Distributed, Embedded Sensing for Quasi-Static Shape Control of Wings

Sponsored by: Air Force Research Laboratory

The performance of an aircraft during takeoff, cruise, loiter, and maneuvering is highly dependent on the aerodynamics of the wing. Since the beginning of modern flight, engineers have considered the use of morphing wings to control the flight of the air vehicle. Wing morphing allows for control of an aircraft through twisting and alterations of the shape of the wing. Modern aircraft use ailerons, elevators, and rudders to change the aircraft from one equilibrium position to another, to produce non-equilibrium-accelerated motions, such as maneuvers, and to alter the sectional lift of the wing. Although this technology is quite mature and generally does not suffer from reliability problems, drawbacks include lift variation with angle of attack and Mach number, loss of control effectiveness (i.e., control reversal), low actuation bandwidth, mechanical complexity, and weight penalty.

In recent years, researchers have investigated the use of active wing shaping (morphing) concepts including twist and camber to optimize the aerodynamic efficiency of the wing for individual flight regimes, as well as to augment and/or replace primary flight control. However, a key enabling technology that is required for practical implementation of wing shape, or wing morphing control, is a means to measure wing deformations in real-time. To this end, AITHER investigated the feasibility of using embedded fiber Bragg grating (FBG) strain sensors to determine the wing shape in real-time.

Based on AITHER’s experience of shape measurement of towed sonar arrays, two approaches were investigated. The first approach involved a neural network that predicted the “wing shape” based on the recorded strain data from a distributed set of FBG sensors. The second approach took the recorded strain readings from the FBG sensors and formulated a surface based on curvature and elasticity models of the cantilever plate. Analytical simulations were performed for each method and tested against a cantilever plate experimental setup with 36 FBG sensors mounted in longitudinal, transverse, and off-axis orientations on the plate’s surface. All experimental data was taken with Micron Optics Inc. si425, controlled by an open architecture LabVIEW program that is capable of being modified to display the plate shape in real-time. For complete analytical and experimental results, refer to the SPIE journal paper entitled “Static Shape Measurements Using a Multiplexed Fiber Bragg Grating Sensor System” in publications.