IMPACT OF NUCLEAR DATA UNCERTAINTIES ON LEAD COOLED FAST REACTORS

Current efforts towards development of preliminary designs for Lead-cooled Fast
Reactors (LFRs) have demonstrated a need for assessing uncertainty propagation
through the reactor system. Evaluating uncertainties will lead to a better understanding of their impact on LFR core design, and will identify the design safety
limits that are reviewed during the licensing process. This study explored the sensitivity and uncertainty analysis for the LFR using available cross-section covariance
data and perturbation theory approaches on multiple scales of reactor modeling.
This study used the 500 MWth Demonstrator Lead Fast Reactor (DLFR) designed
by Westinghouse.
The Monte Carlo code Serpent 2 with implemented Generalized Perturbation Theory (GPT) was used to calculate the sensitivity coefficients of the multiplication
factor with respect to nuclide and reaction-dependent nuclear data for fuel assembly models on fuel lattice level. The uncertainty quantification was performed using
the COMMARA-2.0 covariance library with the focus placed on uncertainty variations due to the fuel compositions.
Uncertainty propagation from nuclear data was then analyzed on the whole core
using the computational tools Serpent and Argonne National Lab (ANL) Advanced
Reactor Computational (ARC) codes. In ARC, DIF3D was employed for DLFR
core modeling and PERSENT was used for sensitivity coefficient calculations. A
correlation matrix providing information on the inter-relationship of the uncertainty
of reactivity coefficients was generated, and will be later applied to transient calculations for evaluating the safety performance of the DLFR.