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Journal Articles

Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations

Zheng, X.; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Maruyama, Yu

Reliability Engineering & System Safety, 223, p.108503_1 - 108503_12, 2022/07

 Times Cited Count:1 Percentile:0.03(Engineering, Industrial)

Journal Articles

Simulation-based dynamic probabilistic risk assessment of an internal flooding-initiated accident in nuclear power plant using THALES2 and RAPID

Kubo, Kotaro; Zheng, X.; Tanaka, Yoichi; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Proceedings of the Institution of Mechanical Engineers, Part O; Journal of Risk and Reliability, 11 Pages, 2022/04

 Times Cited Count:0 Percentile:0.03(Engineering, Multidisciplinary)

Probabilistic risk assessment (PRA) is a method used to assess the risks associated with large and complex systems. However, the timing at which nuclear power plant structures, systems, and components are damaged is difficult to estimate if the risk of an external event is evaluated using conventional PRA based on event trees and fault trees. A methodology coupling thermal-hydraulic analysis with external event simulations using Risk Assessment with Plant Interactive Dynamics (RAPID) is therefore proposed to overcome this limitation. A flood propagation model based on Bernoulli's theorem was applied to represent internal flooding in the turbine building of the pressurized water reactor. Uncertainties were also taken into account, including the flow rate of the floodwater source and the failure criteria for the mitigation systems. The simulated recovery actions included the operator isolating the floodwater source and using a drainage pump; these actions were modeled using several simplifications. Overall, the results indicate that combining isolation and drainage can reduce the conditional core damage probability upon the occurrence of flooding by approximately 90%.

Journal Articles

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

 Times Cited Count:1 Percentile:48.83(Nuclear Science & Technology)

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.

Journal Articles

Application of polynomial chaos expansion technique to dynamic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro; Tanaka, Yoichi

Proceedings of Asian Symposium on Risk Assessment and Management 2021 (ASRAM 2021) (Internet), 13 Pages, 2021/10

Probabilistic risk assessment (PRA) is extensively used, e.g., in periodical safety review and the reactor oversight process, in nuclear regulation systems to improve the safety of nuclear power plants; however, one limitation of classical PRA is the handling of temporal information such as system failure and core damage timings. To resolve this limitation, the dynamic PRA method has been developed and applied for multiple safety issues; however, its improvement is accompanied by considerable computational costs. In this study, we applied the polynomial chaos expansion (PCE) technique to dynamic PRA with the expectation of reduction in computational cost. In particular, to estimate core damage timing, a PCE-based surrogate model was developed. Then, the surrogate model was applied to dynamic PRA to calculate the conditional core damage probability and core damage timing. Consequently, applying the PCE might efficiently perform these analyses without considerable reduction in accuracy.

Journal Articles

Evaluation of risk dilution effects in dynamic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro; Tanaka, Yoichi

Proceedings of 31st European Safety and Reliability Conference (ESREL 2021) (Internet), p.810 - 817, 2021/09

Probabilistic risk assessment (PRA) is a method of effectively evaluating risks in nuclear power plants and is used in various agencies. Dynamic PRA is attracting considerable attention, as it enables realistic assessment by reducing the assumptions and engineering judgments related to time-dependent failure probability and/or human action reliability. However, it is difficult to remove all assumptions and engineering judgments. Therefore, their effects on assessment results should be understood. This study focuses on the "risk dilution effect," which arises from assumptions about uncertainty. Results showed that this effect causes a difference of about 10% to 20% in the relative change of the conditional core damage probability in the station blackout scenario. This effect should be fully considered when using dynamic PRA in critical decision-making, such as that on regulations.

Journal Articles

Thermal-hydraulics to risk assessment; Roles of thermal-hydraulics simulation to risk assessment

Maruyama, Yu; Yoshida, Kazuo

Nihon Genshiryoku Gakkai-Shi ATOMO$$Sigma$$, 63(7), p.517 - 522, 2021/07

no abstracts in English

Journal Articles

A Comparative study of sampling techniques for dynamic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro; Zheng, X.; Tanaka, Yoichi; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.308 - 315, 2020/10

Dynamic probabilistic risk assessment (PRA) is a method for improving the realism and completeness of 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 simplified accident sequence and compared the results for each method. Quasi-Monte Carlo sampling was found to be the most effective method in this case.

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