Quantification of risk dilution induced by correlation parameters in dynamic probabilistic risk assessment of nuclear power plants

Kotaro Kubo, Yoichi Tanaka and Jun Ishikawa

Journal of Risk and Reliability, 2024, vol. 238, issue 3, 464-474

Abstract: Nuclear power plants are critical infrastructures that produce electricity. However, accidents in nuclear power plants can cause considerable consequences such as the release of radioactive materials. Therefore, appropriately managing their risk is necessary. Various nuclear regulatory agencies employ probabilistic risk assessment (PRA) to effectively evaluate risks in nuclear power plants. Dynamic PRA has gained popularity because it allows for more realistic assessment by reducing the assumptions and engineering judgments related to time-dependent failure probability and/or human-action reliability in the conventional PRA methodology. However, removing all assumptions and engineering judgments is difficult; thus, the risk analyst, for example, the regulator, must understand their effects on the assessment results. This study focuses on “risk dilution,†which emerges from the assumptions about uncertainty. Dynamic PRA of a station blackout sequence in a boiling-water reactor was performed using the dynamic PRA tool, namely, Risk Assessment with Plant Interactive Dynamics (RAPID) and the severe-accident code Thermal–Hydraulic Analysis of Loss of Coolant, Emergency Core Cooling, and Severe Core Damage version 2 (THALES2), which altered the correlation parameters among the uncertainties of the events that occurred in sequence. The results demonstrated that the conditional core-damage probability and mean value of the core-damage time varied from 0.27 to 0.47 and from 7.1 to 8.7 h, respectively. When the dynamic PRA results are used for risk-informed decision making, the decision maker should adequately consider the effect of risk dilution.

Keywords: Probabilistic risk assessment; dynamic PRA; nuclear power plant; risk dilution; risk analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:238:y:2024:i:3:p:464-474

DOI: 10.1177/1748006X231167201

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