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Fujita, Tatsuya
Journal of Nuclear Science and Technology, 62(5), p.470 - 479, 2025/01
Times Cited Count：0 Percentile：0.00(Nuclear Science & Technology)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 and also performed the sensitivity analyses on the influence due to the selection of alternative parameter to apply CV. 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-UO
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+LHS was more efficient than AS, LHS, and CV+AS. In addition, sensitivity analyses were performed to select alternative parameters used in CV. The 3
3 mini fuel lattice calculation can improve the efficiency of CV+LHS. The reason was qualitatively considered that this calculation can capture the influence of XS covariance data 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.
Fujita, Tatsuya
Proceedings of International Conference on Physics of Reactors (PHYSOR 2024) (Internet), p.718 - 727, 2024/04
The convergence process of the k-infinity uncertainty during random-sampling-based uncertainty quantification was compared between several efficient sampling techniques. The k-infinity uncertainty was evaluated by statistically processing several times of SERPENT 2.2.1 calculations using perturbed ACE files based on JENDL-5 cross-section covariance data. The antithetic sampling (AS), the Latin hypercube sampling (LHS), the control variates (CV), and the combination approaches of them were focused on in the present paper. In PWR-UO fuel assembly geometry without the nuclide depletion, as discussed in past studies, AS and LHS showed higher efficient convergence than nominal sampling without any efficient sampling techniques. In terms of CV, though a stand-alone application did not have a large impact on the k-infinity uncertainty convergence, its performance was improved in combination with AS, as discussed in the past study. In addition, a new combined approach of LHS and CV (CV+LHS) was proposed in the present paper. CV+LHS improved the k-infinity uncertainty convergence and was more efficient than CV+AS. The main reason for this improvement was that the convergence for the mean value of alternative parameters in CV was enhanced by applying LHS. Consequently, this study proposed the new combined approach of CV+LHS and confirmed its efficiency performance for the random-sampling-based uncertainty quantification in the PWR-UO fuel assembly geometry. The applicability of CV+LHS for the nuclide-depletion calculations will be confirmed in future studies.
