A source term binning methodology for multi-unit consequence analyses

Abstract

Public has concerned on a site risk after the Fukushima nuclear accidents. Therefore, multi-unit probabilistic safety assessment (MUPSA) has been researched actively because it is necessary to perform MUPSA for assessing a site risk. Most of the researches performed until now focus on multi-unit Level 1 PSA because it is the most important issue to model dependencies between units. However, multi-unit Level 3 PSA should be researched because many source term category (STC) combinations exist in multi-unit accidents. A binning methodology to group many STCs into fewer groups was developed in this research for considering this problem. First, a qualitative and quantitative logic tree to group similar STCs into a same group were developed, respectively. Second, five methods to designate representative MACCS inputs of each group were developed. Third, a verification procedure for the binning methodology was developed, and the most appropriate method for each logic tree was selected. The scheme of the binning methodology can be applied to any reactor type with several modifications such as the headings of the logic tree. Conclusively, the binning methodology will be an appropriate example of multi-unit Level 3 PSA and important element of a site risk estimation methodology.