Prediction of Probabilistic Ignition Thresholds of Energetic Materials with Microstructural Heterogeneities

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Wei, Yaochi
Zhou, Min
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The design of new materials requires establishment of macroscopic measures of material performance as functions of intrinsic material microscopic heterogeneities. The heterogeneities in an energetic material (EM) significantly influence its ignition behavior. Traditionally, the effort to relate microscopic heterogeneities to macroscopic ignition threshold has been an empirical endeavor and there is a lack of systematic quantitative study of this effect both experimentally and computationally. This work presents an approach for quantifying the effects of microscopic heterogeneities such as microstructure and intragranular and interfacial defects in EM via mesoscale simulations that explicitly model such heterogeneities. The coupled mechanical-thermal-chemical approach explicitly accounts for microstructure, constituent properties, and interfacial responses and captures multiphysics processes responsible for the development of hotspot and damage. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, heat conduction, chemical reaction, and convective heat transfer. The probabilistic analysis uses systematically composed complex 2D/3D statistically equivalent microstructure sample sets (SEMSS) to directly mimic relevant experiments for quantification of statistical variations of material behavior due to inherent material heterogeneities. The ignition thresholds corresponding to any given level of ignition probability and, conversely, the ignition probability corresponding to any loading condition are predicted for polymer-bonded explosive (PBX) materials containing different levels microstructural heterogeneities. The particular thresholds and ignition probabilities predicted are expressed in James type and Walker-Wasley type relations, leading to the establishment of explicit analytical expressions for the ignition probability as function of loading. The capability to computationally predict the macroscopic engineering material response revelations out of material basic constituent, heterogeneities, and interfacial properties lends itself to the design of new materials and the analysis of existing materials.
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