Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment

Francesco Di Maio, Claudia Picoco, Enrico Zio and Valentin Rychkov

Reliability Engineering and System Safety, 2017, vol. 162, issue C, 122-138

Abstract: The safety assessment of Nuclear Power Plants makes use of Thermal-Hydraulic codes for the quantification of the safety margins with respect to upper/lower safety thresholds, when postulated accidental scenarios occur. To explicitly treat uncertainties in the safety margins estimates within the Risk-Informed Safety Margin Characterization (RISMC) framework, we resort to the concept of Dynamic Probabilistic Safety Margin (DPSM). We propose to add to the framework a sensitivity analysis that calculates how much the Thermal-Hydraulic (TH) code inputs affect the DPSM, in support to the selection of the most proper probabilistic safety assessment method to be used for the problem at hand, between static or dynamic methods (e.g., Event Trees (ETs) or Dynamic ETs (DETs), respectively). Two case studies are considered: firstly a Station Black Out followed by a Seal Loss Of Coolant Accident (LOCA) for a 3-loops Pressurized Water Reactor (PWR), whose dynamics is simulated by a MAAP5 model and, secondly, the accidental scenarios that can occur in a U-Tube Steam Generator, whose dynamics is simulated by a SIMULINK model. The results show that the sensitivity analysis performed on the DPSM points out that an ET-based analysis is sufficient in one case, whereas a DET-based analysis is needed for the other case.

Keywords: Dynamic Probabilistic Safety Margin; Risk-Informed Safety Margin Characterization; Integrated Deterministic Probabilistic Safety Assessment; Dynamic Event Tree; Sensitivity analysis; Seal LOCA; U-Tube Steam Generator (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:162:y:2017:i:c:p:122-138

DOI: 10.1016/j.ress.2017.01.020

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