Physics-based Reactive Burn Model on Ignition and Detonation of High Explosives

(including microstructural effects like grain size)

 Xia (Ruth) Lu, Leader
 Yuchiro Hamate, Post Doctral Associate
 

The research program is to develop the mathematical physically-based reactive burn (PBRB) model, numerical algorithms, and simulation tools, capable of predictions and simulations of the ignition and detonation of solid explosives and/or munitions under threats. The projected goals are to (a) evaluate the safety and performance of energetic systems and munition system, (b) predict the physics of future generations of energetic materials without manipulating model parameters, and (c) serve as a desirable material design tool to fit new munition requirements like the design of a weapon system. The research program is in the category of basic science of energetic materials from AFOSR and AFRL/MN and application programs from IHAAA.

In the science of energetic materials, a thermal “hot spot” theory for heterogeneous explosives is developed to account for energy localization and initiation of explosives and becomes a fundamental. In this theory, hot spots of critical sizes and critical temperature would initiate thermal decomposition reaction and lead to various reaction modes like combustion, explosion or detonation. Various empirical burn models, which are based on hot spot theory, have been developed. These models can give a reaction rate law that governs initiation and reaction of HE. The model parameters introduced in these models are usually determined by fitting a set of experimental tests following an error-and-trial procedure. Transferability of these models is usually not satisfactory. It means that for new applications, for example, if we change grain size of material or the morphology or have new combination formulas or if we have materials exposed to different stimuli, empirical model parameters have to be adjusted to fit the new experimental observations. Usually totally different sets of model parameters will be expected. Therefore, the predictability of these models is quite limited.

For heterogeneous explosives that are largely used in Insensitive Munitions, heterogeneity or microstructural characteristics, including defects, interfaces, inter- and intra- granular voids, slip bands and anisotropic sites, are believed to be responsible for formation and growth of hot spots under stimuli. Then the sensitivity and reaction mechanisms are strongly correlated to material microstructures. Mesoscale simulations including various microstructural effects of interest like porosity, particle size, binder properties, etc, indicated that the roles of these microstructural characteristics cannot separate from each other. The interactions among these microstructural characteristics and their interweaved responses under the various stimuli generate a much more complicated thermal profiles/topology than would be achieved by usual conventional burn models. This strongly recommends us a unified model to include all possible microstructural effects and their interactions. On the other hand, the model should base on micromechanics in order to avoid introducing empirical and non-physcically based model parameters. In this respect, the modeling technique would be expected to have a merit of high predicitivity, particularly for the applications in prediction of the safety of energetic systems or munitions under adverse threats.

In comparing to currently available non-physically based ignition and growth models, the PBRB model to be developed have to be clearly based on a precise knowledge on sensitivity and reaction mechanisms of energetic materials. In PBRB model, specific descriptions are given to hot spot surface progression, sublimation of solid HE surface, and gas bulk combustion. A statistical approach treats the statistical distribution of hot spots. In addition, PBRB will incorporate a multiscale modeling concept, microstructural characteristics like reactive/inert particle size, interfacial area, defect densities and others, and their evolutions and the associated energy or thermal localization or deposition, will be modeled. Statistical and/or stochastic approach will be sought to describe the spatial-time-evolution of various localization characteristics. Then the low transferability and limited predictability of the current models are expected to be lifted up  expected to be lifted up.