MOCABA: A general Monte Carlo–Bayes procedure for improved predictions of integral functions of nuclear data. (March 2015)

• Record Type: Journal Article
• Authors: Hoefer, A.; Buss, O.; Hennebach, M.; Schmid, M.; Porsch, D.
• Is Part Of: Annals of nuclear energy. Volume 77(2015:Mar.)
• Page Start: 514
• Page End: 521
• Publication Date: 2015-03
• Subjects: Uncertainty analysis -- Nuclear data -- Monte Carlo methods -- Nuclear criticality safety -- Reactor analysis; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2014.11.038
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: MOCABA is a non-perturbative generalization of first-order nuclear data adjustment. MOCABA is applicable to the prediction of any integral observable. Any integral measurement may be used to update predictions of integral observables. Constraints on linear combinations of integral observables can be accounted. MOCABA can be based on covariance information in evaluated nuclear data files. 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.