The Casualidad Method for Uncertainty Evaluations of Best-Estimate Code Calculations

Abstract

The description of the salient features of three independent approaches for estimating uncertainties associated with the predictions of complex system codes is provided together with the main drawbacks and advantages. The 1st approach is referred as Propagation of Input Uncertainties (PIU) and it is the “standard” one and the most used at the industrial level: it is based upon the selection of input uncertain parameters, on assigning related ranges of variations and, possibly, PDF (Probability Density Functions) and on performing a suitable number of code runs to get the combined effect of variation on the results. In the 2nd approach the uncertainty derives from the comparison between relevant measured data and results of corresponding code calculations and thus is referred as Output Error Propagation (OEP). The 3rd approach is based on the Bayesian inference technique and on the availability of experimental data by which computer model predictions can be improved and the ranges of variation of (in theory) „all‟ input parameters can be characterized.