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Abstract:The determination of the probability distribution function (PDF) of uncertain input and model parameters in engineering application codes is an issue of importance for uncertainty quantification methods. One of the approaches that can be used for the PDF determination of input and model parameters is the application of methods based on the maximum entropy principle (MEP) and the maximum relative entropy (MREP). These methods determine the PDF that maximizes the information entropy when only partial information about the parameter distribution is known, such as some moments of the distribution and its support. In addition, this paper shows the application of the MREP to update the PDF when the parameter must fulfill some technical specifications (TS) imposed by the regulations. Three computer programs have been developed: GEDIPA, which provides the parameter PDF using empirical distribution function (EDF) methods; UNTHERCO, which performs the Monte Carlo sampling on the parameter distribution; and DCP, which updates the PDF considering the TS and the MREP. Finally, the paper displays several applications and examples for the determination of the PDF applying the MEP and the MREP, and the influence of several factors on the PDF.

SAPIUM: a generic framework for a practical and transparent quantification of thermal hydraulic code model input uncertaintyUncertainty analysis (UA) is a key element in nuclear power plant (NPP) deterministic safety analysis using best-estimate thermal hydraulic codes and best estimate plus uncertainty (BEPU) methodologies. If forward uncertainty propagation methods have now become mature for industrial applications, the input uncertainties quantification (IUQ) on the physical models still requires further investigations. The OECD/NEA PREMIUM project attempted to benchmark the available IUQ methods, but observed a strong user-effect due to lack of best practices guidance.The SAPIUM project has been proposed towards the construction of a clear and shared systematic approach for input uncertainty quantification. The main outcome of the project is a first "good practices" document that can be exploited for safety study in order to reach consensus among experts on recommended practices as well as to identify remaining open issues for further developments. This paper describes the systematic approach that consists in five elements in a step by step approach to perform a meaningful model input uncertainty quantification and validation as well as some "good practice guidelines" recommendations for each step.