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, 237(5), p.947 - 957, 2023/10

https://doi.org/10.1177/1748006X221091604

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%.

Development of dynamic PRA methodology for external hazards in sodium-cooled fast reactor via applying Markov chain Monte Carlo method to severe accident analysis code; Assessment of accident management of assigning independent emergency diesel generators to each air cooler

Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi

Proceedings of 30th International Conference on Nuclear Engineering (ICONE30) (Internet), 10 Pages, 2023/05

Quantification of risk dilution induced by correlation parameters in dynamic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro; Tanaka, Yoichi*; Ishikawa, Jun

Proceedings of the Institution of Mechanical Engineers, Part O; Journal of Risk and Reliability, 11 Pages, 2023/00

https://doi.org/10.1177/1748006X231167201

Development of dynamic PRA methodology for external hazards (Application of CMMC method to severe accident analysis code)

Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi

Dai-26-Kai Doryoku, Enerugi Gijutsu Shimpojiumu Koen Rombunshu (Internet), 4 Pages, 2022/07

https://doi.org/10.1299/jsmepes.2022.26.A234

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.

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.

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

Maruyama, Yu; Yoshida, Kazuo

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

https://doi.org/10.3327/jaesjb.63.7_517

no abstracts in English

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

https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/4717.pdf

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.

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.

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

Uncertainty quantification for severe accident scenarios

Zheng, X.

no journal, ,