Abstract
The aim of this work is to review different Monte Carlo techniques used to propagate nuclear data uncertainties. Firstly, we introduced Monte Carlo technique applied for Uncertainty Quantification studies in safety calculations of large scale systems. As an example, the impact of nuclear data uncertainty of JEFF-3.3 235U, 238U and 239Pu is demonstrated for the main design parameters of a typical 3-loop PWR Westinghouse unit. Secondly, the Bayesian Monte Carlo technique for data adjustment is presented. An example for 235U adjustment using criticality and shielding integral benchmarks shows the importance of performing joint adjustment based on different set of integral benchmarks.
Highlights
1 Introduction This paper focuses on the application of Monte Carlo (MC) methodologies for the propagation of nuclear data uncertainties
The first section of this paper presents MC technique to perform Uncertainty Quantification (UQ) analysis in large-scale systems which is increasingly necessary in reactor safety calculations of LWRs [1]
In case the normality assumption is not acceptable, σ may be mapped onto an approximately normally distributed vector by an invertible transformation [4]. Under these conditions the Bayes’ Theorem yields a multivariate normal posterior probability density function. This Bayesian approach can be defined as Multivariate Normal Bayesian Model (MNBM) and the resulting equations are widely known as the MOCABA equations [4,10]
Summary
This paper focuses on the application of Monte Carlo (MC) methodologies for the propagation of nuclear data uncertainties. The first section of this paper presents MC technique to perform Uncertainty Quantification (UQ) analysis in large-scale systems which is increasingly necessary in reactor safety calculations of LWRs [1]. The second section is devoted to review current nuclear data adjustment methodologies based on a Bayesian approach in combination with a Monte Carlo technique
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