  Several methodologies using different levels of approximations have been
developed for propagating nuclear data uncertainties in nuclear burn-up
simulations. Most methods fall into the two broad classes of Monte Carlo
approaches, which are exact apart from statistical uncertainties but require
additional computation time, and first order perturbation theory approaches,
which are efficient for not too large numbers of considered response functions
but only applicable for sufficiently small nuclear data uncertainties. Some
methods neglect isotopic composition uncertainties induced by the depletion
steps of the simulations, others neglect neutron flux uncertainties, and the
accuracy of a given approximation is often very hard to quantify. In order to
get a better sense of the impact of different approximations, this work aims to
compare results obtained based on different approximate methodologies with an
exact method, namely the NUDUNA Monte Carlo based approach developed by AREVA
GmbH. In addition, the impact of different covariance data is studied by
comparing two of the presently most complete nuclear data covariance libraries
(ENDF/B-VII.1 and SCALE 6.0), which reveals a high dependency of the
uncertainty estimates on the source of covariance data. The burn-up benchmark
Exercise I-1b proposed by the OECD expert group "Benchmarks for Uncertainty
Analysis in Modeling (UAM) for the Design, Operation and Safety Analysis of
LWRs" is studied as an example application. The burn-up simulations are
performed with the SCALE 6.0 tool suite.
