Effect of mesh refinement on the estimation of model input parameters using Inverse Uncertainty Quantification

Proper quantification of the uncertainties in the input parameters of a thermal-hydraulics code (e.g. physical models) is an essential part of the forward Uncertainty Quantification (UQ) problem. Such quantification can be systematically achieved by solving the Inverse Uncertainty Quantification (IUQ) problem using data from code predictions and experimental measurements. The IUQ problem is highly dependent on several factors such as geometry and discretization. In this paper, we study the effect of mesh refinement on the statistical parameters of three uncertain physical models (interfacial friction coefficient, wall to liquid friction coefficient and critical heat flux). A mathematical framework based on MLE and MAP algorithms is implemented to perform IUQ for the physical models of the thermal-hydraulics code RSTART based on experimental data from the OECD/NEA BWR Full-size Fine-mesh Bundle Test (BFBT) benchmark. Sensitivity analysis required for the IUQ problem was achieved by implementing discrete adjoint methods in the thermal-hydraulics code. The statistical parameters of the three physical models were obtained by implementing the MLE and MAP methods using code predictions that correspond to three different mesh sizes. Results from MLE and MAP showed a noticeable effect of mesh refinement on the estimates of the mean and a negligible effect on the estimates of the variance of the physical models. The compensation of error due to mesh refinement was studied by running the code with perturbation values calculated using the three different mesh sizes. It was shown that the results based on the most refined mesh led to best agreement between experimental data and code prediction. (C) 2019 Elsevier Ltd. All rights reserved.