Statistical uncertainty analyses of void fraction predictions using two different sampling strategies: Latin Hypercube and Random Sampling
Paper in proceedings, 2010
In recent years, a more realistic safety analysis of nuclear reactors has been based on best estimate (BE) computer codes. Because their predictions are unavoidably affected by conceptual, aleatory and experimental sources of uncertainty, an uncertainty analysis is needed if useful conclusions are to be obtained from BE codes. In this paper, statistical uncertainty analyses of cross-sectional averaged void fraction calculations using the POLCA-T system code, and based on the BWR Full-Size Fine-Mesh Bundle Test (BFBT) benchmark are presented by means of two different sampling strategies: Latin Hypercube (LHS) and Simple Random Sampling (SRS). LHS has the property of densely stratifying across the range of each input probability distribution, allowing a much better coverage of the input uncertainties than SRS. The aim here is to compare both uncertainty analyses on the BWR assembly void axial profile prediction in steady-state, and on the transient void fraction prediction at a certain axial level coming from a simulated re-circulation pump trip scenario. It is shown that the replicated void fraction mean (either in steady-state or transient conditions) has less variability when using LHS than SRS for the same number of calculations (i.e. same input space sample size) even if the resulting void fraction axial profiles are non-monotonic. It is also shown that the void fraction uncertainty limits achieved with SRS by running 458 calculations (sample size required to cover 95% of 8 uncertain input parameters with a 95% confidence), result in the same uncertainty limits achieved by LHS with only 100 calculations. These are thus clear indications on the advantages of using LHS.
Simple Random Sampling
Latin Hypercube Sampling