  Long-term operation of nuclear steam generators can result in the occurrence
of clogging, a deposition phenomenon that may increase the risk of mechanical
and vibration loadings on tube bundles and internal structures as well as
potentially affecting their response to hypothetical accidental transients. To
manage and prevent this issue, a robust maintenance program that requires a
fine understanding of the underlying physics is essential. This study focuses
on the utilization of a clogging simulation code developed by EDF R\&D. This
numerical tool employs specific physical models to simulate the kinetics of
clogging and generates time dependent clogging rate profiles for particular
steam generators. However, certain parameters in this code are subject to
uncertainties. To address these uncertainties, Monte Carlo simulations are
conducted to assess the distribution of the clogging rate. Subsequently,
polynomial chaos expansions are used in order to build a metamodel while
time-dependent Sobol' indices are computed to understand the impact of the
random input parameters throughout the whole operating time. Comparisons are
made with a previous published study and additional Hilbert-Schmidt
independence criterion sensitivity indices are computed. Key input-output
dependencies are exhibited in the different chemical conditionings and new
behavior patterns in high-pH regimes are uncovered by the sensitivity analysis.
These findings contribute to a better understanding of the clogging phenomenon
while opening future lines of modeling research and helping in robustifying
maintenance planning.
