Title

Aerodynamic Testing of a Smart Composite Wing Using Fiber-Optic Strain Sensing and Neural Networks

Abstract

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.

Department(s)

Mechanical and Aerospace Engineering

Second Department

Electrical and Computer Engineering

Sponsor(s)

University of Missouri Research Board

Keywords and Phrases

Fiber-Optic Instruments

Library of Congress Subject Headings

Aerodynamics
Finite element method
Fluid dynamics
Galerkin methods
Interferometers

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2000 Institute of Physics - IOP Publishing, All rights reserved.


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