4.5 Article

Development of Efficient External Multi-Hazard Risk Quantification Methodology for Nuclear Facilities

期刊

ENERGIES
卷 12, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/en12203925

关键词

multi-hazard; nuclear energy; risk; Boolean algebra; fault tree; sampling; DQFM

资金

  1. National Research Foundation of Korea (NRF) - Korean government [NRF-2017M2A8A4015290]
  2. National Research Foundation of Korea [2017M2A8A4015290] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

Probabilistic safety assessment (PSA) of nuclear facilities on external multi-hazards has become a major issue after the Fukushima accident in 2011. However, the existing external hazard PSA methodology is for single hazard events and cannot cover the impact of multi-hazards. Therefore, this study proposes a methodology for quantifying multi-hazard risks for nuclear energy plants. Specifically, we developed an efficient multi-hazard PSA methodology based on the probability distribution-based Boolean algebraic approach and sampling-based method, which are currently single-hazard PSA methodologies. The limitations of the probability distribution-based Boolean algebraic approach not being able to handle partial dependencies between the components are solved through this sampling-based method. In addition, we devised an algorithm that was more efficient than the existing algorithm for improving the limits of the current sampling-based method, as it required a significant computational time. The proposed methodology was applied from simple examples to single- and multi-hazard PSA examples of actual nuclear power plants. The results showed that the proposed methodology was verified in terms of accuracy and efficiency perspectives. Regarding the sampling-based method, it was confirmed that the proposed algorithm yielded fragility and risk results that have similar degrees of accuracy, even though it extracted a smaller number of samples than the existing algorithm.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据