A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes

Abstract Uncertainty Quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions. The concept of UQ in the nuclear community generally means forward UQ, in which the information flow is from the inputs to the outputs. Inverse UQ, in which the information flow is from the model outputs and experimental data to the inputs, is an equally important component of UQ but has been significantly underrated until recently. Forward UQ requires knowledge in the input uncertainties which has been specified by expert opinion or user self-evaluation. Inverse UQ is defined as the process to inversely quantify the input uncertainties based on experimental data. This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the inverse UQ methodologies that have been used on the physical models in system thermal–hydraulics codes. Inverse UQ methods can be categorized by three main groups: frequentist (deterministic), Bayesian (probabilistic), and empirical (design-of-experiments). We used eight metrics to evaluate an inverse UQ method, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. Twelve inverse UQ methods are reviewed, compared, and evaluated based on these eight metrics. Such comparative evaluation is intended to provide a good guidance for users to select a proper inverse UQ method based on the problem under investigation.