title: Multi-scale modeling of heterogeneous energetic materials in three roles
title: prediction, deduction and induction
titleType: Subtitle
Creators: name: Parepalli, Prarthana
Publisher: name: University of Iowa
Year: 2023

description: Formulations of energetic materials rely on intrinsic (molecular) structure of a particular nitramine (CHNO) compound to engineer their responses to shocks. Based on decades of empirical data some of the most commonly used energetic materials (EMs) can be arranged in a hierarchy of sensitivity; from the most insensitive TATB, useful to prevent accidental initiation in storage, handling and transportation to the most sensitive compound PETN, used to trigger charges. Book-ended by these two compounds are a variety of other CHNO species with varying degrees of sensitivity. In multi-scale predictive models of shock-to-detonation transition (SDT), microstructure-aware burn models are employed to connect meso-scale hotspot dynamics with macro-scale energy release. Meso-scale calculations of hotspot ignition and growth rely on chemical reaction rates expressed in Arrhenius forms; these chemical kinetics models therefore are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for well-studied energetic materials such as HMX, the reaction time scales and pathways provided by available reaction kinetic schemes vary widely. For practical, predictive calculations of SDT in HMX-based energetic materials (EM), uncertainties in reaction rate parameters can amplify to produce large uncertainties in predicted initiation envelopes, such as James curves or Pop-plots.
description: In this thesis, the multiscale model or the meso-informed ignition and growth (MES-IG) model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition (SDT) was used to perform a forward prediction of sensitivity to bound reaction chemistry models. Three global Arrhenius-form rate models that span the range of reaction time scales, namely the Tarver 3-equation, the Henson 1-equation and the Menikoff 1-equation models. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker-Wasley curve) with experimental data for pressed HMX Class V microstructures. The forward model tells you the chemistry models for HMX should be in the vicinity of Henson1 Tarver3 models. To better pin down reaction rate parameters for HMX, we perform an inverse analysis (deduction) to extract the rate constants in an Arrhenius model (assuming a single reaction global kinetics model) that best predicts an experimentally obtained criticality (James) envelope for a neat-pressed HMX material for which the microstructure is well characterized. Interestingly, the estimated global kinetic scheme is shown to conform to a previously proposed global kinetics model, i.e., the well-known Henson 1-equation model. To establish the validity of the inverse analysis, we further carry out a forward analysis using the determined reaction rate law and demonstrate good predictions of the Pop-plot for a different class of pressed HMX material. While the inverse analysis has been applied herein to HMX, this presents a general route to arrive at global kinetics models that can be applied to a wide class of EM species. We can explore the possibility of developing techniques to accelerate the calculation of sensitivity envelopes for the general class of CHNO materials. Such a technique would be highly beneficial in the prediction and design of EMs and their formulations. Therefore, we use the knowledge gained from single energetic species (HMX) for Induction: generalizing surrogate models for energy localization across CHNO species through Transfer Learning. In order to guide the accelerated development of meso-informed multiscale models for other HE species a multi output convolution gaussian process technique was used which uses the metamodel generated for neat pressed HMX as the foundation to obtain the generic response hypersurfaces F_CHNO. These low fidelity F_CHNO are then corrected using sparse DNS simulations in order to develop a high fidelity metamodel for each CHNO, eg. TATB and RDX. This process of transfer learning provides a route for rapid estimations of shock sensitivity for a wide range of species across the CHNO family.