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

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

Dishi Liu, Alexander Litvinenko, Claudia Schillings, and Volker Schulz, Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches submitted 2015
Dishi Liu, Alexander Litvinenko, Claudia Schillings, and Volker Schulz
Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches
2015
‚ÄčUncertainty quantification in aerodynamic simulations calls for efficient numerical methods {\color{noblue} to reduce computational cost}, 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.
2015