Estimation of influence of implicit effect due to multi-group cross-section perturbations on uncertainty analysis in PWR-UO and -MOX lattice calculations

Fujita, Tatsuya

Journal of Nuclear Science and Technology, 62(8), p.731 - 739, 2025/08

https://doi.org/10.1080/00223131.2025.2470234

This study estimated the influence of implicit effect on the k-infinity uncertainty in the PWR-UO and -MOX fuel lattice geometries. Firstly, the preliminary investigation was performed, where the influence of implicit effect was roughly estimated based on the sandwich formula using the cross-section (XS) covariance matrix and the sensitivity coefficient. It was confirmed that the influence of implicit effect became large in the fission and (n,) reactions of heavy nuclides and the change of this dependence was small for the burnup of UO and MOX fuel assemblies. Then, focussing on the heavy nuclides, the influence of implicit effect was compared under several energy group conditions of the XS covariance matrix and neutron transport calculation. For Pu and Pu, the noticeable influence of implicit effect was observed in MOX fuel pin-cell geometry. However, increasing the number of energy groups for neutron transport calculations and that of the XS covariance matrix can reduce the influence of implicit effect. Consequently, by appropriately setting the number of energy groups for neutron transport calculations and that of the XS covariance matrix, it became practically possible not to explicitly consider the implicit effect during the random sampling.

Uncertainty quantification for severe-accident reactor modelling; Results and conclusions of the MUSA reactor applications work package

Brumm, S.*; Gabrielli, F.*; Sanchez Espinoza, V.*; Stakhanova, A.*; Groudev, P.*; Petrova, P.*; Vryashkova, P.*; Ou, P.*; Zhang, W.*; Malkhasyan, A.*; et al.

Annals of Nuclear Energy, 211, p.110962_1 - 110962_16, 2025/02

https://doi.org/10.1016/j.anucene.2024.110962

Hybrid data assimilation methods for nuclear-data-induced uncertainties

Maruyama, Shuhei; Yamamoto, Akio*; Endo, Tomohiro*

Journal of Nuclear Science and Technology, 14 Pages, 2025/00

https://doi.org/10.1080/00223131.2025.2500579

Quantifying uncertainty induced by scattering angle distribution using maximum entropy method

Maruyama, Shuhei; Yamamoto, Akio*; Endo, Tomohiro*

Annals of Nuclear Energy, 205, p.110591_1 - 110591_13, 2024/09

https://doi.org/10.1016/j.anucene.2024.110591

A Comparative study of efficient sampling techniques for uncertainty quantification due to cross-section covariance data

Fujita, Tatsuya

Proceedings of International Conference on Physics of Reactors (PHYSOR 2024) (Internet), p.718 - 727, 2024/04

https://doi.org/10.13182/PHYSOR24-43640

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.

Convergence behavior of statistical uncertainty in probability table for cross section in unresolved resonance region

Tada, Kenichi; Endo, Tomohiro*

Journal of Nuclear Science and Technology, 60(11), p.1397 - 1405, 2023/11

https://doi.org/10.1080/00223131.2023.2204102

The probability table method is a well-known method for addressing self-shielding effects in the unresolved resonance region. A long computational time is required to generate the probability table. The effective way to reduce the generation time of the probability table is the reduction of the number of ladders. The purpose of this study is the estimation of the optimal number of ladders using the statistical uncertainty in the probability table. To this end, the statistical uncertainty quantification method of the probability table was developed and the convergence behavior of the statistical uncertainty was investigated. The product of the probability table and the average cross section was considered the target of the statistical uncertainty. The convergence rate was affected by the average level spacing and reduced neutron width. The generation time of the probability table was less than half when the input parameter was changed from the number of ladders to the tolerance value.

Hierarchical Bayesian modeling to quantify fracture limit uncertainty of high-burnup advanced fuel cladding tubes under loss-of-coolant accident conditions

Narukawa, Takafumi; Hamaguchi, Shusuke*; Takata, Takashi*; Udagawa, Yutaka

Nuclear Engineering and Design, 411, p.112443_1 - 112443_12, 2023/09

https://doi.org/10.1016/j.nucengdes.2023.112443

Dynamic probabilistic risk assessment of seismic-induced flooding in pressurized water reactor by seismic, flooding, and thermal-hydraulics simulations

Kubo, Kotaro; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Journal of Nuclear Science and Technology, 60(4), p.359 - 373, 2023/04

https://doi.org/10.1080/00223131.2022.2100837

Probabilistic risk assessment (PRA) is an essential approach to improving the safety of nuclear power plants. However, this method includes certain difficulties, such as modeling of combinations of multiple hazards. Seismic-induced flooding scenario includes several core damage sequences, i.e., core damage caused by earthquake, flooding, and combination of earthquake and flooding. The flooding fragility is time-dependent as the flooding water propagates from the water source such as a tank to compartments. Therefore, dynamic PRA should be used to perform a realistic risk analysis and quantification. This study analyzed the risk of seismic-induced flooding events by coupling seismic, flooding, and thermal-hydraulics simulations, considering the dependency between multiple hazards explicitly. For requirements of safety improvement, especially in light of the Fukushima Daiichi Nuclear Power Plant accident, sensitivity analysis was performed on the seismic capacity of systems, and the effectiveness of alternative steam generator injection by a portable pump was estimated. We demonstrate the use of this simulation-based dynamic PRA methodology to evaluate the risk induced by a combination of hazards.

Hierarchical Bayes model to quantify fracture limit uncertainty of high-burnup advanced fuel cladding tubes under LOCA conditions

Narukawa, Takafumi; Hamaguchi, Shusuke*; Takata, Takashi*; Udagawa, Yutaka

Proceedings of Asian Symposium on Risk Assessment and Management 2022 (ASRAM 2022) (Internet), 11 Pages, 2022/12

Quasi-Monte Carlo sampling method for simulation-based dynamic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Journal of Nuclear Science and Technology, 59(3), p.357 - 367, 2022/03

https://doi.org/10.1080/00223131.2021.1971119

Dynamic probabilistic risk assessment (PRA), which handles epistemic and aleatory uncertainties by coupling the thermal-hydraulics simulation and probabilistic sampling, enables a more realistic and detailed analysis than conventional PRA. However, enormous calculation costs are incurred by these improvements. One solution is to select an appropriate sampling method. In this paper, we applied the Monte Carlo, Latin hypercube, grid-point, and quasi-Monte Carlo sampling methods to the dynamic PRA of a station blackout sequence in a boiling water reactor and compared each method. The result indicated that quasi-Monte Carlo sampling method handles the uncertainties most effectively in the assumed scenario.