Comparison of inverse uncertainty quantification methods for critical flow test

Paweł Domitr, Mateusz Włostowski, Rafał Laskowski and Romuald Jurkowski

Energy, 2023, vol. 263, issue PA

Abstract: A problem of epistemic uncertainties introduced by code input parameters that cannot be estimated other than by user expertise has existed in nuclear reactor systems analyses since first uncertainty analyses were conducted. Inverse Uncertainty Quantification (IUQ) methods aim at providing an estimation of the distributions of such parameters. This paper compares and assesses two approaches to quantify the uncertainty of critical flow model parameters available in the TRACE code. First novel approach is based on a machine-learning algorithm, using a Random Forest classifier to assign the results of calculations to one of the defined classes of prediction accuracy with respect to experimental data. The second approach is based on Markov Chain Monte Carlo sampling and Bayesian inference. Both methods allows to assign probability distribution functions to TRACE internal variables, which is a goal for IUQ methods.

Keywords: Critical flow; TRACE; Machine learning; Inverse uncertainty quantification; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222025269

DOI: 10.1016/j.energy.2022.125640

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