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Application of integrated deterministic-probabilistic safety analysis to assessment of severe accident management effectiveness in Nordic BWRsстатья Исследовательская статья
- Авторы: Kudinov P., Galushin S., Grishchenko D., Yakush S., Basso S., Konovalenko A., Davydov M.
- Сборник: Proceedings of the 17th International Topical Meeting on Nuclear Reactor Thermal-Hydraulics (NURETH-17)
- Год издания: 2017
- Издательство: American Nuclear Society
- Местоположение издательства: United States
- Первая страница: 21590
- Аннотация: The goal of this work is to assess effectiveness of severe accident management strategy in Nordic type boiling water reactors (BWRs). Corium melt released into a deep pool of water below reactor vessel is expected to be fragmented to form a porous debris bed coolable by natural circulation of coolant. However, there is a risk that energetic steam explosion or non-coolable debris can threaten containment integrity. Both stochastic accident scenario (aleatory) and modeling (epistemic) uncertainties contribute to the risk assessment. Namely, the effects of melt release characteristics (jet diameter, melt composition, superheat), water pool conditions (i.e. depth and subcooling) at the time of the release, and modeling assumptions have to be quantified in a consistent manner. In order to address the uncertainty, we develop a Risk Oriented Accident Analysis framework (ROAAM+) where all stages of the accident progression are simulated using a set of models coupled through initial and boundary conditions. The analysis starts from plant damage states determined in PSA Level-1 and follows time dependent accident scenarios of core degradation, vessel failure, melt release, steam explosion and debris bed formation and coolability. In order to achieve computational efficiency sufficient for extensive sensitivity, uncertainty, and risk analysis the surrogate modeling approach is used. In the development of simplified but computationally efficient surrogate models (SM), we employ databases of solutions obtained by detailed but computationally expensive full models (FM). The process includes iterative refining of the framework, full and surrogate models in order to achieve completeness, consistency, and transparency in the review of the analysis results. In the paper we present results of the analysis aimed at quantification of uncertainty in the conditional containment failure probability. Specifically, we carry out sensitivity analysis using standalone and coupled models in order to identify the most influential scenario and modeling parameters for each sub-model. We assess the impact of the parameters on the prediction of the “load”, “capacity” and also failure probability. Then we quantify the effect of the most influential parameters on the failure probability. The results are presented using the failure domain approach and second order probability analysis, considering the uncertainty in distributions of the input parameters. © 2016 Association for Computing Machinery Inc. All Rights Reserved.
- Добавил в систему: Якуш Сергей Евгеньевич