検索結果： 3 件中 1件目～3件目を表示

- 1

Initialising ...

Initialising ...

Initialising ...

Initialising ...

Initialising ...

Initialising ...

Initialising ...

Zheng, X.; 玉置 等史; 塩津 弘之; 杉山 智之; 丸山 結

Proceedings of Asian Symposium on Risk Assessment and Management 2017 (ASRAM 2017) (USB Flash Drive), 11 Pages, 2017/11

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.

Zheng, X.; 伊藤 裕人; 玉置 等史; 丸山 結

Journal of Nuclear Science and Technology, 53(3), p.333 - 344, 2016/03

被引用回数：9 パーセンタイル：69.23(Nuclear Science & Technology)Large-scale computer programs simulate severe accident phenomena and often have a moderate-to-large number of models and input variables. Analytical solutions to uncertainty distributions of interested source terms are impractical, and influential inputs on outputs are hard to discover. Additionally, runs of such computer programs, or integral codes, are time-consuming and hence expensive. This article presents an integrated approach to the uncertainty and sensitivity analysis for nuclear reactor severe accident source terms, with an example which simulates an accident sequence similar to that occurred at Unit 2 of the Fukushima Daiichi Nuclear Power Plant using an integral code, MELCOR. Monte Carlo based uncertainty analysis has been elaborated to investigate released fractions of representative radionuclides, Cs and CsI. In order to estimate sensitivity of inputs, which have a substantial influence on the core melt progression and the transportation process of radionuclides, a variance decomposition method is applied. Stochastic process, specifically a Dirichlet process, is applied to construct a surrogate model in sensitivity analysis as a substitute of the code. The surrogate model is cross-validated by comparing with corresponding results of MELCOR. The analysis with the simpler model avoids laborious computational cost so that importance measures for input factors are obtained successfully.

Zheng, X.; 伊藤 裕人; 川口 賢司; 玉置 等史; 丸山 結

Reliability Engineering & System Safety, 138, p.253 - 262, 2015/06

被引用回数：9 パーセンタイル：39.62(Engineering, Industrial)An important issue for nuclear severe accident is the source tern uncertainty and sensitivity analysis. Generally, thousands of cases are needed to reach a stable result of sensitivity analysis. Based on the limited data obtained by MELCOR analysis, in which the accident at Unit 2 of the Fukushima Daiichi Nuclear Power Plant is used as an example, an approximate stochastic model has been constructed via Bayesian nonparametrics, specifically, the Dirichlet process. The advantage of a nonparametric model is that any deterministic function between explanatory and response variables is not necessary to be determined. The complexity of model will grow automatically as more actual data is observed. The approximate model saves the computational cost and makes it possible to complement thousands of Monte Carlo computation for uncertainty and sensitivity analysis. Probability density functions of uncertainty analysis by MELCOR and the approximate model are obtained and compared. Two densities show great accordance that proves the good predictive ability of the stochastic model. The appropriateness of the approximate model is further validated by the cross-validation through the comparison with actual MELCOR results. Global sensitivity analysis by Sobol' sensitivity index has been performed with the approximate model. Three input parameters are ranked according to their respective influences on the output uncertainty based on first-order and total effect.