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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]

For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic material models, which might not be feasible from physical experiments alone. However, synthesizing realistic microstructures with realistic microstructural attributes is highly challenging. Thus, it is often oversimplified via rough approximations that may yield an inaccurate representation of the physical world. Here, we propose a novel deep learning method that can synthesize realistic three-dimensional microstructures with controlled structural properties using the combination of generative adversarial networks (GAN) and actor-critic (AC) reinforcement learning. The GAN-AC combination enables the generation of microstructures that not only resemble the appearances of real specimens but also yield user-defined physical quantities of interest (QoI). Our validation experiments confirm that the properties of synthetic microstructures generated by the GAN-AC framework are within a 5% error margin with respect to the target values. The scientific contribution of this paper resides in the novel design of the GAN-AC microstructure generator and the mathematical and algorithmic foundations therein. The proposed method will have a broad and substantive impact on the materials community by providing lenses for analyzing structure-property-performance linkages and for implementing the notion of 'materials-by-design'. [ABSTRACT FROM AUTHOR]

The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework. [ABSTRACT FROM AUTHOR]

Morphology and dynamics at the mesoscale 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 (mesoscale) physics and therefore encapsulate structure–property (S–P) linkages to predict performance at the macroscale. This work establishes a route to S–P linkage, proceeding all the way from imaged microstructures 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. Mesoscale dynamics is computed to calculate sub-grid properties, i.e., closure models for momentum and energy equations. The S–P 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. [ABSTRACT FROM AUTHOR]

Surrogate models for hotspot ignition and growth rates were presented in Part I (Nassar et al., Shock Waves 29(4):537–558, 2018), 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, void orientation (θ) , and void fraction (ϕ) . 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 macroscale models of HMX, but also for understanding the physics of void–void interactions and sensitivity due to void shape and orientation. [ABSTRACT FROM AUTHOR]

This work presents the procedure for constructing a machine-learned surrogate model for hot-spot ignition and growth rates in pressed HMX materials. A Bayesian kriging algorithm is used to assimilate input data obtained from high-resolution mesoscale simulations. The surrogates are built by generating a sparse set of training data using reactive mesoscale simulations of void collapse by varying loading conditions and void sizes. Insights into the physics of void collapse and ignition and growth of hot spots are obtained. The criticality envelope for hot spots is obtained as the function Σ cr = f P s , D void where P s is the imposed shock pressure and D void is the void size. Criticality of hot spots is classified into the plastic collapse and hydrodynamic jetting regimes. The information obtained from the surrogate models for hot-spot ignition and growth rates and the criticality envelope can be utilized in meso-informed ignition and growth models to perform multi-scale simulations of pressed HMX materials. [ABSTRACT FROM AUTHOR]

Macroscale models of shock-particle interactions require closure terms for unresolved solid-fluid momentum and energy transfer. These comprise the effects of mean as well as fluctuating fluid-phase velocity fields in the particle cloud. Mean drag and Reynolds stress equivalent terms (also known as pseudo-turbulent terms) appear in the macroscale equations. Closure laws for the pseudo-turbulent terms are constructed in this work from ensembles of high-fidelity mesoscale simulations. The computations are performed over a wide range of Mach numbers (M) and particle volume fractions (ϕ) and are used to explicitly compute the pseudo-turbulent stresses from the Favre average of the velocity fluctuations in the flow field. The computed stresses are then used as inputs to a Modified Bayesian Kriging method to generate surrogate models. The surrogates can be used as closure models for the pseudo-turbulent terms in macroscale computations of shock-particle interactions. It is found that the kinetic energy associated with the velocity fluctuations is comparable to that of the mean flow—especially for increasing M and ϕ. This work is a first attempt to quantify and evaluate the effect of velocity fluctuations for problems of shock-particle interactions. [ABSTRACT FROM AUTHOR]

A three-layer Newtonian model is investigated using a combined Eulerian-Lagrangian computational method to describe the dynamic behaviour of leukocytes. The model, composed of a cell membrane (outer layer), cytoplasm (middle layer) and nucleus (inner layer), can better describe the recovery characteristics because large viscosity and capillarity differences between layers are considered, and both Newtonian and seemingly non-Newtonian behaviours reported in the literature can be reproduced. It is found that, to describe adequately the various rheological characteristics of leukocytes, the presence of the highly viscous nucleus and its deformation/recovery, as well as the surface energy stored in the fluid interfaces, are critical. Photographs from pipette experiments using a fluorescent technique confirm the theoretical finding of the important role played by the nucleus in cell deformation. [ABSTRACT FROM AUTHOR]