A Comparison of Airfoil Shape Parameterization Techniques for Early Design Optimization

Sripawadkul, Vis; Padulo, Mattia; Guenov, Marin D.

doi:10.2514/6.2010-9050

Cited by 72 publications

(43 citation statements)

References 8 publications

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“…Sripawadkul [22] simplified the research by adding intuitiveness into 5 principles as criteria for parameterization method evaluation (shown in Table 1 with makers ranging from 0.0 to 4.0). The 5 principles:…”

Section: Parameterizationmentioning

confidence: 99%

Artificial neural network based inverse design: Airfoils and wings

Sun

Wang

2015

Aerospace Science and Technology

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Section: Parameterizationmentioning

confidence: 99%

Artificial neural network based inverse design: Airfoils and wings

Sun

Wang

2015

Aerospace Science and Technology

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“…Sripawuadkul et al [21] compared the Hicks-Henne, B-spline, PARSEC and CST methods, though instead of looking at individual test cases rated each method out of four across five different criteria: parsimony; completeness; orthogonality; flawlessness; and intuitiveness. Over these criteria, they rated the CST method highest, followed by PARSEC, B-splines and Hicks-Henne bumps.…”

Section: Introductionmentioning

confidence: 99%

Review of Aerofoil Parameterisation Methods for Aerodynamic Shape Optimisation

Masters

Taylor

Rendall³

et al. 2015

53rd AIAA Aerospace Sciences Meeting

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This paper presents a review of aerofoil shape parameterisation methods that can be used for aerodynamic shape optimisation. Six parameterisation methods are considered for a range in design variables: Class function/Shape function Transformations (CST); B-splines; Hicks-Henne bump functions; a domain element approach using Radial Basis functions (RBF); Bèzier surfaces; and a singular value decomposition modal extraction method (SVD); plus the PARSEC method. The performance of each method is analysed by considering geometric shape recovery on over 1000 aerofoils using a range of design variables, testing the efficiency of design space coverage. A more in-depth analysis is then presented for three aerofoils, NACA4412, RAE2822 and ONERA M6 (D section), with geometric error and convergence of the resulting aerodynamic properties presented. In the large scale test it is shown that, for all the methods, a large number of design variables are needed to achieve significant design space coverage. For example at least 25 design variables are needed to cover 80% of the design space regardless of the method used; this is often higher than is desired for two-dimensional studies, suggesting that further work may be required to reduce the number of design variables needed.

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“…The most important characteristic of the CAD model is to be highly flexible in order to represent a variety of designs as large as possible. Secondly the model must be robust and reliable, since there will not be a specialist manually entering new parameters and supervising the update process (Amadori et al 2008;Sripawadkul et al 2010;Wang et al 2013). Flying wing configuration has the characteristics of simple shape and blended wing body, and the UAV can be seen as a special wing which is connected together by 3 segments.…”

Section: Generating Parametric Modelmentioning

confidence: 99%

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

Pan

Huang

et al. 2017

J.Aerosp. Technol. Manag.

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An optimization strategy is constructed to solve the aerodynamic and structural optimization problems in the conceptual design of double-swept flying wing aircraft. Aircraft preliminary aerodynamic and structural design optimization is typically based on the application of a deterministic approach of optimizing aerodynamic performance and structural weight. In aerodynamic optimization, the objective is to minimize induced drag coefficient, and the structural optimization aims to find the minimization of the structural weight. In order to deal with the multiple objective optimization problems, an optimization strategy based on collaborative optimization is adopted. Based on the optimization strategy, the optimization process is divided into system level optimization and subsystem level optimization. The system level optimization aims to obtain the optimized design which meets the constraints of all disciplines. In subsystem optimization, the optimization process for different disciplines can be executed simultaneously to search for the consistent schemes. A double-swept configuration of flying wing aircraft is optimized through the suggested optimization strategy, and the optimization results demonstrate the effectiveness of the method.

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A Comparison of Airfoil Shape Parameterization Techniques for Early Design Optimization

Cited by 72 publications

References 8 publications

Artificial neural network based inverse design: Airfoils and wings

Artificial neural network based inverse design: Airfoils and wings

Review of Aerofoil Parameterisation Methods for Aerodynamic Shape Optimisation

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

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