Key to understanding the uncertainties in a physics simulation code is the full characterization of the uncertainties in the physics submodels incorporated in it. I demonstrate an approach to the determining the parameters of material models in a simulation code that combines the principles of physics and probabilistic Bayesian analysis. The focus is on the parameters and their uncertainties in the simulation-code submodels, as well as the numerical errors introduced in solving the dynamical equations. Bayesian analysis provides the underpinning for quantifying the uncertainties in models inferred from experimental results, which possess their own degree of uncertainty. The aim is to construct an uncertainty model for the submodels that is based on inferences drawn from comparing the code's predictions to relevant experimental results. As an example, I will analyze a set of material-characterization experiments performed on tantalum to determine the parameters of the Preston-Tonks-Wallace model for plastic material behavior. I will indicate how data from a Taylor impact test may be used to update the parameters in the model by using Bayesian calibration in the context of a simulation code.
Keywords: plastic deformation model, Preston-Tonks-Wallace model, uncertainty analysis, Bayesian analysis, hierarchy of experiments, simulation uncertainty, model uncertainty, systematic uncertainty, Hopkinson-bar experiments, quasistatic-compression experiments
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