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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorMatthies, Hermann G.
dc.contributor.authorLiu, Dishi
dc.contributor.authorSchillings, Claudia
dc.contributor.authorSchulz, Volker
dc.date.accessioned2017-05-15T08:22:25Z
dc.date.available2017-05-15T08:22:25Z
dc.date.issued2017-05-16
dc.identifier.urihttp://hdl.handle.net/10754/623498
dc.description.abstractWe consider uncertainty quantification problem in aerodynamic simulations. We identify input uncertainties, classify them, suggest an appropriate statistical model and, finally, estimate propagation of these uncertainties into the solution (pressure, velocity and density fields as well as the lift and drag coefficients). The deterministic problem under consideration is a compressible transonic Reynolds-averaged Navier-Strokes flow around an airfoil with random/uncertain data. Input uncertainties include: uncertain angle of attack, the Mach number, random perturbations in the airfoil geometry, mesh, shock location, turbulence model and parameters of this turbulence model. This problem requires efficient numerical/statistical methods since it is computationally expensive, especially for the uncertainties caused by random geometry variations which involve a large number of variables. In numerical section we 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 [D.Liu et al '17]. For modeling we used the TAU code, developed in DLR, Germany.
dc.description.sponsorshipECRC KAUST
dc.relation.urlhttps://pccfd.kaust.edu.sa/about
dc.subjectUncertainty Quantification
dc.subjectnumerical aeridynamics
dc.subjectPCE, polynomial chaos
dc.subjectuncertain geometry of airfoil
dc.subjectTAU code
dc.subjectgradient-enhanced Kriging
dc.titleUncertainty Quantification in Numerical Aerodynamics
dc.typePoster
dc.contributor.departmentExtreme Computing Research Center
dc.conference.dateMay 22-24, 2017
dc.conference.namePredictive Complex Computational Fluid Dynamics Conference at KAUST
dc.conference.locationKAUST, B9, L2
dc.contributor.institutionTU Braunschweig, Germany
dc.contributor.institutionDLR, Germany
dc.contributor.institutionUniversitaet Mannheim, Germany
dc.contributor.institutionUniversitaet Trier, Germany
refterms.dateFOA2018-06-14T04:23:42Z


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