The capability to control unsteady separated flow fields could dramatically enhance aircraft agility. To enable control, however, real-time prediction of these flow fields over a broad parameter range must be realized. The present work describes real-time predictions of three-dimensional unsteady separated flow fields and aerodynamic coefficients using neural networks. Unsteady surface-pressure readings were obtained from an airfoil pitched at a constand rate through the static stall angle. All data sets were comprised of 15 simultaneously acquired pressure records and one pitch angle record. Five such records and the associated pitch angle histories were used to train the neural network using a time-series algorithm. The results indicated that the neural network accurately predicted the unsteady separated flow fields as well as the aerodynamic coefficients to within 5 % of the experimental data.Consistent results were obtained both for the training set as well as for generalization to both other constant pitch rates and to sinusoidal pitch motions. The results clearly indicated that the neural-network model could predict the unsteady surface-pressure distributions and aerodynamic coefficients based solely on angle of attack information. The capability for real-time prediction of both unsteady separated flow fields and aerodynamic coefficients across a wide range of parameters in turn provides a critical step towards the development of control systems targeted at exploiting unsteady aerodynamics for aircraft maneuverability enhancement.
Real-time prediction of unsteady aerodynamics: Application for aircraft control and maneuverability enhancement
Vorhersage in Echtzeit von unstetigen aerodynamischen Vorgängen mit neuronalen Netzen: Eine Anwendung zur Flugregelung und Verbesserung der Manövrierbarkeit
IEEE Transactions on Neural Networks ; 6 , 6 ; 1461-1468
1995
8 Seiten, 10 Bilder, 5 Tabellen, 19 Quellen
Article (Journal)
English
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