Estimation of the Impact of Geometrical Uncertainties on Aerodynamic Coefficients Using CFD

Chalot, F.; Dinh, Quang; Herbin, Érick; Martin, Ludovic; Ravachol, M.; Rogé, Gilbert

doi:10.2514/6.2008-2068

Cited by 13 publications

(8 citation statements)

References 1 publication

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“…Uncertainty quantification is being watched with keen interest these days [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] since high-fidelity computational fluid dynamics (CFD) simulations typically assume perfect knowledge of all parameters. In reality, however, there is much uncertainty due to manufacturing tolerances, in-service wear-and-tear, approximate modeling parameters and so on.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Drag Analysis via Polynomial Chaos Uncertainty Quantification

Yamazaki

2015

Trans. Japan Soc. Aero. S Sci.

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In this paper, uncertainty quantification approaches are applied to an aerodynamic uncertainty quantification problem with a far-field drag breakdown approach. Two uncertainty quantification approaches, a non-intrusive polynomial chaos approach and an inexpensive Monte-Carlo simulation approach on a response surface model are compared and investigated for more advanced aerodynamic uncertainty quantification. Using the drag breakdown approach, total drag of a body can be decomposed into three physical and one unphysical drag components: wave, viscous, induced and spurious drag components. The drag source distribution can also be visualized on the flowfield using this approach. An uncertainty quantification problem of two-dimensional airfoil with four uncertain input variables is analyzed with the drag breakdown approach to extract more aerodynamic design information about its uncertainty propagation.

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Section: Introductionmentioning

confidence: 99%

Stochastic Drag Analysis via Polynomial Chaos Uncertainty Quantification

Yamazaki

2015

Trans. Japan Soc. Aero. S Sci.

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“…A useful application of the gradient is for extrapolation as discussed in Ghate and Giles. 6 The extrapolated function values can, for example, be used for an inexpensive Monte Carlo (IMC) simulation 5,43,44 for uncertainty analysis as extrapolation of the function value is much less expensive than a nonlinear function evaluation.…”

Section: Ivb Linear Extrapolationmentioning

confidence: 99%

Uncertainty Quantification in Viscous Hypersonic Flows using Gradient Information and Surrogate Modeling

Lockwood

Rumpfkeil

Yamazaki

et al. 2011

49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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In this paper, the use of gradient observations in conjunction with surrogate models for the purposes of uncertainty quantification within the context of viscous hypersonic flows is examined. The approach is presented within the context of both perfect gas and nonequilibrium real gas simulations and can be used to quantify the uncertainty in an output of interest due to the uncertainty associated with various model and design parameters. The gradient of an objective is calculated via a discrete adjoint approach. By using an adjoint based approach, the sensitivity of an objective to a large number of input parameters can be calculated in an efficient and timely manner. With these sensitivity derivatives, the uncertainty in an objective due to input parameters is calculated in various ways. Initially, first-order methods, such as the method of moments and linear extrapolation, are used to represent the design space and calculate relevant output statistics. In order to improve upon these first-order approaches, Kriging and gradient enhanced Kriging models are created for the function space and serve as a basis for additional inexpensive Monte Carlo sampling. The probability distribution functions and statistics generated by these linear and Kriging based methods compare favorably with the results of nonlinear Monte Carlo sampling and can serve as a basis for further exploration of uncertainty quantification in hypersonic flows.

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“…Thus, the use of surrogate models for UQ has become popular. [7][8][9][10][11][12][13][14][15][16][17] The idea of a surrogate model is to replace expensive function evaluations with an approximate but inexpensive functional representation which can be probed exhaustively if required. Recently, we have developed a gradient and Hessian enhanced co-Kriging approach with a dynamic sample point selection.…”

Section: Introductionmentioning

confidence: 99%

Optimizations Under Uncertainty Using Gradients, Hessians, and Surrogate Models

Rumpfkeil

2013

AIAA Journal

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In this paper a first-order moment method and a Kriging surrogate model are used for optimizations under uncertainty applied to two-bar truss designs and two-dimensional lift-constrained drag minimizations. Given uncertainties in statistically independent, random, normally distributed input variables, the two approaches are used to propagate these uncertainties through the mathematical model and to approximate output statistics of interest. In order to assess the validity of the approximations, the results are compared with full Monte-Carlo simulations. When using first-order moment methods for robust optimizations, first-order sensitivity derivatives appear in the objective function and system constraints. Therefore, second-order sensitivity derivatives are needed for gradient-based optimization approaches. When the Kriging surrogate model is used to calculate the objective function value and system constraints it will be shown that a gradient predictor for the Kriging model can be successfully used for gradient-based optimizations. Both, the Kriging and first-order moment method approaches enable predictions of the mean and variance of quantities of interest while at the same time keeping the computational cost for optimization under uncertainty problems manageable.

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Estimation of the Impact of Geometrical Uncertainties on Aerodynamic Coefficients Using CFD

Cited by 13 publications

References 1 publication

Stochastic Drag Analysis via Polynomial Chaos Uncertainty Quantification

Stochastic Drag Analysis via Polynomial Chaos Uncertainty Quantification

Uncertainty Quantification in Viscous Hypersonic Flows using Gradient Information and Surrogate Modeling

Optimizations Under Uncertainty Using Gradients, Hessians, and Surrogate Models

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