Efficient Quantification of Aerodynamic Uncertainty due to Random Geometry Perturbations

The effort of quantifying the aerodynamic uncertainties caused by uncertainties in the airfoil geometry is hindered by the large number of the variables and the high computational cost of the CFD model. To identify efficient methods addressing this challenge, four promising methods, gradient-enhanced Kriging (GEK), gradient-assisted polynomial chaos (GAPC), maximum entropy method and quasi-Monte Carlo quadrature are applied to a test case where the geometry of an RAE2822 airfoil is perturbed by a Gaussian random field parameterized by nine independent variables. The four methods are compared in their efficiency of estimating some statistics and probability distribution of the uncertain lift and drag coefficients. The results show that the two surrogate method, GEK and GAPC, both utilizing gradient information obtained by an adjoint CFD solver, are more efficient in this situation. Their advantage is expected to increase as the number of variables increases.