Within emerging best-estimate-plus-uncertainty (BEPU) approaches code output uncertainties can be inferred from the propagation of fundamental or microscopic uncertainties. This paper examines the propagation of fundamental nuclear data uncertainties though the entire analysis framework to predict macroscopic reactor physics phenomena which can be measured in CANDU reactors. In this work 151 perturbed multi-group cross sections libraries, each based on a set of perturbed microscopic nuclear data, were generated. Subsequently these data were processed into few-group cross sections and used to generate full-core diffusion models in PARCS. The impact of these nuclear data perturbations leads to changes in core reactivity for a fixed set of fuel compositions of 4.5 mk. The impact of online fueling operations was simulated using a series of fueling-rules which attempted to mimic operator actions during CANDU operations such as studying the assembly powers and selecting fueling sites which would minimize the deviation in power from some desirable reference condition or increasing or decreasing fuelling frequency to manage reactivity. An important feature of this analysis was to perform long-transients (1-3 years) starting with each one of the 151 perturbed full core models. It was found that the operational feedbacks reduced the standard deviation in core reactivity by 99% from 0.0045 to 2.8x10-5. Overall the conclusions demonstrate that while microscopic nuclear data uncertainties may give rise to large macroscopic variability during simple propagation, when important macro-level feedbacks are considered the variability is significantly reduced.

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