Event sequence assessment of deep snow in sodium-cooled fast reactor based on continuous Markov chain Monte Carlo method with plant dynamics analysis |
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| Authors: | Takashi Takata Emiko Azuma |
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| Affiliation: | 1. Fast Reactor Computational Engineering Department, Advanced Nuclear System Research and Development Center, Japan Atomic Energy Agency, Oarai, Japan;2. Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Suita, Japan |
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| Abstract: | ![]()
Margin assessment of a nuclear power plant against external hazards is one of the most important issues after Fukushima Dai-ichi Nuclear Power Plant Accident. In this paper, a new approach has been developed to assess the plant status during external hazards and countermeasures against them in operation quantitatively and stochastically. A continuous Markov chain Monte Carlo (CMMC) method is applied and coupled with a plant dynamics analysis. In the CMMC method, a subsequence plant status is determined by the latest state (Markov chain) and the status is evaluated from the plant dynamics analysis. A failure or success of safety function of plant component is also evaluated stochastically based on a latest state of plant or hazard. A numerical investigation of plant dynamics analysis against a snow hazard is also carried out in a loop type sodium-cooled fast reactor so as to assess the margin against the hazard. |
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| Keywords: | External hazard snow CMMC plant dynamics analysis sodium-cooled fast reactor |
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