Lift coefficient prediction at high angle of attack using recurrent neural network-Reference-Cited by-同舟云学术

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

Published:2003-12 Issue:8 Volume:7 Page:595-602
ISSN:1270-9638
Container-title:Aerospace Science and Technology
language:en
Short-container-title:Aerospace Science and Technology

Author:

Suresh S.,Omkar S.N.,Mani V.,Guru Prakash T.N.

Publisher

Elsevier BV

Subject

Aerospace Engineering

Reference21 articles.

1. Semi-empirical model for the dynamic stall of airfoils in view of the application to the calculation of responses of a helicopter blade in forward flight;Tran,1980

2. Fluid mechanics of dynamic stall. Part I. Unsteady flow concepts;Ericsson;J. Fluid Structures,1988

3. Principles of Helicopter Dynamics;Leishman,2000

4. J.A. Ekaterinaris, G.R. Srinivasan, W.J. Croskey, Present capabilities of predicting two-dimensional dynamic stall, AGARD CP-552, 1994

5. Neural Network: A Comprehensive Foundation;Haykins,1994

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