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

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Abstract

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.

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Acknowledgments

The authors acknowledge the supports by ENSMA, METU, TUBITAK, CNRS and French Embassy in Turkey.

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  1. Aerospace Engineering Department, Middle East Technical University, 06531, Ankara, Turkey

    Dilek Funda Kurtulus

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  1. Dilek Funda Kurtulus
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Correspondence to Dilek Funda Kurtulus.

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Kurtulus, D.F. Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network. Neural Comput & Applic 18, 359–368 (2009). https://doi.org/10.1007/s00521-008-0186-2

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

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