Shock initiation experiments with ignition and growth modeling on the HMX-based explosive LX-14

Vandersall, Kevin S.; DeHaven, Martin R.; Strickland, Shawn L.; Tarver, Craig M.; Springer, H. Keo; Cowan, Matt

doi:10.1063/1.5044917

Cited by 10 publications

(1 citation statement)

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“…Previous studies on porous explosives show significant increases in shock sensitivity compared to the same explosives formulated with binder at higher density [1][2][3][4][5]. In these porous beds, the intergranular porosity significantly exceeds intragranular porosity and, under shock loading, hotspots formed on the grain surfaces can dominate the overall reactivity.…”

Section: Introductionmentioning

confidence: 93%

Simulating the Effects of Grain Surface Morphology on Hot Spots in HMX with Surrogate Model Development

Springer

Miller²,

Kroonblawd

et al. 2022

Propellants Explo Pyrotec

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There have been several numerical studies on the collapse of internal voids in energetic grains but fewer investigations have probed grain-grain interface effects. In this study, we examine the effects of grain surface morphology and binder conditions on hot spot mechanisms during shock loading using the multi-physics hydrocode, ALE3D, coupled with the thermochemical code, Cheetah. To improve the accuracy of our grain-scale simulations, the HMX material models have been updated from previous studies to incorporate new property predictions from molecular dynamics simulations. In our simulations of the interface between two neighboring energetic grains, the upstream grain surface is described by a single sinusoid with amplitude (0.5-2 μm) and wavelength (0.5-2 μm). The effects of grain surface coating, i. e., no binder, partially coated, and fully coated with binder, is also explored. A range of shock loading pressures (10-30 GPa) is considered. Bind-er coating reduces hot spots at grain-grain interfaces by limiting plastic deformation and heating of upstream grain surfaces as well as preventing transmission of localized deformation modes (e. g., jetting) to the downstream grain. More plastic work and heating is observed with new HMX material models versus the previous models because the strength does not thermally soften as much as a result of the higher melt temperature; this also reduces vortical flow and jetting mechanisms with the new HMX material models. A surrogate model based on neural networks is developed for the early stage reaction rate. Physics-based input features improve the performance of the neural networks over basic input features alone with a root mean squared error = 1.05 μs À 1 and R 2 = 0.98. Accurate and fast-running surrogate models can effectively serve as structure-property-sensitivity relationships.

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