Monte Carlo calculation and similarity evaluation for verification of influential uncertainty parameters to LBLOCA derived by statistical methods

Abstract

Several studies for identification of influential uncertainty parameters have been conducted, and the most of them used statistical methods utilizing small sample data. On the other hand, the consideration of influential uncertainty variables affects the distribution and the value of the figures of merit (FOMs). Therefore, we can verify influential uncertainty parameter identification results of statistical methods by comparing the distributions and the values of the FOMs. Kang (Kang, 2020) identified important uncertainty variables influential to APR-1400 large break loss of coolant accident (LBLOCA) by utilizing comprehensive correlation and multiple linear regression analysis with hypothesis testing. In this subsequent research, direct Monte Carlo calculations were performed by applying influential uncertainty parameters derived from previous work (Kang, 2020). Then, the similarity of some descriptive statistics, 95-percentile peak cladding temperature (PCT95) with 95% confidence interval, PCT distribution was evaluated. In addition, the PCT95 with 95% tolerance limit by Wilks’ method was also compared to verify previous results obtained by statistical methods.