Abstract
The use of Artificial Neural Networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN algorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.
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References
Ferman, M. A., Patel, S. R., Zimmerman, N. H., and Gerstenkorn, G.: A Unified Approach to Buffet Response, 70th Meeting of Structures and Materials Panel, AGARD 17, Sorrento, Italy, April (1990).
Jacobs, J. H., Hedgecock, C. E., Lichtenwalner, P. F., and Pado, L. E.: Use of Artificial Neural Networks for Buffet Environments, Journal of Aircraft, Vol. 31, No. 4, July–Aug (1994).
Martin, C.A., Thompson, D.H.: Scale Model Measurements of Fin Buffet Due to Vortex Bursting on F/A-18, AGARD Manoeuvring Aerodynamics, AGARD-CP-497, (1991).
Meyn, L. A., James, K. D.: Full Scale Wind Tunnel Studies of F/A-18 Tail Buffet, AIAA Applied Aerodynamics Conference, AIAA 93-3519, August 9–11, (1993), Monterey, CA.
Pettit C.L., Brown D.L., and Pendleton E.: Wind Tunnel Tests of Full-Scale F/A-18 Twin Tail Buffet: A Summary of Pressure and Response Measurements, AIAA Paper 94-3476, (1994).
Kandil, O.A., Sheta, E.F., Massey, S.J.: Twin Tail/Delta Wing Configuration Buffet due to Unsteady Vortex Breakdown Flow, AIAA Paper 96-2517-CP, (1996).
Gee, K., Murman, S. M., and Schiff, L. B.,: Computation of F/A-18 Tail Buffet, Journal of Aircraft, Vol. 33, No. 6, November–December (1996).
Levinski, O.: Prediction of Buffet Loads on Twin Vertical Tail Using Vortex Method, Australian Department of Defence, Aeronautical and Maritime Research Laboratory, DSTO-RR-0217, (2001).
Hagan, M. T., Demuth, H., Beale, M.: Neural Network Design, PWS Publishing Company, (1996).
Demuth, H., Beale, M.: Neural Network Toolbox, For Use with MATLAB, The Mathworks, Inc, (2000).
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© 2002 Springer-Verlag Berlin Heidelberg
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Levinski, O. (2002). Use of Artificial Neural Networks for Buffet Loads Prediction. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_2
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DOI: https://doi.org/10.1007/3-540-48035-8_2
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