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.

Correlationν̅p − σ − χin the fast neutron range via integral information

“…Many simulation tools are capable of using such matrices to propagate nuclear data uncertainties on final quantities, with either perturbation theories [1][2][3][4][5][6], or Monte Carlo sampling [3,[7][8][9][10][11][12][13]. These results can for instance be used for the review procedure of new facilities, or during the safety assessment of new reactor core designs [14].…”

Analysis of experimental series of plutonium nitrate in aqueous solution and their correlation coefficients

“…Also the two cases with aluminum sphere, the two cases with additional cadmium layers on the outside of the sphere and the three experiments For series PST-03, 04, 05, and 06 the given temperature is 300 • K, for series PST-20 and 21 it is 298 • K, following the experimental description. Accordingly, the density of the reflecting water is slightly different: 0.9965 g/cm 3 and 0.9970 g/cm 3 . The plutonium densities ρ Pu in [g/cm 3 ] vary for all experiments from around 0.025 to 0.07 due to the varying concentration of plutonium nitrate in water, shown in figure 2.…”

“…Accordingly, the density of the reflecting water is slightly different: 0.9965 g/cm 3 and 0.9970 g/cm 3 . The plutonium densities ρ Pu in [g/cm 3 ] vary for all experiments from around 0.025 to 0.07 due to the varying concentration of plutonium nitrate in water, shown in figure 2. With increasing ρ Pu the moderator-to-fuel ratio decreases, leading to a harder spectrum.…”

“…tanks, fuel rods or solutions. This can lead to significant correlations between experiments which in some cases have to be considered when determining safety margins [3,4,5,6,7]. One possibility to quantify the degree of linearity between two quantities X and Y (e.g.…”

Impact of uncertainties in the uranium 235 cross section resonance structure on characteristics measured in the BFS-79 critical assemblies

“…Sophisticated statistical approaches have been proposed and are nowadays widely used for the assessment of reactor characteristics uncertainties caused by nuclear data uncertainties [for instance, [3][4][5]. Especially valuable is that such approaches provide capabilities to propagate pointwise nuclear data uncertainty on reactor characteristics uncertainties that allows carrying out neutron crosssections adjustments with due account for both differential and integral experiments.…”