MOCABA: a general Monte Carlo-Bayes procedure for improved predictions of integral functions of nuclear data

Abstract

MOCABA is a combination of Monte Carlo sampling and Bayesian updating algorithms for the prediction of integral functions of nuclear data, such as reactor power distributions or neutron multiplication factors. Similarly to the established Generalized Linear Least Squares (GLLS) methodology, MOCABA offers the capability to utilize integral experimental data to reduce the prior uncertainty of integral observables. The MOCABA approach, however, does not involve any series expansions and, therefore, does not suffer from the breakdown of first-order perturbation theory for large nuclear data uncertainties. This is related to the fact that, in contrast to the GLLS method, the updating mechanism within MOCABA is applied directly to the integral observables without having to "adjust" any nuclear data. A central part of MOCABA is the nuclear data Monte Carlo program NUDUNA, which performs random sampling of nuclear data evaluations according to their covariance information and converts them into libraries for transport code systems like MCNP or SCALE. What is special about MOCABA is that it can be applied to any integral function of nuclear data, and any integral measurement can be taken into account to improve the prediction of an integral observable of interest. In this paper we present two example applications of the MOCABA framework: the prediction of the neutron multiplication factor of a water-moderated PWR fuel assembly based on 21 criticality safety benchmark experiments and the prediction of the power distribution within a toy model reactor containing 100 fuel assemblies.

arXiv comment10 pages, Preprint submitted to Annals of Nuclear Energy

References in corpus (1)

• Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

Cited by in corpus (4)

• Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference
• Modeling of critical experiments and its impact on integral covariance matrices and correlation coefficients
• Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation
• Analysis of experimental series of plutonium nitrate in aqueous solution and their correlation coefficients