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

Abstract

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 Ps2τs=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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 27, 2018
Source ID
10.1063/1.5046185

Entities

People

  • A. S. Diggs
  • D. Barrett Hardin
  • H. S. Udaykumar
  • N. K. Rai
  • Oishik Sen

Organizations

  • Air Force Office of Scientific Research
  • Air Force Research Laboratory
  • University of Iowa

Tags

Readers

  • Combustion Dynamics and Shock Wave Physics.
  • Computational Fluid Dynamics (CFD)
  • Occupational Health and Safety.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference