Logo image
Back
Modeling mesoscale energy localization in shocked HMX, part I: machine-learned surrogate models for the effects of loading and void sizes
Journal article   Peer reviewed

Modeling mesoscale energy localization in shocked HMX, part I: machine-learned surrogate models for the effects of loading and void sizes

A Nassar, N Rai, O Sen and H Udaykumar
Shock waves, Vol.29(4), pp.537-558
05/01/2019
DOI: 10.1007/s00193-018-0874-5
url
https://arxiv.org/pdf/1810.04666View
Open Access

Abstract

This work presents the procedure for constructing a machine-learned surrogate model for hot-spot ignition and growth rates in pressed HMX materials. A Bayesian kriging algorithm is used to assimilate input data obtained from high-resolution mesoscale simulations. The surrogates are built by generating a sparse set of training data using reactive mesoscale simulations of void collapse by varying loading conditions and void sizes. Insights into the physics of void collapse and ignition and growth of hot spots are obtained. The criticality envelope for hot spots is obtained as the function $$ \varSigma_{\text{cr}} = f\left( {P_{\text{s}} ,D_{\text{void}} } \right) $$ Σ cr = f P s , D void where $$ P_{\text{s}} $$ P s is the imposed shock pressure and $$ D_{\text{void}} $$ D void is the void size. Criticality of hot spots is classified into the plastic collapse and hydrodynamic jetting regimes. The information obtained from the surrogate models for hot-spot ignition and growth rates and the criticality envelope can be utilized in meso-informed ignition and growth models to perform multi-scale simulations of pressed HMX materials.
 Acoustics Condensed Matter Physics Energetic materials Engineering Engineering Fluid Dynamics Engineering Thermodynamics, Heat and Mass Transfer Fluid- and Aerodynamics Ignition/growth reaction rates Machine learning Multi-scale modeling Pressed HMX Surrogate modeling Thermodynamics Void collapse

Details

Metrics

21 Record Views
39 Times Cited - Web of Science
Logo image