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.
- 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
Available at: http://works.bepress.com/rohit-dua/2/