Global sensitivity analysis of the main steam line break accident by using sampling methods and surrogate models

Abstract

Global sensitivity analysis (GSA) aims at quantifying the individual effects of input variables on the model outputs. The global sensitivity analysis is expressed by the Sobol’ indices. The Sobol’ indices can be obtained by different algorithms. These algorithms can be divided into two main categories: the sampling methods and the surrogate models. This work employs these methods to perform the global sensitivity analysis of Main Steam Line Break Accident (MSLB) of the AP1000 nuclear power plant. Specifically, the DAKOTA is coupled with an in-house containment analysis code to perform the sensitivity analysis. This study focuses on the effects of five key input parameters on safety parameters. The results show that atmospheric pressure has the most important impact on the peak pressure and peak temperature of the containment during the MSLB accident. Besides, compared with the sampling methods, the surrogate model methods need fewer samples and less time to obtain the Sobol’ indices.