We present an approach for assessing the uncertainties in simulation code outputs in which one focuses on the physics submodels incorporated into the code. Through a Bayesian analysis of a hierarchy of experiments that explore various aspects of the physics submodels, one can infer the sources of uncertainty, and quantify them. As an example of this approach, we describe an effort to describe the plastic-flow characteristics of a high-strength steel by combining data from basic material tests with an analysis of Taylor impact experiments. A thorough analysis of the material-characterization experiments is described, which necessarily includes the systematic uncertainties that arise form sample-to sample variations in the plastic behaviour of the specimens. The Taylor experiments can only be understood by means of a simulation code. We describe how this analysis can be done and how the results can be combined with the results of analyses of data from simpler materialcharacterization experiments.
Keywords: simulation uncertainty, Monte Carlo technique, material-characterization experiments, Taylor cylinder impact test, model inference, likelihood analysis, systematic uncertainty, uncertainty assessment, probabilistic network, hierarchy of experiments, simulation code validation
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