Propagation of statistical and nuclear data uncertainties in Monte Carlo burn-up calculations

Abstract

Two methodologies to propagate the uncertainties on the nuclide inventory in combined Monte Carlo-spectrum and burn-up calculations are presented, based on sensitivity/uncertainty and random sampling techniques (uncertainty Monte Carlo method). Both enable the assessment of the impact of uncertainties in the nuclear data as well as uncertainties due to the statistical nature of the Monte Carlo neutron transport calculation. The methodologies are implemented in our MCNP-ACAB system, which combines the neutron transport code MCNP-4C and the inventory code ACAB.A high burn-up benchmark problem is used to test the MCNP-ACAB performance in inventory predictions, with no uncertainties. A good agreement is found with the results of other participants.This benchmark problem is also used to assess the impact of nuclear data uncertainties and statistical flux errors in high burn-up applications. A detailed calculation is performed to evaluate the effect of cross-section uncertainties in the inventory prediction, taking into account the temporal evolution of the neutron flux level and spectrum. Very large uncertainties are found at the unusually high burn-up of this exercise (800 MWd/kgHM). To compare the impact of the statistical errors in the calculated flux with respect to the cross uncertainties, a simplified problem is considered, taking a constant neutron flux level and spectrum. It is shown that, provided that the flux statistical deviations in the Monte Carlo transport calculation do not exceed a given value, the effect of the flux errors in the calculated isotopic inventory are negligible (even at very high burn-up) compared to the effect of the large cross-section uncertainties available at present in the data files.

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].…”

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

“…With the modern computation capabilities the stochastic sampling approach became also attractive for uncertainty quantifications and in the last decade a number of computation tools has been developed over the world towards the propagation of nuclear data uncertainties in neutronic calculations, ranging from tools for "simple" criticality stationary calculations, up to time-dependent calculations (including isotopic kinetics/fuel depletion and reactor dynamics/transient modeling in the vocabulary of nuclear engineering community) [31][32][33][34][35][36][37][38][39][40][41][42][43][44]. When it is possible, the stochastic and deterministic methods are cross-verified against each other [33,37,39] or even work in combination to provide the best capabilities for calculation simulations and results analysis [44].…”

Assessment of fissionable material behaviour in fission chambers

“…This well known distribution is equivalent to a normal distribution when A is small. [18] Using this uncertainty information, and sampling with the adequate probability distribution, we obtain the transition matrix, A. With this A matrix, ACAB computes the vector of nuclides (N) and response quantities (R tot , Sf ast , ...) per nuclide.…”