D. Liu, A. Litvinenko, C. Schillings, V. Schulz
SIAM/ASA Journal on Uncertainty Quantification, (2016)
Aerodynamics simulation, airfoil geometric uncertainty, Surrogate modeling, Gradient-enhanced kriging, Numerical integration
Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods 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 determ ined 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 independent Gaussian variables. The results show that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods with the same computational cost.