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Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials
Author(s) -
Barnes Brian C.,
Leiter Kenneth W.,
Larentzos James P.,
Brennan John K.
Publication year - 2020
Publication title -
propellants, explosives, pyrotechnics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.56
H-Index - 65
eISSN - 1521-4087
pISSN - 0721-3115
DOI - 10.1002/prep.201900187
Subject(s) - representative elementary volume , multiscale modeling , porosity , supercomputer , dissipative particle dynamics , materials science , estimator , computational science , scaling , computer science , microstructure , statistical physics , biological system , physics , parallel computing , mathematics , composite material , chemistry , computational chemistry , statistics , geometry , biology , polymer
Abstract We present new capabilities for investigation of microstructure in energetic material response for both explicit large‐scale and multiscale simulations. We demonstrate the computational capabilities by studying the effect of porosity on the reactive shock response of a coarse‐grain (CG) model of the energetic material cyclotrimethylene trinitramine (RDX), the non‐reactive equation of state for a porous representative volume element (RVE) of CG RDX, and utilization of available supercomputing resources for speculative sampling to accelerate hierarchical multiscale simulations. Small amounts of porosity (up to 4 %) are shown to have significant effect on the initiation of reactive CG RDX using large‐scale reactive dissipative particle dynamics simulations. Non‐reactive RVEs are shown to undergo a porosity‐dependent pore collapse at hydrostatic conditions, and an existing automation framework is shown to be easily modified for the incorporation of microstructure while retaining reliable convergence properties. A novel predictive sampling method based on use of kernel density estimators is shown to effectively accelerate time‐to‐solution in a multiscale simulation, scaling with free CPU cores, while making no assumptions about the underlying physics for the data being analyzed. These multidisciplinary studies of distinct yet connected problems combine to provide methodological insights for high‐fidelity modeling of reactive systems with microstructure.