Best Estimate Methods for Safety Analysis and Trip Assessment

Abstract

Nuclear safety has moved away from deterministic conservative methods toward more probability based analysis. Advances in computer modeling and distributed computing have made possible the use of more advanced computational tools with quantifiable levels of accuracy. These improvements allow for more rigorous treatments of accident scenarios and lend themselves to statistical uncertainty analysis. This paper describes various methods for best estimate analysis ranging from conservative to more realistic assessments using Monte Carlo simulations, Wilk's method and extreme value statistics. Best Estimate and Uncertainty (BEAU) methodology is examined along with the use of probability and confidence intervals such as the 95/95 criterion in safety analysis and trip assessments. The examination details how best estimate methods can contribute to more realistic and robust safety analysis.