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Application of Bayesian approaches to nuclear reactor severe accident analysis

原子炉シビアアクシデント解析へのベイズ統計の適用

Zheng, X.; 玉置 等史; 塩津 弘之; 杉山 智之; 丸山 結

Zheng, X.; Tamaki, Hitoshi; Shiotsu, Hiroyuki; Sugiyama, Tomoyuki; Maruyama, Yu

Nuclear reactor severe accident simulation involves uncertainties, which may result from incompleteness of modeling of accident scenarios, selection of alternative models and unrealistic setting of parameters during the numerical simulation, etc. Both deterministic and probabilistic methods are required to reach reasonable estimation of risk for severe accidents. Computational codes are widely used for the deterministic accident simulations. Bayesian approaches, including both parametric and nonparametric, are applied to the simulation-based severe accident researches at Japan Atomic Energy Agency (JAEA). In the paper, an overview of these research activities is introduced: (1) Dirichlet process models, a nonparametric Bayesian approach, are applied to source term uncertainty and sensitivity analyses; (2) Gaussian process models are applied to the optimization for operations of severe accident countermeasures; (3) Nonparametric models, include models based on Dirichlet process and K-nearest neighbors algorithm, are built to predict the chemical forms of fission products. Simplified models are integrated into the integral severe accident code, THALES2/KICHE; (4) We have also launched the research of dynamic probabilistic risk assessment (DPRA), and because a great number of accident scenarios will be generated during DPRA, Bayesian approaches would be useful for the boosting of computational efficiency.

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