Monte Carlo nuclear data adjustment via integral information

• Published in: European physical journal plus Vol. 133; no. 12; p. 537
• Main Authors: Rochman, D., Bauge, E., Vasiliev, A., Ferroukhi, H., Pelloni, S., Koning, A. J., Sublet, J. Ch
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2018; Springer Nature B.V
• Subjects: Applied and Technical Physics; Atomic; Benchmarks; Complex Systems; Condensed Matter Physics; Fast neutrons; Libraries; Mathematical and Computational Physics; Molecular; Monte Carlo simulation; Neutron spectra; Optical and Plasma Physics; Physics; Physics and Astronomy; Reduction; Regular Article; Standard deviation; Theoretical; Uncertainty
• ISSN: 2190-5444; 2190-5444
• DOI: 10.1140/epjp/i2018-12361-x

.
In this paper, we present three Monte Carlo methods to include integral benchmark information into the nuclear data evaluation procedure: BMC, BFMC and Mocaba. They allow to provide posterior nuclear data and their covariance information in a Bayesian sense. Different examples will be presented, based on 14 integral quantities with fast neutron spectra (k
eff
and spectral indices). Updated nuclear data for
235
U,
238
U and
239
Pu are considered and the posterior nuclear data are tested with MCNP simulations. One of the noticeable outcomes is the reduction of uncertainties for integral quantities, obtained from the reduction of the nuclear data uncertainties and from the rise of correlations between cross sections of different isotopes. Finally, the posterior nuclear data are tested on an independent set of benchmarks, showing the limit of the adjustment methods and the necessity for selecting well representative systems.