Assessment of the Potential Applicability of Fuzzy Set Theory to Accident Progression Event Trees with Phenomenological Uncertainties

Abstract

Expert opinion is frequently used in the risk analysis of nuclear power plant systems to assess, in particular, the probabilities of rare events. However, this procedure is always accompanied by imprecision and uncertainty that characterise the experts judgment. Since the fuzzy set theory provides a framework for dealing with such judgmental imprecision and uncertainty, the potential applicability of the fuzzy set theory to the uncertainty analysis of accident progression event trees with imprecise and uncertain branch probabilities and/or with a number of phenomenological uncertainty issues are examined as a possible alternative procedure to that used in the current probabilistic risk assessments. The main purpose of this paper is to demonstrate a potential use of fuzzy set theory and provide its formal procedure in the quantification of the uncertainties of accident progression event trees. First, an example application of the fuzzy set theory is made to the simple portion of a given accident progression event tree with typical imprecise and uncertain type of input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated. Secondly, to show the merits of the fuzzy set theory model in real application, the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e. a typical plant damage state 'SEC' of the Zion nuclear power plant risk assessment, and the results are compared with the one obtained by current probabilistic methods. In addition, discussions and answers for five major questions on its real application are given.