Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches
For aerodynamic engineers, this provides a practical comparison of methods to efficiently quantify geometry-induced uncertainties, though the findings are incremental.
This paper compares five uncertainty quantification methods for aerodynamic simulations with random airfoil geometry perturbations parameterized by 9 Gaussian variables, finding that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods at the same computational cost.
Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods since it is computationally expensive, especially for the uncertainties caused by random geometry variations which involve a large number of variables. This paper compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and gradient-enhanced version of Kriging, radial basis functions and point collocation polynomial chaos, in their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry which is parameterized by 9 independent Gaussian variables. The results show that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods with the same computational cost.