Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
成川 隆文; 山口 彰*; Jang, S.*; 天谷 政樹
Proceedings of 14th International Conference on Probabilistic Safety Assessment and Management (PSAM-14) (USB Flash Drive), 10 Pages, 2018/09
The reduction of epistemic uncertainty for safety-related events that rarely occur or require high experimental costs is a key concern for researchers worldwide. In this study, we develop a new framework to effectively reduce parameter uncertainty, which is one of the epistemic uncertainties, by using the Bayesian optimal experimental design. In the experimental design, we used a decision theory that minimizes the Bayes generalization loss. For this purpose, we used the functional variance, which is a component of widely applicable information criterion, as a decision criterion for selecting informative data points. Then, we conducted a case study to apply the proposed framework to reduce the parameter uncertainty in the fracture boundary of a non-irradiated, pre-hydrided Zircaloy-4 cladding tube specimen under loss-of-coolant accident (LOCA) conditions. The results of our case study proved that the proposed framework greatly reduced the Bayes generalization loss with minimal sample size compared with the case in which experimental data were randomly obtained. Thus, the proposed framework is useful for effectively reducing the parameter uncertainty of safety-related events that rarely occur or require high experimental costs.
Zheng, X.; 玉置 等史; 石川 淳; 杉山 智之; 丸山 結
Proceedings of 14th International Conference on Probabilistic Safety Assessment and Management (PSAM-14) (USB Flash Drive), 10 Pages, 2018/09
Several types of uncertainties exist during the simulation of a severe accident. These may result from incomplete knowledge about the plant systems, accident progression and oversimplified numerical models. Among them, parameter uncertainty can be treated via Monte-Carlo-sampling-based methods. To tackle the severe accident scenario uncertainty, we must resort to advanced dynamic probabilistic risk assessment (PRA) methods. In this paper, authors reviewed the previous dynamic PRA methods and tools, and then performed a preliminary scenario uncertainty analysis, by using an integrated SA code (THALES2) and a scenario generator (RAPID, risk assessment with plant interactive dynamics), both being developed at JAEA. THALES2 is a fast-running severe accident code for the simulation of severe accident progression and source term in light water reactors. Typical scenarios of station-blackout (SBO)-initiated accidents in boiling water reactors are generated and simulated. The dynamic event tree (DET) method is applied to consider the stochastic uncertainties during the scenario progression. Major groups of SBO sequences with the similar accident characteristics can be found. To provide a reference value for risk, a conditional core damage frequency is calculated accordingly. This is a preliminary analysis for severe accident scenario uncertainty quantification at JAEA, and further DPRA researches are in progress.