Global Sensitivity Analysis and Bayesian Calibration on a Series of Reflood Experiments with Varying Boundary Conditions

Best estimate plus uncertainty for the safety assessment of nuclear power plant transient requires, among others, estimating the probability density function (PDF) of physical model parameters in thermal-hydraulic system codes. In that context, Bayesian calibration based on experimental data from separate-effect test facilities are increasingly popular to inform the PDF of a single thermal-hydraulic phenomenon. These calibrations are, however, time intensive, especially when considering multiple time-dependent outputs. Calibrating on many tests with different boundary conditions and potentially different phenomena to derive PDFs applicable to complex transients appears intractable, even using hierarchical modeling. In this paper, we start investigating this problem by considering a set of Flooding Experiments with Blocked Arrays reflood tests with different boundary conditions. We use TRACE v5.0p3 to model time- and space-dependent temperature profiles, pressure drops, and liquid carry-over. Global sensitivity analysis helps screen out noninfluential parameters and gain a detailed understanding of the modeled physics of reflood. The analysis shows that, for all tests, the outputs were sensitive to a similar set of influential model parameters. In turn, Bayesian calibration yields similar posterior PDFs for the influential parameters, and forward propagation of these posterior PDFs yields similar confidence intervals. As such, the information of the investigated tests can well be represented by a unique posterior PDF. Such simplifications, although not general, are welcome to help manage the intensive calibration effort necessary for dealing with complex thermal-hydraulic transients encountered in nuclear power plants.