Application of quasi-Monte Carlo and importance sampling toMonte Carlo-based fault tree quantification for seismic probabilistic risk assessment of nuclear power plants IntroductionProbabilistic risk assessment (PRA) is a methodology to identify weaknesses in large and complex systems and to enhance their safety. Therefore, PRA has been applied to the safety assessment of nuclear power plants, including nextgeneration reactors, and the most widespread light-water reactors (Yamano et al., 2018; Sato andOhashi, 2020). The importance of PRA against external events, such as seismic activities and tsunamis, is re-recognized after the Fukushima Daiichi Nuclear Power Plant accident to prevent severe accidents. In Japan, after this accident, the Nuclear Regulation Authority (NRA, J) started to use seismic PRAs in its periodic safety reviews of nuclear power plants (Fuketa, 2014). In this assessment, managing the correlated failures of systems, components, and structures (SSCs) brought on by seismic ground motion is essential. The reason is that this inter-dependency of SSCs negatively impacts the reliability of redundant accident mitigation systems or the entire plant and significantly affects PRA assessment results. The root cause

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. These 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, the importance sampling is suitable for assumed low ground motion level, and that the parallel computing enables practical uncertainty and importance analyses. 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.

No abstract