Uncertainty Quantification for a Multi-phase Carbon Equation of State Model

Many physics models have tunable parameters that are calibrated by matching the model output to experimental or calculated data. However, given that calibration data often contain uncertainty and that different model parameter sets might result in a very similar simulated output for a finite calibra...

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Bibliographic Details
Published in:Journal of applied physics 2022-04, Vol.131 (15)
Main Authors: Lindquist, Beth Anne, Jadrich, Ryan Bradley
Format: Article
Language:English
Subjects:
ATOMIC AND MOLECULAR PHYSICS
Bayesian inference
covariance and correlation
equations of state
MATERIALS SCIENCE
phase transitions
probability theory
statistical analysis
thermodynamic properties
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Summary:Many physics models have tunable parameters that are calibrated by matching the model output to experimental or calculated data. However, given that calibration data often contain uncertainty and that different model parameter sets might result in a very similar simulated output for a finite calibration data set, it is advantageous to provide an ensemble of parameter sets that are consistent with the calibration data. Uncertainty quantification (UQ) provides a means to generate such an ensemble in a statistically rigorous fashion. In this work, we perform UQ for a multi-phase equation of state (EOS) model for carbon containing the diamond, graphite, and liquid phases. We use a Bayesian framework for the UQ and introduce a novel strategy for including phase diagram information in the calibration. The method is highly general and accurately reproduces the calibration data without any material-specific prior knowledge of the EOS model parameters.
ISSN:0021-8979
1089-7550