A new procedure for airfoil definition

Venkataraman, P.

doi:10.2514/6.1995-1875

Cited by 12 publications

(4 citation statements)

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“…structural model is not considered in this work but an important physical consideration is to impose an upper bound on the wing's root bending moment defined in (7). The optimization problem for the NACA-5 parametererization is what we have seen previously without the bending moment constraint (Fig.…”

Section: Optimization Of Variable-span Wingsmentioning

confidence: 99%

“…Venkataraman [7] introduced the idea of using splines to parametrize the airfoil where two Bézier curves were used to define upper and lower surfaces, and have since been used to define thickness and camber distributions [8,9]. Basis splines (B-splines) were later adopted for similar parameterization methods [10][11][12] as they are less susceptible to bumps or fluctuations because the order of the curve is not defined by the number of points.…”

Section: Introductionmentioning

confidence: 99%

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Aerodynamic Shape Optimization of Aircraft Wings Using Panel Methods

et al. 2020

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Section: Optimization Of Variable-span Wingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aerodynamic Shape Optimization of Aircraft Wings Using Panel Methods

et al. 2020

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“…Common parameterization for aerodynamic shapes includes splines (e.g., B-spline and Bézier curves) [47][48][49], free-form deformation (FFD) [25,26], class-shape transformations (CST) [50,51], PARSEC [52,53], and Bézier-PARSEC [54].…”

Section: B Shape Parameterizationmentioning

confidence: 99%

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Chen¹,

Chiu²,

Fuge³

2020

Preprint

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Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design space. One approach to combat this problem is to reduce the design space dimension by obtaining a new representation. This requires a parametric function that compactly and sufficiently describes useful variation in shapes. We propose a deep generative model, Bézier-GAN, to parameterize aerodynamic designs by learning from shape variations in an existing database. The resulted new parameterization can accelerate design optimization convergence by improving the representation compactness while maintaining sufficient representation capacity. We use the airfoil design as an example to demonstrate the idea and analyze Bézier-GAN's representation capacity and compactness. Results show that Bézier-GAN both (1) learns smooth

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“…Parameterization maps a set of parameters to points along a smooth curve or surface via a parametric function. Common parameterization for aerodynamic shapes includes splines (e.g., B-spline and Bézier curves) [39][40][41], free-form deformation (FFD) [42,43], class-shape transformations (CST) [44,45], PARSEC [46,47], and Bézier-PARSEC [48]. While this work does not study paramterization, we show the optimization performance of two paramterization approaches, namely nonuniform rational B-splines (NURBS) [49] and PARSEC [46], in comparison to our proposed method.…”

Section: B Shape Parameterizationmentioning

confidence: 99%

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

Chen

Chiu

Fuge

2019

AIAA Scitech 2019 Forum

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Global optimization of aerodynamic shapes requires a large number of expensive CFD simulations because of the high dimensionality of the design space. One means to combat that problem is to reduce the dimension of the design space-for example, by constructing low dimensional parametric functions (such as PARSEC and others)-and then optimizing over those parameters instead. Such approaches require first a parametric function that compactly describes useful variation in airfoil shape-a non-trivial and error-prone task. In contrast, we propose to use a deep generative model of aerodynamic designs (specifically airfoils) that reduces the dimensionality of the optimization problem by learning from shape variations in the UIUC airfoil database. We show that our data-driven model both (1) learns realistic and compact airfoil shape representations and (2) empirically accelerates optimization convergence by over an order of magnitude.

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A new procedure for airfoil definition

Cited by 12 publications

References 0 publications

Aerodynamic Shape Optimization of Aircraft Wings Using Panel Methods

Aerodynamic Shape Optimization of Aircraft Wings Using Panel Methods

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

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