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Koike, Akari*; Nemoto, Masaya*; Nakashima, Risako*; Sakai, Takaaki*; Doda, Norihiro; Tanaka, Masaaki
Proceedings of 2023 International Congress on Advanced in Nuclear Power Plants (ICAPP 2023) (Internet), 2 Pages, 2023/04
To evaluate the effect of the operator's recognition of the accident management (AM) necessity on plant safety, the operator's recognition of the AM necessity was modeled as a function of time-dependent success probability, and dynamic PRA analyses were performed for a sodium-cooled fast reactor during abnormal snowfall. The analysis results showed that the operator's recognition of the snowfall can avoid the core damage at an earlier stage after the accident.
Koike, Akari*; Nakashima, Risako*; Nemoto, Masaya*; Sakai, Takaaki*; Doda, Norihiro; Tanaka, Masaaki
Proceedings of 12th Japan-Korea Symposium on Nuclear Thermal Hydraulics and Safety (NTHAS12) (Internet), 4 Pages, 2022/10
Due to global warming, the amount of snowfall in abnormal snowfall events may increase in the future. In order to evaluate the effect of global warming on the probability of exceeding the limit temperature at the core outlet as a core damage factor in a sodium-cooled fast reactor, a hazard curve of snowfall was developed considering global warming, and a dynamic PRA was performed. As a result, it was found that the amount of snowfall in abnormal snowfall events increases due to global warming, and the probability of exceeding the limit temperature increases.
Kato, Atsushi; Ide, Akihiro*; Shibata, Akihiro*; Ishizaki, Miku*; Tanaka, Futoshi*; Sakaba, Hiroshi*; Nishizaki, Chihiro*; Sawairi, Tsuyoshi*
no journal, ,
Sodium fast reactor needs long time decay heat removal compared with Light water reactor. This reports trial evaluation of dynamic PRA application on Sodium cooled fast reactor in terms of decay heat. Thanks to this application, dominant risk factor could be cleared out.
Zheng, X.; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Maruyama, Yu
no journal, ,
no abstracts in English
Zheng, X.; Takahara, Shogo; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Maruyama, Yu
no journal, ,
no abstracts in English
Zheng, X.; Tamaki, Hitoshi; Sugiyama, Tomoyuki
no journal, ,
Probabilistic risk assessment (PRA) is an approach to quantifying risk of accidents including their stochastic uncertainties and consequences. However, because of inadequate understanding of phenomena, PRA results involve epistemic uncertainties. In this study, from the perspective of probability-of-frequency, we compared approaches of conventional PRA and dynamic PRA. Dynamic PRA has the advantages in the treatment of dependencies between accident progression and failure modes, so it is an advanced approach possible to mitigate epistemic uncertainties.
Zheng, X.; Tamaki, Hitoshi; Shibamoto, Yasuteru; Takada, Tsuyoshi
no journal, ,
JAEA is constructing a dynamic probabilistic risk assessment (PRA) approach which integrates deterministic accident analysis and probabilistic reliability analysis, and developing an associated computational tool, RAPID. Taking the estimation of failure probability of machines as an example, this paper identifies latent sources of epistemic uncertainties in PRA. To reduce such epistemic uncertainties, authors have proposed to apply probabilistic physics-of-failure by dynamically modeling the interaction between operational conditions and failure probabilities of machines. Moreover, authors have implemented automatic coupling techniques between simulation codes in RAPID.
Zheng, X.; Tamaki, Hitoshi; Maruyama, Yu; Takada, Tsuyoshi; Narukawa, Takafumi*; Takata, Takashi*
no journal, ,
The dynamic probabilistic risk assessment (dynamic PRA) methodology explicitly treats the dynamics of event progression, enabling risk assessment that does not require predefined scenarios or success criteria. Based on these characteristics, from the perspective of risk triplet, we investigated the concept and measures of risk importance in dynamic PRA. Furthermore, the importance measures were applied to a dynamic PRA to evaluate their effectiveness. In this presentation, the authors provide a review on the application status of traditional risk importance measures in nuclear regulatory activities, and by using quantities such as release amount (consequence) of time-dependent source term to the environment and associated containment failure frequency (CFF), they confirm the applicability of the proposed risk importance measure to dynamic Level 2 PRA.
Narukawa, Takafumi*; Takata, Takashi*; Zheng, X.; Tamaki, Hitoshi; Maruyama, Yu; Takada, Tsuyoshi
no journal, ,
The dynamic probabilistic risk assessment (dynamic PRA) methodology explicitly treats the dynamics of event progression, enabling risk assessment that does not require predefined scenarios or success criteria. Based on these characteristics, from the perspective of risk triplet, we investigated the concept and measures of risk importance in dynamic PRA. Furthermore, the proposed importance measures were applied to a dynamic PRA to evaluate their effectiveness. This presentation reports the results of the investigation into the concept and measures of risk importance.
Koike, Akari*; Sakai, Takaaki*; Doda, Norihiro; Tanaka, Masaaki
no journal, ,
To evaluate the effect of the operator's recognition of the accident management (AM) necessity on plant safety, the operator's recognition of the AM necessity was modeled as a function of time-dependent success probability, and dynamic PRA analyses using the Continuous Markov chain Monte Carlo method (CMMC) were performed for a sodium-cooled fast reactor during abnormal snowfall event. The analysis results showed that AM was effective in delaying the core damage and that the recognition timing by the operator was an important factor in avoiding the core damage after the accident.
Zheng, X.; Tamaki, Hitoshi; Shibamoto, Yasuteru; Maruyama, Yu
no journal, ,
The nuclear industry is expressing a growing interest in the research and use of artificial intelligence and machine learning (AI/ML) technology to improve plant operational performance and reduce the risks associated with nuclear power generation. JAEA is applying the AI/ML technology to advancing researches on severe accidents and probabilistic risk assessment (PRA). To efficiently perform dynamic PRA and uncertainty quantification of source terms, both simulation-based, we are introducing surrogate models trained via machine learning to estimate core damage frequency (conditional core damage probability), to obtain information about the probability distribution of source terms and importance ranking of parameters. AI/ML can be expected to efficiently provide risk and uncertainty information to make rational decisions for the continuous improvement of nuclear safety.