Data-driven blended equations of state for condensed-phase explosives

Kibaek Lee; Alberto M. Hernández; D. Scott Stewart; Seungjoon Lee

doi:10.1080/13647830.2021.1887524

Data-driven blended equations of state for condensed-phase explosives

Kibaek Lee, Alberto M. Hernández, D. Scott Stewart, Seungjoon Lee

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English (US)
Pages (from-to)	413-435
Number of pages	23
Journal	Combustion Theory and Modelling
Volume	25
Issue number	3
DOIs	https://doi.org/10.1080/13647830.2021.1887524
State	Published - 2021
Externally published	Yes

ASJC Scopus subject areas

General Chemistry
General Chemical Engineering
Modeling and Simulation
Fuel Technology
Energy Engineering and Power Technology
General Physics and Astronomy

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10.1080/13647830.2021.1887524

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AB - 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.

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Data-driven blended equations of state for condensed-phase explosives

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