Handle URI:
http://hdl.handle.net/10754/623498
Title:
Uncertainty Quantification in Numerical Aerodynamics
Authors:
Litvinenko, Alexander ( 0000-0001-5427-3598 ) ; Matthies, Hermann G.; Liu, Dishi; Schillings, Claudia; Schulz, Volker
Abstract:
We 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.
KAUST Department:
Extreme Comuting Research Center
Conference/Event name:
Predictive Complex Computational Fluid Dynamics Conference at KAUST
Issue Date:
16-May-2017
Type:
Poster
Sponsors:
ECRC KAUST
Additional Links:
https://pccfd.kaust.edu.sa/about
Appears in Collections:
Posters; KAUST Research Conference: Predictive Complex Computational Fluid Dynamics 2017

Full metadata record

DC FieldValue Language
dc.contributor.authorLitvinenko, Alexanderen
dc.contributor.authorMatthies, Hermann G.en
dc.contributor.authorLiu, Dishien
dc.contributor.authorSchillings, Claudiaen
dc.contributor.authorSchulz, Volkeren
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.en
dc.description.sponsorshipECRC KAUSTen
dc.relation.urlhttps://pccfd.kaust.edu.sa/abouten
dc.subjectUncertainty Quantificationen
dc.subjectnumerical aeridynamicsen
dc.subjectPCE, polynomial chaosen
dc.subjectuncertain geometry of airfoilen
dc.subjectTAU codeen
dc.subjectgradient-enhanced Krigingen
dc.titleUncertainty Quantification in Numerical Aerodynamicsen
dc.typePosteren
dc.contributor.departmentExtreme Comuting Research Centeren
dc.conference.dateMay 22-24, 2017en
dc.conference.namePredictive Complex Computational Fluid Dynamics Conference at KAUSTen
dc.conference.locationKAUST, B9, L2en
dc.contributor.institutionTU Braunschweig, Germanyen
dc.contributor.institutionDLR, Germanyen
dc.contributor.institutionUniversitaet Mannheim, Germanyen
dc.contributor.institutionUniversitaet Trier, Germanyen
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