Aerodynamic Testing of a Smart Composite Wing Using Fiber-Optic Strain Sensing and Neural Networks
The feasibility of developing a smart wing using composite materials, fiber-optic sensors, and neural networks is investigated. Strain and aerodynamic lift force induced in a wing model at different air speeds and angles-of-attack are experimentally determined in an open circuit wind tunnel. The smart wing model consisted of a glass/epoxy composite beam with an interferometric fiber-optic sensor mounted at the wing root. The strains were correlated to the set of air velocities and angles-of-attack using a feedforward backpropagation neural network approach. The resulting neural network simulation could predict the experimental strain in real time with an average error of 3.17%. Finite-element analysis prediction of aerodynamic lift calculated using measured strain at the wing root generally agreed with thin airfoil theory, the differences being attributed to uncertainty in composite material properties.
A. Lunia et al., "Aerodynamic Testing of a Smart Composite Wing Using Fiber-Optic Strain Sensing and Neural Networks," Smart Materials and Structures, Institute of Physics - IOP Publishing, Dec 2000.
The definitive version is available at http://dx.doi.org/10.1088/0964-1726/9/6/305
Mechanical and Aerospace Engineering
Electrical and Computer Engineering
University of Missouri Research Board
Keywords and Phrases
Library of Congress Subject Headings
Finite element method
Article - Journal
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