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Lift coefficient prediction at high angle of attack using recurrent neural network

Suresh, S and Omkar, SN and Mani, V and Prakash, Guru TN (2003) Lift coefficient prediction at high angle of attack using recurrent neural network. In: Aerospace Science and Technology, 7 (8). pp. 595-602.

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In this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift $(C_Z)$ at high angle of attack. In our approach, the coefficient of lift $(C_Z)$ obtained from the experimental results (wind tunnel data) at different mean angle of attack $\theta_{mean}$ is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict $C_Z$ in the proposed method is less and it is easy to incorporate in any commercially available rotor code.

Item Type: Journal Article
Publication: Aerospace Science and Technology
Publisher: Elsevier
Additional Information: Copyright of this article belongs to Elsevier.
Keywords: Unsteady rotor blade analysis;Dynamic stall;Memory neuron network;Recurrent multilayer perceptron network
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 02 Jun 2006
Last Modified: 19 Sep 2010 04:28
URI: http://eprints.iisc.ac.in/id/eprint/7351

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