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

TL;DR

This paper applies uncertainty quantification to assess how inflow variability in a transonic wind tunnel affects drag predictions, developing surrogate models to efficiently propagate uncertainties through RANS simulations.

Contribution

It introduces a novel method combining polynomial surrogate models with sparse regression to efficiently quantify inflow uncertainty impacts on aerodynamic drag predictions.

Findings

Inflow uncertainty has a limited overall impact on drag predictions.

The sparse regression approach significantly reduces computational effort.

The central inflow region most influences drag uncertainty.

Abstract

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.