Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches

TL;DR

This paper compares five numerical methods for efficiently quantifying how random airfoil geometry variations affect aerodynamic performance, highlighting the superior accuracy of gradient-enhanced surrogate models at similar computational costs.

Contribution

It introduces a comprehensive comparison of five advanced uncertainty quantification methods applied to airfoil geometry variations, emphasizing the effectiveness of gradient-enhanced surrogates.

Findings

Gradient-enhanced surrogate methods outperform direct integration in accuracy.

Efficient estimation of aerodynamic uncertainties with fewer simulations.

Comparison provides guidance for selecting UQ methods in aerodynamics.

Abstract

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.