The present paper deals with the description of the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes. The 1st approach 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. 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. More details are provided in respect with the third approach that has been named CASUALIDAD (Code with the capability of Adjoint Sensitivity and Uncertainty AnaLysis by Internal Data ADjustment and assimilation).

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