Uncertainty quantification for the first-cycle modeling and simulations of the BEAVRS benchmark problem

Abstract

In this paper, the uncertainty quantification has been implemented to the first-cycle modeling and simulations for the BEAVRS benchmark problem, propagating the nuclear-data uncertainties to the key core parameters. NJOY has been applied to generate the nuclear-data covariance library based on ENDF/B-VII.1 in the research. The statistical sampling method has been utilized for the uncertainty quantification, based on the conventional “two-step” scheme. First, the nuclear-data uncertainties are propagated to the few-group constants through the lattice modeling and simulations; and then the uncertainties of the key core parameters are quantified through the reactor modeling and simulations. As interests, the uncertainties have been quantified to the critical boron concentrations, axial-power offset and radial assembly-power distributions through the whole life of first cycle. From the numerical results, it can be observed that for the critical boron concentration, the nuclear-data introduced uncertainties can up to be 51 ppm as maximum; for the axial-power offset, the relative uncertainties vary within 1.3% and for the radial assembly-power distributions, the maximum relative uncertainties is about 3.8% at BOL and 0.8% at EOL.