Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials. Issue 2 (4th December 2019)

• Record Type: Journal Article
• Authors: Barnes, Brian C.; Leiter, Kenneth W.; Larentzos, James P.; Brennan, John K.
• Is Part Of: Propellants, explosives, pyrotechnics. Volume 45:Issue 2(2020)
• Page Start: 177
• Page End: 195
• Publication Date: 2019-12-04
• Subjects: energetic materials -- multiscale simulation -- coarse-grain models -- dissipative particle dynamics -- Gaussian process regression; Propellants -- Periodicals; Explosives -- Periodicals; 662.2
• DOI: 10.1002/prep.201900187
• Languages: English
• ISSNs: 0721-3115

Abstract

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. Abstract :