Uncertainty and sensitivity analyses as a validation tool for BWR bundle thermal-hydraulic predictions
Artikel i vetenskaplig tidskrift, 2011
In recent years, more realistic safety analyses of nuclear reactors have been based on best estimate (BE) computer codes. The need to validate and refine BE codes that are used in the predictions of relevant reactor safety parameters, led to the organization of international benchmarks based on high quality experimental data. The OECD/NRC BWR full-size fine-mesh bundle test (BFBT) benchmark offers a good opportunity to assess the accuracy of thermal hydraulic codes in predicting, among other parameters, single and two phase bundle pressure drop, cross-sectional averaged void fraction distributions and critical powers under a wide range of system conditions. The BFBT is based on a multi-rod assembly integral test facility which is able to simulate the high pressure, high temperature fluid conditions found in BWRs through electrically heated rod bundles. Since code accuracy is unavoidably affected by models and experimental uncertainties, an uncertainty analysis is fundamental in order to have a complete validation study. In this paper, statistical uncertainty and sensitivity analyses are used to validate the thermal hydraulic features of the POLCA-T code, based on a one dimensional model of the following macroscopic BFBT exercises: (1) single and two phase bundle pressure drop, (2) steady-state cross-sectional averaged void fraction, (3) transient cross-sectional averaged void fraction and (4) steady-state critical power tests. The Latin hypercube sampling (LHS) strategy was chosen since it densely stratifies across the range of each uncertain input probability distribution, allowing a much better coverage of the input uncertainties than simple random sampling (SRS). The results show that POLCA-T predictions on pressure drop and void fractions under a wide range of conditions are within the validation limits imposed by the uncertainty analysis, while the accuracy of critical power predictions depends much on the boundary and input conditions.