Data-driven blended equations of state for condensed-phase explosives
Combustion Theory and Modelling
We present a data-driven blended equation of state (EOS) approach for condensed phase high explosive materials. We first calibrate four different high explosive materials (Nitromethane, HMX, PETN and TATB) using a single or blending multiple Fried Howard Gibbs (FHG) EOS by an ad hoc trial and error method that has been used in the past, and which leads to a predictive model that can be used in engineering calculations. This ad-hoc calibration is then re-calibrated based on Bayesian optimisation via Gaussian Process regression. The two calibrations are then compared qualitatively and quantitatively and are shown to be in good to excellent agreement.
Office of Naval Research
Applied Data Science
Kibaek Lee, Alberto M. Hernández, D. Scott Stewart, and Seungjoon Lee. "Data-driven blended equations of state for condensed-phase explosives" Combustion Theory and Modelling (2021): 413-435. https://doi.org/10.1080/13647830.2021.1887524