Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches

Dishi Liu, Alexander Litvinenko, Claudia Schillings, Volker Schulz

Research output: Contribution to journalArticlepeer-review

Abstract

Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods to reduce computational cost, especially for 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 by point collocation, radial basis function and a gradient-enhanced version of kriging, and examines 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.
Original languageEnglish (US)
Pages (from-to)334-352
Number of pages19
JournalSIAM/ASA Journal on Uncertainty Quantification
Volume5
Issue number1
DOIs
StatePublished - Mar 30 2017

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