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    Uncertainty Quantification in Numerical Aerodynamics

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    Type
    Poster
    Authors
    Litvinenko, Alexander cc
    Matthies, Hermann G.
    Liu, Dishi
    Schillings, Claudia
    Schulz, Volker
    KAUST Department
    Extreme Computing Research Center
    Date
    2017-05-16
    Permanent link to this record
    http://hdl.handle.net/10754/623498
    
    Metadata
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    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.
    Sponsors
    ECRC KAUST
    Conference/Event name
    Predictive Complex Computational Fluid Dynamics Conference at KAUST
    Additional Links
    https://pccfd.kaust.edu.sa/about
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    Posters; KAUST Research Conference: Predictive Complex Computational Fluid Dynamics 2017

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