Combined Analysis Of Fire Model Uncertainty And Input Parameter Uncertainty For Nuclear Power Plant Fire Scenarios
Kristopher J. Overholt, Martin Clouthier
Quantitative fire risk analysis can be used to conduct safety assessments at nuclear facilities to evaluate the consequences of potential fire scenarios that can damage structures, systems, and components. Point estimates or bounding analyses are commonly used to evaluate a given scenario against pass/fail criteria. However, these methods do not take into account probability distributions of input parameters. Therefore, in some cases, a more robust methodology for probabilistic risk assessment is needed, and the effect of different types of uncertainty should be considered in a quantitative risk analysis. The purpose of this paper is to present an uncertainty analysis for a single fire scenario that accounts for different types of uncertainty. Three cases are presented that account for the effect of different types of uncertainty (model bias and uncertainty, input parameter uncertainty, and combined uncertainty) on the probability of exceeding a specified criterion. For this uncertainty analysis, the heat release rate (HRR) was selected as the uncertain input parameter of interest, and the hot gas layer temperature was selected as the model output quantity of interest. The Consolidated Model of Fire Growth and Smoke Transport (CFAST) zone model was used to predict hot gas layer (HGL) temperatures for a given input HRR. The results include a comparison of the probabilities of exceeding a threshold HGL temperature for the three different cases.
Proceedings of the 19th Pacific Basin Nuclear Conference (PBNC 2014)
August 24-28, 2014
Vancouver, British Columbia
The 19th Pacific Basin Nuclear Conference (PBNC 2014)
and Clouthier, M.
Combined Analysis Of Fire Model Uncertainty And Input Parameter Uncertainty For Nuclear Power Plant Fire Scenarios, Proceedings of the 19th Pacific Basin Nuclear Conference (PBNC 2014), Vancouver, British Columbia, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=915757
(Accessed June 6, 2023)