Uncertainty Quantification of Shock to Detonation Transition in TNT
ORAL
Abstract
The run-to-detonation distance is an important metric for quantifying
the safety and performance of a High Explosive (HE), while
Trinitrotoluene (TNT) is a commonly used explosive with important
industrial and defense applications. Previous work has calibrated
reactive burn models to match experimental data for the
Shock-to-Detonation Transition (SDT) of TNT and have shown good
agreement with the experimental results. The present works seeks to
understand how uncertainties in these reactive burn model parameters
may affect these predictions. A Stochastic Finite Volume (SFV)
approach is employed to understand how uncertainties in the model
parameters affect the uncertainties in the quantities of interest in
the simulation. These results will illustrate what variations could be
expected in the run-to-detonation distance for TNT due the uncertainty
in reactive burn models. It will also demonstrate the SFV method as a
tool to, more generally, understand how uncertainties in constitutive
models for HE can affect predictions of important performance metrics.
the safety and performance of a High Explosive (HE), while
Trinitrotoluene (TNT) is a commonly used explosive with important
industrial and defense applications. Previous work has calibrated
reactive burn models to match experimental data for the
Shock-to-Detonation Transition (SDT) of TNT and have shown good
agreement with the experimental results. The present works seeks to
understand how uncertainties in these reactive burn model parameters
may affect these predictions. A Stochastic Finite Volume (SFV)
approach is employed to understand how uncertainties in the model
parameters affect the uncertainties in the quantities of interest in
the simulation. These results will illustrate what variations could be
expected in the run-to-detonation distance for TNT due the uncertainty
in reactive burn models. It will also demonstrate the SFV method as a
tool to, more generally, understand how uncertainties in constitutive
models for HE can affect predictions of important performance metrics.
*This work was supported by the U.S. Department of Energythrough the Los Alamos National Laboratory. Los Alamos NationalLaboratory is operated by Triad National Security, LLC, for theNational Nuclear Security Administration of U.S. Department of Energy(Contract No. 89233218CNA000001)
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Presenters
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Stephen A Andrews
- Los Alamos National Laboratory