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Microscopic defects such as voids and cracks in an energetic material significantly influence its shock sensitivity. So far, there is a lack of systematic and quantitative study of the effects of cracks both experimentally and computationally, although significant work has been done on voids. We present an approach for quantifying the effects of intragranular and interfacial cracks in polymerbonded explosives (PBXs) via mesoscale simulations that explicitly account for such defects. Using this approach, the ignition thresholds corresponding to any given level of ignition probability and, conversely, the ignition probability corresponding to any loading condition (i.e., ignition probability maps) are predicted for PBX 9404 containing different levels of initial grain cracking or interfacial debonding. James relations are utilized to express the predicted thresholds and ignition probabilities. It is found that defects lower the ignition thresholds and cause the material to be more sensitive. This effect of defects on shock sensitivity diminishes as the shock load intensity increases. Furthermore, the sensitivity differences are rooted in energy dissipation and the consequent hotspot development. The spatial preference in hotspot distribution is studied and quantified using a parameter called the defect preference ratio (r pref). Analyses reveal that defects play an important role in the development of hotspots and thus have a strong influence on the ignition thresholds. The findings are in qualitative agreement with reported trends in experiments.

Fully three-dimensional (3D) microstructure-explicit and void-explicit mesoscale simulations of the shock-to-detonation (SDT) process of pressed granular HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) are performed. The overall size scale of the models is up to 3 × 3 × 15 mm3, with ∼30 000 grains and 206 265 voids. The models account for the heterogeneous material microstructure, constituent distribution, constituent morphology, and voids. Loading conditions considered involve piston velocities in the range of 600–1200 m/s or pressures in the range of 4–8 GPa. The focus is on analyzing the SDT process and the effects of microstructure and voids on the run-to-detonation distance (RDD). Companion two-dimensional (2D) simulations are also carried out to assess the differences between 2D and 3D. Statistically equivalent microstructure sample sets (SEMSSs) are generated and used for both 2D and 3D, allowing the prediction of the statistical and probabilistic Pop plots (PPs). The predictions are in general agreement with trends in available experimental data in the literature. It is found that both the microstructure (heterogeneous grain size, morphology, and size distribution) and voids significantly affect the RDD and the PPs. These effects are systematically delineated and quantified via the use of SEMSSs with different combinations of attributes. A recently developed probabilistic formulation for the PPs is used to characterize the results, allowing uncertainties in the relations between the shock pressure and RDD arising from material heterogeneities to be quantified. The probabilistic formulation is further used to quantify the confidence levels in the ranked order of influences of different combinations of microstructure and voids on the PPs.

The generation of three-dimensional (3D) microstructures with multiple constituents is an important part of multiscale computational simulation and design for a wide range of materials including heterogeneous polycrystalline metals, ceramics, composites, and energetics. Realistic 3D microstructures for multiphase materials are difficult to obtain experimentally or computationally. Challenges include generation and representation of complex constituent morphologies, topological arrangement and distribution, defect description, and statistical conformity. Here, we present a novel technique for systematically composing complex 3D statistically equivalent microstructure sample sets (SEMSS) with prescribed statistical constituents and morphological attributes. Based on large libraries of varying representations of individual constituents, the technique can be used with experimental micro computerized tomography (CT) scans to establish SEMSS that track the attributes of existing materials as well as to design SEMSS for new materials not yet in existence for computational exploration. Heterogeneous systems involving different combinations of molecular crystallites, metallic particles, oxidizer granules, and a polymeric matrix are designed and generated to track the properties of an existing material. The corresponding SEMSS are used in multiphysics simulations accounting for coupled thermal-mechanical processes or thermal-mechanical-chemically reactive processes. The results are used to quantify microstructure-induced response variations and point out the limitations of two-dimensional (2D) microstructures that are direct sections of the full 3D microstructures. The use of the SEMSS has also enabled uncertainty quantification (UQ) and the development of probabilistic characterizations for variations in macroscopic responses due to intrinsic material microstructural heterogeneities.