  The uncertainty associated with the experimental inflow in a wind tunnel
affects the prediction of the flow of interest by numerical simulations. We
evaluate this impact using uncertainty quantification. A method is developed
and applied to the simulation of the drag generated by the flow past a cylinder
installed in the transonic S3Ch ONERA mid-scale facility. The inflow
uncertainty results from the imperfect knowledge and variability of the flow in
the settling chamber. It is taken into account via the inlet boundary condition
in the numerical companion setup and evaluated experimentally by measuring the
inflow using a hot-wire rake. The propagation of the input uncertainties is
carried \alert{out} through a two-dimensional RANS model of the experiment. A
polynomial surrogate model is developed to infer the uncertainty associated
with the drag of the cylinder. Following observations of Gaussian inputs, the
parameters of the stochastic model are constructed in two ways, first through a
projection approach, based on the Gauss-Hermite quadrature rule, and then using
a sparsity based regression approach, based on compressed sensing. The latter
drastically reduces the number of deterministic numerical simulations. The drag
is most influenced by the central part of the inflow but the overall
uncertainty remains low.
