{'fraction_non_alphanumeric': 0.028481012658227847, 'fraction_numerical': 0.011075949367088608, 'mean_word_length': 5.052631578947368, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Parametric Study of Trailing Edge Flap Implementation on Three Different Airfoils Through an Artificial Neuronal Network

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.