrogate model for hotspot ignition and growth rates in pressed HMX materials. A Bayesian Kriging algorithm is used to assimilate input data obtained from high-resolution meso-scale simulations. The surrogates are built by generating a sparse set of training data using reactive meso-scale simulations of void collapse by varying loading conditions and void sizes. Insights into the physics of void collapse and ignition and growth of hotspots are obtained. The criticality envelope for hotspots is obtained as the function {\Sigma}_cr=f(P_s,D_void ) where P_s is the imposed shock pressure and D_void is the void size. Criticality of hotspots is classified into the plastic collapse and hydrodynamic jetting regimes. The information obtained from the surrogate models for hotspot ignition and growth rates and the criticality envelope can be utilized in meso-informed Ignition and Growth (MES-IG) models to perform multi-scale simulations of pressed HMX materials.Comment: 37 Pages, 19 Figure

Structure-Property Linkage in Shocked Multi-Material Flows Using A Level-Set Based Eulerian Image-To-Computation Framework

Morphology and dynamics at the meso-scale play crucial roles in the overall macro- or system-scale flow of heterogeneous materials. In a multi-scale framework, closure models upscale unresolved sub-grid (meso-scale) physics and therefore encapsulate structure-property (S-P) linkages to predict performance at the macro-scale. This work establishes a route to structure-property linkage, proceeding all the way from imaged micro-structures to flow computations in one unified level set-based framework. Level sets are used to: 1) Define embedded geometries via image segmentation; 2) Simulate the interaction of sharp immersed boundaries with the flow field, and 3) Calculate morphological metrics to quantify structure. Meso-scale dynamics are computed to calculate sub-grid properties, i.e. closure models for momentum and energy equations. The structure-property linkage is demonstrated for two types of multi-material flows: interaction of shocks with a cloud of particles and reactive meso-mechanics of pressed energetic materials. We also present an approach to connect local morphological characteristics in a microstructure containing topologically complex features with the shock response of imaged samples of such materials. This paves the way for using geometric machine learning techniques to associate imaged morphologies with their properties