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

Handle URI:
http://hdl.handle.net/10754/623697
Title:
Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches
Authors:
Liu, Dishi; Litvinenko, Alexander ( 0000-0001-5427-3598 ) ; Schillings, Claudia; Schulz, Volker
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.
KAUST Department:
Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
Citation:
Liu D, Litvinenko A, Schillings C, Schulz V (2017) Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches. SIAM/ASA Journal on Uncertainty Quantification 5: 334–352. Available: http://dx.doi.org/10.1137/15M1050239.
Publisher:
Society for Industrial & Applied Mathematics (SIAM)
Journal:
SIAM/ASA Journal on Uncertainty Quantification
Issue Date:
14-Apr-2015 ; 30-Mar-2017
DOI:
10.1137/15M1050239
ARXIV:
arXiv:1505.05731
Type:
Article
ISSN:
2166-2525
Sponsors:
This work was supported by the project MUNA under the framework of the German Luftfahrtforschungsprogramm funded by the Ministry of Economics (BMWi). A part of this work was done by A. Litvinenko during his stay at King Abdullah University of Science and Technology. We are grateful to the anonymous reviewers for their diligence and insights which have greatly helped to improve this paper. Special gratitude is extended to Dr. Stefan Gortz at Institute of Aerodynamics and Flow Technology of the German Aerospace Center (DLR) for his invaluable advice during the modifi cation. The authors also thank Bernhard Eisfeld and Normann Krimmelbein at DLR for their kind help on the CFD test case.
Additional Links:
https://arxiv.org/abs/1505.05731; http://epubs.siam.org/doi/10.1137/15M1050239
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Dishien
dc.contributor.authorLitvinenko, Alexanderen
dc.contributor.authorSchillings, Claudiaen
dc.contributor.authorSchulz, Volkeren
dc.date.accessioned2017-05-23T07:35:09Z-
dc.date.available2017-05-23T07:35:09Z-
dc.date.issued2015-04-14-
dc.date.issued2017-03-30en
dc.identifier.citationLiu D, Litvinenko A, Schillings C, Schulz V (2017) Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches. SIAM/ASA Journal on Uncertainty Quantification 5: 334–352. Available: http://dx.doi.org/10.1137/15M1050239.en
dc.identifier.issn2166-2525en
dc.identifier.doi10.1137/15M1050239en
dc.identifier.urihttp://hdl.handle.net/10754/623697-
dc.description.abstractUncertainty 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.en
dc.description.sponsorshipThis work was supported by the project MUNA under the framework of the German Luftfahrtforschungsprogramm funded by the Ministry of Economics (BMWi). A part of this work was done by A. Litvinenko during his stay at King Abdullah University of Science and Technology. We are grateful to the anonymous reviewers for their diligence and insights which have greatly helped to improve this paper. Special gratitude is extended to Dr. Stefan Gortz at Institute of Aerodynamics and Flow Technology of the German Aerospace Center (DLR) for his invaluable advice during the modifi cation. The authors also thank Bernhard Eisfeld and Normann Krimmelbein at DLR for their kind help on the CFD test case.en
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)en
dc.relation.urlhttps://arxiv.org/abs/1505.05731en
dc.relation.urlhttp://epubs.siam.org/doi/10.1137/15M1050239en
dc.rightsArchived with thanks to SIAM/ASA Journal on Uncertainty Quantificationen
dc.subjectaerodynamic simulationen
dc.subjectairfoil geometric uncertaintyen
dc.subjectsurrogate modelingen
dc.subjectgradient-enhanced krigingen
dc.subjectnumerical integrationen
dc.titleQuantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approachesen
dc.typeArticleen
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)en
dc.identifier.journalSIAM/ASA Journal on Uncertainty Quantificationen
dc.eprint.versionPost-printen
dc.contributor.institutionGerman Aerospace Center (DLR), Institute of Aerodynamics and Flow Technology, Lilienthalplatz 7, 38108 Braunschweig, Germanyen
dc.contributor.institutionSeminar for Applied Mathematics, ETH, 8092 Zurich, Switzerlanden
dc.contributor.institutionDepartment of Mathematics, Universit at Trier Universit atsring 15, 54296 Trier, Germanyen
dc.identifier.arxividarXiv:1505.05731en
kaust.authorLitvinenko, Alexanderen
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