Real-time prediction of unsteady aerodynamics: Application for aircraft control and manoeuvrability enhancement
- 1 January 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 6 (6) , 1461-1468
- https://doi.org/10.1109/72.471362
Abstract
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 constant 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. Post-training, the input to the network was the pitch angle (alpha), the angular velocity (dalpha/dt), and the initial 15 recorded surface pressures at time (t (0)). Subsequently, the time (t+Deltat) network predictions, for each of the surface pressures, were fed back as the input to the network throughout the pitch history. 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 manoeuvrability enhancement.Keywords
This publication has 16 references indexed in Scilit:
- Unsteady vortex dynamics and surface pressure topologies on a finite pitching wingJournal of Aircraft, 1994
- Quasi-linear neural networks: application to the prediction and control of unsteady aerodynamicsPublished by SPIE-Intl Soc Optical Eng ,1993
- Neural network prediction of three-dimensional unsteady separated flow fieldsPublished by American Institute of Aeronautics and Astronautics (AIAA) ,1993
- Implementations of learning control systems using neural networksIEEE Control Systems, 1992
- Intelligent control using neural networksIEEE Control Systems, 1992
- Identification of aerodynamic coefficients using computational neural networksPublished by American Institute of Aeronautics and Astronautics (AIAA) ,1992
- Neural networks approach to AIAA aircraft control design challengePublished by American Institute of Aeronautics and Astronautics (AIAA) ,1991
- Parameter estimation in space systems using recurrent neural networksPublished by American Institute of Aeronautics and Astronautics (AIAA) ,1991
- Neural networks for self-learning control systemsIEEE Control Systems Magazine, 1990
- Unsteady surface pressure measurements on a pitching rectangular wingPublished by American Institute of Aeronautics and Astronautics (AIAA) ,1988