ABSTRACT This study confirmed the efficiency of a combined approach of the control variates (CV) and the Latin hypercube sampling (LHS), which enhanced the random-sampling-based uncertainty quantification due to cross-section (XS) covariance data, by considering the effect of statistical variation. The convergence performance for the uncertainty of infinite multiplication factor (k-infinity) during the random sampling was compared between several efficient sampling techniques such as the antithetic sampling (AS), LHS, CV, and the combined approaches of them in the PWR-UO2 fuel assembly geometry. The k-infinity uncertainty was evaluated by statistically processing several times Serpent2 calculations using perturbed ACE-formatted XS files based on ENDF/B-VIII.0. CV+AS and CV+LHS showed similar higher efficiency than AS, LHS, and CV. In addition, sensitivity analyses were performed to select alternative parameters used in CV. The calculation results obtained in fuel pin-cell and 3 × 3 mini fuel lattice geometries showed almost the same performance as alternative parameters. The reason was qualitatively considered that the influence of XS covariance data for U isotopes was more dominant though the 3 × 3 mini fuel lattice geometry can capture that for Gd isotopes. Consequently, the applicability of CV+LHS for the improvement of convergence performance to evaluate the k-infinity uncertainty during the random sampling was confirmed.
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