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Advisor(s)
Abstract(s)
The recent increase in interest in artificial intelligence and neural networks has stirred up various industries. Inevitably, its application will trickle down to the most fundamental studies, for instance, unsteady aerodynamics. The present paper serves the purpose of exploring the ability of a recurrent neural network to predict flapping airfoil aerodynamics, in particular the lift coefficient of a plunging NACA0012 airfoil. Thus, a neural network is designed and trained using motion parameters, such as motion frequency and effective angle of attack, to output the instantaneous lift coefficient over a plunging period. Training data is generated using a panel code (HSPM) for fast generation and early testing. Results show that the neural network can adequately predict the lift coefficient for various conditions, including plunging kinematics that are far from the training domain. Future work will build on this framework and extend it to other aerodynamic coefficients using CFD results and experiments, which should enhance the value of the estimates.
Description
Keywords
Aerodynamic Coefficients Flapping Airfoil Recurrent Neural Network Aerodynamic Performance Computational Fluid Dynamics Artificial Intelligence Unsteady Aerodynamics Reduced Order Modelling Helicopter Blade Flow Conditions
Citation
João A. Pereira, Emanuel A. Camacho, Flavio D. Marques and Andre R. Silva, "Flapping Airfoil Aerodynamics using Recurrent Neural Network", 2024 AIAA SciTech Forum and Exposition, Orlando (FL), USA , 8-12 January 2024
Publisher
American Institute of Aeronautics and Astronautics, Inc