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The uncertainty associated with the experimental inflow in a wind tunnelaffects the prediction of the flow of interest by numerical simulations. Weevaluate this impact using uncertainty quantification. A method is developedand applied to the simulation of the drag generated by the flow past a cylinderinstalled in the transonic S3Ch ONERA mid-scale facility. The inflowuncertainty results from the imperfect knowledge and variability of the flow inthe settling chamber. It is taken into account via the inlet boundary conditionin the numerical companion setup and evaluated experimentally by measuring theinflow using a hot-wire rake. The propagation of the input uncertainties iscarried \alert{out} through a two-dimensional RANS model of the experiment. Apolynomial surrogate model is developed to infer the uncertainty associatedwith the drag of the cylinder. Following observations of Gaussian inputs, theparameters of the stochastic model are constructed in two ways, first through aprojection approach, based on the Gauss-Hermite quadrature rule, and then usinga sparsity based regression approach, based on compressed sensing. The latterdrastically reduces the number of deterministic numerical simulations. The dragis most influenced by the central part of the inflow but the overalluncertainty remains low.

Uncertainty Quantification Applied to the Propagation of a Transonic Wind Tunnel Inflow Inhomogeneities