Uncertainty analysis of dynamic PRA using nested Monte Carlo simulations and multi-fidelity models

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

Proceedings of Probabilistic Safety Assessment and Management (PSAM16) (Internet), 10 Pages, 2022/09

https://www.iapsam.org/PSAM16/

Cutting edge of application of AI technology to PRA, 3; Advancement of approaches to dynamic PRA and uncertainty quantification using machine learning

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.