Publications
Addressing the Gap between Meso(grain) and Continuum Scales with Stochastic Burn Models and Probability Density Function Theory
Within the energetics community, considerable effort is being put forth to find a robust scale-bridging link between unreacted material microstructures and the observed material responses, e.g. detonation and sub-detonative phenomena. Specifically, one area where this scale-bridging capability is needed is mesoscale modeling of explosives initiation (MMEI); here, material microstructures are imported directly or as statistical reconstructions into a hydrocode. While MMEI is attractive for simulating the shock initiation process with ever-increasing model fidelity, a large gap remains between the data being generated at the mesoscale and the calibration of burn model parameters. In this work, stochastic burn models are explored as a paradigm-shift to address possible scale-bridging schemes. These stochastic, particle-based methods are similar to those used for granular and droplet-laden flows, with Langevin-type equations. Further parallels are drawn to turbulent combustion modeling and preliminary developments using probability density function (pdf) theory by Baer, Gartling, and DesJardin. In order to implement these new scale-bridging schemes, one example of a stochastic burn model is explained in greater detail. Results from the stochastic burn model and MMEI simulations are given to illustrate the proposed approach. Ultimately, the execution of this work will be a community endeavor; to achieve such a capability, research efforts should focus on full-field data mining and pdf evolution, in addition to new numerical techniques for hydrocodes.