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.
K. M. Isaac et al., "Intelligent Strain Sensing on a Smart Composite Wing using Extrinsic Fabry-Perot Interferometric Sensors and Neural Networks," Proceedings of the International Joint Conference on Neural Networks, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/IJCNN.2003.1223988
International Joint Conference on Neural Networks, 2003
Electrical and Computer Engineering
Mechanical and Aerospace Engineering
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)
Article - Conference proceedings
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