Methodology for Sensitivity and Uncertainty-Based Criticality Safety Validation
Monte Carlo radiation transport software is used extensively for determining the effective multiplication factor k of fissionable systems towards assessing the criticality safety of operations. Understanding the predictive capability of the Monte Carlo software and nuclear data libraries for calculations of specific applications is vital towards making these assessments. Recently, sensitivity and uncertainty (S/U) methods have been developed for this purpose. A computational tool has been developed that uses the S/U methods with MCNP6 to select critical experiment benchmarks that have similar sources of computational bias, and extreme value theory is then used to determine suitable and conservative calculational margins. The S/U techniques are then used to perform nuclear data adjustments, which are then used to determine a suitable margin of subcriticality from cross section uncertainties. This document gives an overview of the theory of S/U methods used for criticality safety validation. First an overview is given for using the sensitivity coefficients and nuclear co variance data towards determining neutronic similarity factors is then discussed. Next, the extreme value theory (EVT) to determine a calculational margin is given along with how the similarity factors are used in the analysis. Then, the Generalized Linear Least Squares (GLLS) method for performing data adjustment and how that method is used to reject benchmarks and compute adjusted nuclear data uncertainties in k are discussed. Last, a discussion of the margin of subcriticality is given along with how to apply of the GLLS data adjustment methods to quantify the effect of nuclear data variability on k.