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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

Dynamic PRA of flooding-initiated accident scenarios using THALES2-RAPID

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.

Journal Articles

Simulation-based Level 2 multi-unit PRA using RAVEN and a simplified thermal-hydraulic code

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

Journal Articles

Enhancement of the treatment of system interactions in a dynamic PRA tool

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

Journal Articles

Case study on sampling techniques using machine learning and simplified physical model for simulation-based dynamic probabilistic risk assessment

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.

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.

Journal Articles

Search for chaotic character of the time series of reactor signals

Suzudo, Tomoaki; Hayashi, Koji

Proc. of a Symp. on Nuclear Reactor Surveillance and Diagnostics,Vol. 1, 12 Pages, 1991/00

no abstracts in English

Oral presentation

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