Dynamic fault trees are important tools for modeling systems with sequence failure behaviors. The Markov chain state space method is the only analytical approach for a repairable dynamic fault tree (DFT). However, this method suffers from state space explosion, and is not suitable for analyzing a large scale repairable DFT. Furthermore, the Markov chain state space method requires the components’ time-to-failure to follow exponential distributions, which limits its application. In this study, motivated to efficiently analyze a repairable DFT, a Monte Carlo simulation method based on the coupling of minimal cut sequence set (MCSS) and its sequential failure region (SFR) is proposed. To validate the proposed method, a numerical case was studied. The results demonstrated that our proposed approach was more efficient than other methods and applicable for repairable DFTs with arbitrary time-to-failure distributed components. In contrast to the Markov chain state space method, the proposed method is straightforward, simple and efficient.

The Fukushima nuclear disaster has raised the focus on the reliability and risk evaluation of the spent fuel pool (SFP), especially the fire risk. From a safety point of view, the low decay heat of the spent fuel assemblies and large water inventory may make the accident progress very slow, but a large number of fuel assemblies stored inside the pool and without containment above the SFP building might bring greater risk. For pressurized water reactor HPR1000 nuclear power plant, the reliability and risk of spent fuel pool under internal fire is assessed. The fire probabilistic safety analysis method is used to assess the reliability and risk of spent fuel pool under internal fire. Through quantitative analysis, we can know more about which aspects play a leading role in the internal fire risk contribution.

Latin Hypercube Design (LHD) is widely used in computer simulation to solve large-scale, complex, nonlinear problems. The high-dimensional LHD (HLHD) problem is one of the crucial issues and has been a large concern in the long run. This paper proposes an improved Hybrid Particle Swarm Optimization (IHPSO) algorithm to find the near-optimal HLHD by increasing the particle evolution speed and strengthening the local search. In the proposed algorithm, firstly, the diversity of the population is ensured through comprehensive learning. Secondly, the Minimum Point Distance (MPD) method is adopted to solve the oscillation problem of the PSO algorithm. Thirdly, the Ranked Ordered Value (ROV) rule is used to realize the discretization of the PSO algorithm. Finally, local and global searches are executed to find the near-optimal HLHD. The comparisons show the superiority of the proposed method compared with the existing algorithms in obtaining the near-optimal HLHD.

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The Fukushima nuclear disaster has raised the importance on the reliability and risk research of the spent fuel pool (SFP), including the risk of internal events, fire, external hazards and so on. From a safety point of view, the low decay heat of the spent fuel assemblies and large water inventory in the SFP has made the accident progress goes very slow, but a large number of fuel assemblies are stored inside the spent fuel pool and without containment above the SFP building, it still has an unignored risk to the safety of the nuclear power plant. In this paper, a standardized approach for performing a holistic and comprehensive evaluation approach of the SFP risk based on the probabilistic safety analysis (PSA) method has been developed, including the Level 1 SFP PSA and Level 2 SFP PSA and external hazard PSA. The research scope of SFP PSA covers internal events, internal flooding, internal fires, external hazards and new risk source-fuel route risk is also included. The research will provide the risk insight of Spent Fuel Pool operation, and can help to make recommendation for the prevention and mitigation of SFP accidents which will be applicable for the SFP configuration risk management.