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Topic

Airfoil

About: Airfoil is a research topic. Over the lifetime, 24696 publications have been published within this topic receiving 337709 citations. The topic is also known as: aerofoil & wing section.


Papers
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Journal ArticleDOI
TL;DR: In this paper, an optimization method, featuring a loop algorithm which includes a genetic optimizer, a two-dimensional URANS CFD model, a fitness calculator and an airfoil generator, is created and deployed to develop a new aerodynamic shape which can possibly increase the aerodynamic performance of a Darrieus wind turbine.

71 citations

Proceedings ArticleDOI
01 Jan 1992
TL;DR: In this article, a 2D numerical investigation was performed to determine the effect of a Gurney flap on a NACA 4412 airfoil and the results were obtained using the Baldwin-Barth one-equation turbulence model.
Abstract: A 2D numerical investigation was performed to determine the effect of a Gurney flap on a NACA 4412 airfoil. A Gurney flap is a flat plate on the order of 1 to 3 percent of the airfoil chord length, oriented perpendicular to the airfoil chord line and located at the trailing edge of the airfoil. An incompressible Navier Stokes code, INS2D, was used to calculate the flow field about the airfoil. The fully turbulent results were obtained using the Baldwin-Barth one-equation turbulence model. Gurney flap sizes of 0.5 , 1, 1.25, 1.5, 2, and 3 percent of the airfoil chord were studied. Computational results were compared with experimental results where possible. The numerical solutions show that the Gurney flap increases airfoil lift coefficient with only a slight increase in drag coefficient. Use of a 1.5 percent chord Gurney flap increases the maximum lift coefficient by approximately 0.3 and decreases the angle of attack for a given lift coefficient by more than 3 deg. The numerical solutions exhibit detailed flow structures at the trialing edge and provide a possible explanation for the increased aerodynamic performance.

71 citations

Proceedings ArticleDOI
01 Jan 1992
TL;DR: In this paper, the repeatability of the ice shape over a range of icing conditions was evaluated in the Icing Research Tunnel (IRT) at the NASA Lewis Research Center to document the current capability of the IRT.
Abstract: Tests were conducted in the Icing Research Tunnel (IRT) at the NASA Lewis Research Center to document the current capability of the IRT, focused mainly on the repeatability of the ice shape over a range of icing conditions. Measurements of drag increase due to the ice accretion were also made to document the repeatability of drag. Surface temperatures of the model were obtained to show the effects of latent-heat release by the freezing droplets and heat transfer through the ice layer. The repeatability of the ice shape was very good at low temperatures, but only fair at near freezing temperatures. In general, drag data shows good repeatability.

71 citations

Journal ArticleDOI
TL;DR: The results clearly indicated that the neural-network model could predict the unsteady surface-pressure distributions and aerodynamic coefficients based solely on angle of attack information.
Abstract: The capability to control unsteady separated flow fields could dramatically enhance aircraft agility. To enable control, however, real-time prediction of these flow fields over a broad parameter range must be realized. The present work describes real-time predictions of three-dimensional unsteady separated flow fields and aerodynamic coefficients using neural networks. Unsteady surface-pressure readings were obtained from an airfoil pitched at a constant rate through the static stall angle. All data sets were comprised of 15 simultaneously acquired pressure records and one pitch angle record. Five such records and the associated pitch angle histories were used to train the neural network using a time-series algorithm. Post-training, the input to the network was the pitch angle (/spl alpha/), the angular velocity (d/spl alpha//dt), and the initial 15 recorded surface pressures at time (t/sub 0/). Subsequently, the time (t+/spl Delta/t) network predictions, for each of the surface pressures, were fed back as the input to the network throughout the pitch history. The results indicated that the neural network accurately predicted the unsteady separated flow fields as well as the aerodynamic coefficients to within 5% of the experimental data. Consistent results were obtained both for the training set as well as for generalization to both other constant pitch rates and to sinusoidal pitch motions. The results clearly indicated that the neural-network model could predict the unsteady surface-pressure distributions and aerodynamic coefficients based solely on angle of attack information. The capability for real-time prediction of both unsteady separated flow fields and aerodynamic coefficients across a wide range of parameters in turn provides a critical step towards the development of control systems targeted at exploiting unsteady aerodynamics for aircraft manoeuvrability enhancement. >

71 citations

Journal ArticleDOI
Steven Vogel1
TL;DR: The structural basis for the performance of fly wings and the relationship of their characteristics to their opening conditions are discussed in this article, where the fly wing behaves as if encountering a Reynolds number below the actual value.
Abstract: 1. The variation of the lift and drag of fruit-fly wings with angle of attack and velocity was compared with that of thin plates. 2. High drag and low ratios of lift to drag characterized these airfoils, the primary difference being the absence of stalling in the fly wings. 3. Flow photographs and determinations of stall point on thin plates suggested that the fly wing behaves as if encountering a Reynolds number below the actual value. 4. At positive angles of attack camber improved the aerodynamic characteristics of fly wings; at negative angles uncambered wings were superior. 5. The structural basis for the performance of fly wings and the relationship of their characteristics to their opening conditions are discussed.

71 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,083
20221,871
2021923
2020979
20191,097
20181,002