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Kubo, Kotaro; Tanaka, Yoichi; Hakuta, Yuto*; Arake, Daisuke*; Uchiyama, Tomoaki*; Muramatsu, Ken
Mechanical Engineering Journal (Internet), 10(4), p.23-00051_1 - 23-00051_17, 2023/08
The significance of probabilistic risk assessments (PRAs) of nuclear power plants against external events was re-recognized after the Fukushima Daiichi Nuclear Power Plant accident. Regarding the seismic PRA, handling correlated failures of systems, components, and structures (SSCs) is very important because this type of failure negatively affects the redundancy of accident mitigation systems. The Japan Atomic Energy Research Institute initially developed a fault tree quantification methodology named the direct quantification of fault tree using Monte Carlo simulation (DQFM) to handle SSCs' correlated failures in detail and realistically. This methodology allows quantifying the top event occurrence probability by considering correlated uncertainties related to seismic responses and capacities with Monte Carlo sampling. The usefulness of DQFM has already been demonstrated. However, improving its computational efficiency would allow risk analysts to perform several analyses. Therefore, we applied quasi-Monte Carlo and importance sampling to the DQFM calculation of simplified seismic PRA and examined their effects. Specifically, the conditional core damage probability of a hypothetical pressurized water reactor was analyzed with some assumptions. Applying the quasi-Monte Carlo sampling accelerates the convergence of results at intermediate and high ground motion levels by an order of magnitude over Monte Carlo sampling. The application of importance sampling allows us to obtain a statistically significant result at a low ground motion level, which cannot be obtained through Monte Carlo and quasi-Monte Carlo sampling. These results indicate that these applications provide a notable acceleration of computation and raise the potential for the practical use of DQFM in risk-informed decision-making.
Kubo, Kotaro; Fujiwara, Keita*; Tanaka, Yoichi; Hakuta, Yuto*; Arake, Daisuke*; Uchiyama, Tomoaki*; Muramatsu, Ken*
Proceedings of 29th International Conference on Nuclear Engineering (ICONE 29) (Internet), 8 Pages, 2022/08
After the Fukushima Daiichi Nuclear Power Plant accident, the importance of conducting probabilistic risk assessments (PRAs) of external events, especially seismic activities and tsunamis, was recognized. The Japan Atomic Energy Agency has been developing a computational methodology for seismic PRA, called the direct quantification of fault tree using Monte Carlo simulation (DQFM). When appropriate correlation matrices are available for seismic responses and capacities of components, the DQFM makes it possible to consider the effect of correlated failures of components connected through AND and/or OR gates in fault trees, which is practically difficult when methods using analytical solutions or multidimensional numerical integrations are used to obtain minimal cut set probabilities. The usefulness of DQFM has already been demonstrated. Nevertheless, a reduction of the computational time of DQFM would allow the large number of analyses required in PRAs conducted by regulators and/or operators. We; therefore, performed scoping calculations using three different approaches, namely quasi-Monte Carlo sampling, importance sampling, and parallel computing, to improve calculation efficiency. Quasi-Monte Carlo sampling, importance sampling, and parallel computing were applied when calculating the conditional core damage probability of a simplified PRA model of a pressurized water reactor, using the DQFM method. The results indicated that the quasi-Monte Carlo sampling works well at assumed medium and high ground motion levels, importance sampling is suitable for assumed low ground motion level, and that parallel computing enables practical uncertainty and importance analysis. The combined implementation of these improvements in a PRA code is expected to provide a significant acceleration of computation and offers the prospect of practical use of DQFM in risk-informed decision-making.
