Premium
The Repeatability of Uncertainty and Sensitivity Analyses for Complex Probabilistic Risk Assessments
Author(s) -
Iman Ronald L.,
Helton Jon C.
Publication year - 1991
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1991.tb00649.x
Subject(s) - latin hypercube sampling , monte carlo method , nuclear power plant , uncertainty analysis , sensitivity (control systems) , repeatability , probabilistic logic , probabilistic risk assessment , probabilistic analysis of algorithms , sampling (signal processing) , computer science , statistical power , reliability engineering , econometrics , statistics , engineering , mathematics , simulation , physics , nuclear physics , filter (signal processing) , electronic engineering , computer vision
The performance of a probabilistic risk assessment (PRA) for a nuclear power plant is a complex undertaking, involving the assembly of an accident frequency analysis, an accident progression analysis, a source term analysis, and a consequence analysis. Each of these analyses is, in itself, quite complex. Uncertainties enter into a PRA from each of these analyses. An important focus in recent PRAs has been to incorporate these uncertainties at each stage of the analysis, propagate the subsequent uncertainties through the entire analysis, and include uncertainty in the final results. Monte Carlo procedures based on Latin hypercube sampling provide one way to perform propagations of this type. In this paper, the results of two complete and independent Monte Carlo calculations for a recently completed PRA for a nuclear power plant are compared as a means of providing empirical evidence on the repeatability of uncertainty and sensitivity analyses for large‐scale PRA calculations. These calculations use the same variables and analysis structure with two independently generated Latin hypercube samples. The results of the two calculations show a high degree of repeatability for the analysis of a very complex system.