A case study to address the limitation of accident scenario identifications with respect to diverse manual responses

Abstract

Probabilistic safety assessment (PSA) is a tool for securing the operational safety of nuclear power plants. One unique benefit of PSA is to identify accident scenarios leading to undesirable consequences. Since these accident scenarios result from highly complicated combinations of automatic and manual responses, a huge number of simulations are generally required with a precise thermal-hydraulic (TH) code. Therefore, current PSA typically reduces the number of TH code runs by focusing on limited numbers of safety-critical components with reasonable engineering assumptions. Nevertheless, it has been pointed out that the current catalog of accident scenarios in PSA may be insufficient to some extent. In order to corroborate this claim, in this work, 14,348,907 simulation conditions were randomly generated after combining 79 manual responses identified from the emergency operating procedure of a steam generator tube rupture (SGTR) event in a reference nuclear power plant. Results show that about 14.7 % of the simulations correspond to an undesirable consequence pertaining to the SGTR. Accordingly, in terms of enhancing the quality of PSA results, the results of this study support that it is inevitable to develop a promising way to reduce the amount of computational resources required by large numbers of TH code runs.