Bayesian probabilistic analysis of a nuclear power plant small loss of coolant event tree model with possibilistic parameters
Abstract
Nuclear Power plant risk analysis models (e.g., Fault and Event Trees) contain parameters (e.g., probabilities) that are epistemically uncertain, i.e., known with poor precision. In current Probabilistic Risk Assessment (PRA) practice, epistemic uncertainty is described by predetermined probability distributions. However, a probabilistic representation of epistemic uncertainty is difficult to justify in those cases in which the analysis is carried out based on imprecise and incomplete information. In this paper, we describe epistemic uncertainty by possibility distributions, which encode families of probability distributions and, thus, represent the analyst's imprecise knowledge of the uncertain model parameters. This work addresses the issue of updating, in a Bayesian framework, the possibilistic representation of the epistemically-uncertain parameters of risk models as new information (e.g., data) becomes available: a purely possibilistic counterpart of the classical, well-grounded probabilistic Bayes theorem is adopted. The method is applied to revise, by means of real plant failure data, the possibility distributions describing the epistemic uncertainties in the probabilities of occurrence of accident sequences following a small loss of coolant event in a nuclear power plant.