A Parametric Study of Trailing Edge Flap Implementation on Three Different Airfoils Through an Artificial Neuronal Network

Abstract

Trailing edge flaps (TEFs) are high-lift devices that generate changes in the lift and drag coefficients of an airfoil. A large number of 2D simulations are performed in this study, in order to measure these changes in aerodynamic coefficients and to analyze them for a given Reynolds number. Three different airfoils, namely NACA 0012, NACA 64(3)-618, and S810, are studied in relation to three combinations of the following parameters: angle of attack, flap angle (deflection), and flaplength. Results are in concordance with the aerodynamic results expected when studying a TEF on an airfoil, showing the effect exerted by the three parameters on both aerodynamic coefficients lift and drag. Depending on whether the airfoil flap is deployed on either the pressure zone or the suction zone, the lift-to-drag ratio, CL/CD, will increase or decrease, respectively. Besides, the use of a larger flap length will increase the higher values and decrease the lower values of the CL/CD ratio. In addition, an artificial neural network (ANN) based prediction model for aerodynamic forces was built through the results obtained from the research.

CNN-based flow control device modelling on aerodynamic airfoils

“…Regarding flow control devices, there are some studies in which deep learning techniques are used for a better understanding of the behavior of flow control elements in airfoils. For example, Rodriguez-Eguia et al 22 and Aramendia et al 5 used ANNs to predict the aerodynamic coefficients of an airfoil with flaps and Gurney flaps, respectively. However, in these studies the parameters are not predicted directly from the geometry and boundary conditions.…”

CFD Analysis and Shape Optimization of Airfoils Using Class Shape Transformation and Genetic Algorithm—Part I

“…Nowadays, the optimization of aerodynamic shape has become crucial with the rapid development of aerospace and mechanical engineering. As Igor Rodriguez-Eguia et al [3] explained, the idea to control devices and aerodynamic shapes that locally change the aerodynamic performance of the airfoil on the wind turbine blade. The parametric method in aerodynamic shape plays a crucial role in the optimized process of airfoil optimization.…”

CNN-Based Flow Control Device Modelling On Aerodynamic Airfoils

“…Regarding airfoils and their flow control devices, Thuerey et al [17] proposed a CNN to approximate the velocity and pressure fields obtained by Reynolds-Averaged Navier-Stokes (RANS)based Spalart-Allmaras [18] turbulence model on airfoils, and Bhatnagar et al [19] created a CNN to predict flow fields around airfoils. Chen et al [20] used a CNN to predict the drag (𝐶 𝐷 ) and lift (𝐶 𝐿 ) coefficients of different airfoils, and Rodriguez-Eguila et al [21] modelled the lift-to-drag ratio (𝐶 𝐿 /𝐶 𝐷 ) of three different airfoils with flaps on their TE by means of an Artificial Neural Network (ANN).…”