K.S. Dawson, L. Maxwell

A static artificial neural network is developed to predict limit cycle oscillation (LCO) amplitude levels and frequency of external store configurations on a fighter aircraft. This work is similar to previous networks studied for this purpose, however the inclusion of continuous analytic descriptors of the coupling aeroelastic modes and the flutter eigenvectors is used in the current work. A neural network with two layers, one with 26 nodes and the second with 8 nodes, is used for the prediction of the two outputs, LCO amplitude and LCO frequency. The methodology for the analytic descriptors is presented, as well as sample network input training data. The results of the neural network using the analytic descriptors show a capability to predict trends for both the LCO onset speeds and the overall amplitude levels. This neural network was also capable of predicting an observed trend of LCO amplitude levels decreasing partially or completely after a certain airspeed.

International Forum on Aeroelasticity and Structural Dynamics, 2005, München

Conference Paper

englisch

21,0 x 29,7 cm, 12 Seiten

DGLR-Bericht, 2005, 2005-04, International Forum on Aeroelasticity and Structural Dynamics 2005; S.1-12; 2005; Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn

NA

in getr. Zählung;

flight tests, neural networks, flutter

Bibliothek

2005