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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, 237(5), p.947 - 957, 2023/10
Times Cited Count：4 Percentile：69.72(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%.
Kubo, Kotaro
Science and Technology of Nuclear Installations, 2023, p.7402217_1 - 7402217_12, 2023/06
Times Cited Count：0 Percentile：0.01(Nuclear Science & Technology)Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi
Proceedings of 30th International Conference on Nuclear Engineering (ICONE30) (Internet), 10 Pages, 2023/05
Kubo, Kotaro; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*
Journal of Nuclear Science and Technology, 60(4), p.359 - 373, 2023/04
Times Cited Count：5 Percentile：78.52(Nuclear Science & Technology)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.
Kubo, Kotaro; Tanaka, Yoichi*; Ishikawa, Jun
Proceedings of the Institution of Mechanical Engineers, Part O; Journal of Risk and Reliability, 11 Pages, 2023/00
Times Cited Count：0 Percentile：0.01(Engineering, Multidisciplinary)Zheng, X.; Tamaki, Hitoshi; Takahara, Shogo; Sugiyama, Tomoyuki; Maruyama, Yu
Proceedings of Probabilistic Safety Assessment and Management (PSAM16) (Internet), 10 Pages, 2022/09
Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi
Dai-26-Kai Doryoku, Enerugi Gijutsu Shimpojiumu Koen Rombunshu (Internet), 4 Pages, 2022/07
Identifying accident scenarios that could lead to severe accidents and evaluating their frequency of occurrence are essential issues. This study aims to establish the methodology of the dynamic Probabilistic Risk Assessment (PRA) for sodium-cooled fast reactors that can consider the time dependency and the interdependence of each event. Specifically, the Continuous Markov chain Monte Carlo (CMMC) method is newly applied to the SPECTRA code, which analyzes the severe accident conditions of nuclear reactors, to develop an evaluation methodology for typical external hazards. Currently, a fault-tree model of air coolers of decay heat removal system is implemented as the CMMC method, and a series of preliminary analysis of the plant's transient characteristics under the scenario of volcanic ashfall has been conducted.
Kubo, Kotaro; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*
Journal of Nuclear Science and Technology, 59(3), p.357 - 367, 2022/03
Times Cited Count：5 Percentile：65.59(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.
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.
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.
Maruyama, Yu; Yoshida, Kazuo
Nihon Genshiryoku Gakkai-Shi ATOMO
, 63(7), p.517 - 522, 2021/07
no abstracts in English
Kubo, Kotaro; Zheng, X.; Tanaka, Yoichi; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*
Proceedings of 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL 2020 and PSAM-15) (Internet), p.2279 - 2286, 2020/11
Probabilistic risk assessment (PRA) is one of the methods used to assess the risks associated with large and complex systems. When the risk of an external event is evaluated using conventional PRA, a particular limitation is the difficulty in considering the timing at which nuclear power plant structures, systems, and components fail. To overcome this limitation, we coupled thermal-hydraulic and external-event simulations using Risk Assessment with Plant Interactive Dynamics (RAPID). Internal flooding was chosen as the representative external event, and a pressurized water reactor plant model was used. Equations based on Bernoulli's theorem were applied to flooding propagation in the turbine building. In the analysis, uncertainties were taken into account, including the flow rate of the flood water source and the failure criteria for the mitigation systems. In terms of recovery action, isolation of the flood water source by the operator and drainage using a pump were modeled based on several assumptions. The results indicate that the isolation action became more effective when combined with drainage.
Zheng, X.; Mandelli, D.*; Alfonsi, A.*; Smith, C.*; Sugiyama, Tomoyuki
Proceedings of 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL 2020 and PSAM-15) (Internet), p.2176 - 2183, 2020/11
Tanaka, Yoichi; Tamaki, Hitoshi; Zheng, X.; Sugiyama, Tomoyuki
Proceedings of 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL 2020 and PSAM-15) (Internet), p.2195 - 2201, 2020/11
Kubo, Kotaro; Zheng, X.; Ishikawa, Jun; Sugiyama, Tomoyuki; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*
Proceedings of Asian Symposium on Risk Assessment and Management 2020 (ASRAM 2020) (Internet), 11 Pages, 2020/11
Dynamic probabilistic risk assessment (PRA) enables a more realistic and detailed analysis than classical PRA. However, the trade-off for these improvements is the enormous computational cost associated with performing a large number of thermal-hydraulic (TH) analyses. In this study, based on machine learning (ML), we aim to reduce these costs by skipping the TH analysis. For the ML algorithm, we selected a support vector machine; we built it using a high-fidelity/high-cost detailed model and low-fidelity/low-cost simplified model. As a result, the computational costs could be reduced by approximately 80% without significantly decreasing the accuracy under the assumed conditions.
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.
Suzudo, Tomoaki; Hayashi, Koji
Proc. of a Symp. on Nuclear Reactor Surveillance and Diagnostics,Vol. 1, 12 Pages, 1991/00
no abstracts in English
Tamaki, Hitoshi
no journal, ,
no abstracts in English
