Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

Date

2009-05-01

Author

Kurtuluş, Dilek Funda

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The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg-Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.

Subject Keywords

Software, Artificial Intelligence

URI

https://hdl.handle.net/11511/36761

Journal

NEURAL COMPUTING & APPLICATIONS

DOI

https://doi.org/10.1007/s00521-008-0186-2

Collections

Department of Aerospace Engineering, Article

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Citation Formats

D. F. Kurtuluş, “Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network,” NEURAL COMPUTING & APPLICATIONS, pp. 359–368, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36761.