Abstract

Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping provides a method to predict the stall condition by comparing the strain available in real time and the predicted strain by the trained neural network.

Meeting Name

International Joint Conference on Neural Networks, 2003

Department(s)

Electrical and Computer Engineering

Second Department

Mechanical and Aerospace Engineering

Third Department

Computer Science

Keywords and Phrases

Fabry-Perot Interferometers; Aerodynamic Condition; Aerodynamics; Aerospace Computing; Aerospace Materials; Air Speeds; Angle of Attack; Backpropagation; Backpropagation Training Algorithm; Beams (Structures); Closed Circuit Wind Tunnel; Extrinsic Fabry-Perot Interferometric Sensors; Feedforward Neural Nets; Feedforward Neural Networks; Fibre Optic Sensors; Glass Fibre Reinforced Plastics; Glass/Epoxy Composite Beam; Intelligent Sensors; Intelligent Strain Sensing; Intelligent Structures; Smart Composite Wing; Stall Condition; Strain Measurement; Strain Prediction; Strain Sensors

International Standard Serial Number (ISSN)

1098-7576

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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