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The Repeatability of Uncertainty and Sensitivity Analyses for Complex Probabilistic Risk Assessments
Ronald L. Iman,
Jon C. Helton,
Ronald L. Iman
Manufacturing Systems Reliability Modeling, Sandia National Laboratories, Division 6613, Albuquerque, New Mexico 87185.
Search for more papers by this authorJon C. Helton
Department of Mathematics, Arizona State University, Tempe, Arizona 85287.
Search for more papers by this authorRonald L. Iman,
Jon C. Helton,
Ronald L. Iman
Manufacturing Systems Reliability Modeling, Sandia National Laboratories, Division 6613, Albuquerque, New Mexico 87185.
Search for more papers by this authorJon C. Helton
Department of Mathematics, Arizona State University, Tempe, Arizona 85287.
Search for more papers by this authorAbstract
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.
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