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Reactive burn models for heterogeneous energetic materials (EMs) must account for chemistry as well as microstructure to predict shock-to-detonation transition (SDT). Upon shock loading, the collapse of individual voids leads to ignition of hotspots, which then grow and interact to consume the surrounding material. The sub-grid dynamics of shock-void interactions and hotspot development are transmitted to macro-scale SDT calculations in the form of a global reactive "burn model." This paper presents a physically evocative model, called meso-informed sub-grid source terms for energy localization (MISSEL), to close the macro-scale governing equations for calculating SDT. The model parameters are explicitly related to four measurable physical quantities: two depending on the microstructure (the porosity ϕ and average pore size D ¯ v o i d ), one depending on shock–microstructure interaction (the fraction of critical voids ξ c r ), and the other depending on the chemistry (the burn front velocity V h s ). These quantities are individually quantifiable using a small number of rather inexpensive meso-scale simulations. As constructed, the model overcomes the following problems that hinder the development of meso-informed burn models: (1) the opacity of more sophisticated surrogate/machine-learning approaches for bridging meso- and macro-scales, (2) the rather large number of high-resolution mesoscale simulations necessary to train machine-learning algorithms, and (3) the need for calibration of many free parameters that appear in phenomenological burn models. The model is tested against experimental data on James curves for a specific class of pressed 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane materials. The simple, evocative, and fast-to-construct MISSEL model suggests a route to develop frameworks for physics-informed, simulation-derived meso-informed burn models. [ABSTRACT FROM AUTHOR]

The response of a wide class of heterogeneous energetic materials (HEs) to loads is determined by dynamics at the meso-scale, i.e., by physicochemical processes in their underlying microstructure. Structure–property–performance (S–P–P) linkages for such materials can be developed in a multi-scale framework, connecting the physics and thermophysical properties at the meso-scale to response at the macro-scale. Due to the inherent stochasticity of the microstructure, ensembles of microstructures are required to conduct meso-scale simulations to establish S–P–P linkages. Here, a deep neural network-based method called deep feature representation is applied to generate a range of material microstructures from heterogeneous energetic materials to metal foams and metallic mixtures. The method allows for the generation of stochastic microstructures using a single real microstructure as the input and is not limited to low packing density or topological complexity of solids. In its application to pressed energetic materials, we show that qualitative and quantitative features of real (i.e., imaged) microstructures are captured in the synthetic microstructures. Therefore, a stochastic ensemble of synthetic microstructures can be created for use in reactive meso-scale simulations to relate the microstructures of HEs to their performance. While the focus is on pressed HE microstructures, we also show that the method is general and useful for generating microstructures for in silico experiments for a wide range of composite/multiphase materials, which can be used to establish S–P–P linkages. [ABSTRACT FROM AUTHOR]

Predictive simulations of the shock‐to‐detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo‐mechanics of EM during the SDT, both macro‐scale response and sub‐grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock‐initiated EM microstructures. The proposed multiscale modeling framework is divided into two stages. First, a physics‐aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock‐initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub‐grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high‐performance and safer energetic materials. [ABSTRACT FROM AUTHOR]

Crystals of energetic materials, such as 1,3,5,7-Tetranitro-1,3,5,7-tetrazocane (HMX), embedded in plastic binders are the building blocks of plastic-bonded explosives (PBX). Such heterogeneous energetic materials contain microstructural features such as sharp corners, interfaces between crystal and binder, intra- and extra-granular voids, and other defects. Energy localization or "hotspots" arise during shock interaction with the microstructural heterogeneities, leading to initiation of PBXs. In this paper, high-resolution numerical simulations are performed to elucidate the mechanistic details of shock-induced initiation in a PBX; we examine four different mechanisms: (1) shock-focusing at sharp corners or edges and its dependency on the shape of the crystal and the strength of the applied shock; (2) debonding between crystal and binder interfaces; (3) collapse of voids in the binder located near an HMX crystal; and (4) the collapse of voids within HMX crystals. Insights are obtained into the relative contributions of these mechanisms to the ignition and growth of hotspots. Understanding these mechanisms of energy localization and their relative importance for hotspot formation and initiation sensitivity of PBXs will aid in the design of energetic material-driven systems with controlled sensitivity, to prevent accidental initiation and ensure reliable performance. [ABSTRACT FROM AUTHOR]