NUSS-RF: stochastic sampling-based tool for nuclear data sensitivity and uncertainty quantification

Abstract

The “blackbox” approach of stochastic sampling (SS) methods for simultaneous nuclear data uncertainty quantification is powerful except it reveals little of the individual uncertainty contributions. In this work, the SS-based tool “NUSS” (nuclear data uncertainty stochastic sampling) developed at PSI is updated to “NUSS- RF” which estimates individual nuclear data uncertainty contributions to the total output uncertainty. The new capability is based on the Random balance design and Fourier amplitude sensitivity testing methods, both belonging to the so-called global sensitivity analysis. First, the implementation of NUSS-RF is tested using a mathematical function, followed by the sensitivity and uncertainty analysis for 235U(n,γ) and 238U(n,γ) cross sections in Godiva and BWR pincell benchmarks, respectively. The results are compared to the deterministic sensitivity/uncertainty “Sandwich Rule” approach which is local. For uncorrelated inputs, both methods have the equivalent interpretation of the input uncertainty contribution (in terms of variance fraction and sensitivity index), hence producing good agreement in the results. For correlated inputs, the discrepancy between the two methods broadens with the extent of the correlations.