Multi-scale shock-to-detonation simulation of pressed energetic material: A meso-informed ignition and growth model

• Published in: Journal of applied physics Vol. 124; no. 8
• Main Authors: Sen, O., Rai, N. K., Diggs, A. S., Hardin, D. B., Udaykumar, H. S.
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 28.08.2018
• Subjects: Applied physics; Bayesian analysis; Collapse; Computer simulation; Detonation; Energetic materials; Growth models; HMX; Ignition; Kriging interpolation; Mathematical models; Mesoscale phenomena; Porosity; Scale models
• ISSN: 0021-8979; 1089-7550
• DOI: 10.1063/1.5046185

This work presents a multiscale modeling framework for predictive simulations of shock-to-detonation transition (SDT) in pressed energetic (HMX) materials. The macro-scale computations of SDT are performed using an ignition and growth (IG) model. However, unlike in the traditional semi-empirical ignition-and-growth model, which relies on empirical fits, in this work meso-scale void collapse simulations are used to supply the ignition and growth rates. This results in a macro-scale model which is sensitive to the meso-structure of the energetic material. Energy localization at the meso-scale due to hotspot ignition and growth is reflected in the shock response of the energetic material via surrogate models for ignition and growth rates. Ensembles of meso-scale reactive void collapse simulations are used to train the surrogate model using a Bayesian Kriging approach. This meso-informed Ignition and Growth (MES-IG) model is applied to perform SDT simulations of pressed HMXs with different porosity and void diameters. The computations are successfully validated against experimental pop-plots. Additionally, the critical energy for SDT is computed and the experimentally observed P s 2 τ s = constant relations are recovered using the MES-IG model. While the multiscale framework in this paper is applied in the context of an ignition-and-growth model, the overall surrogate model-based multiscale approach can be adapted to any macro-scale model for predicting SDT in heterogeneous energetic materials.