Abstract
The load and angle of attack (AoA) for wing structures are critical parameters to be monitored for efficient operation of an aircraft. This study presents wing load and AoA identification techniques by integrating an optical fiber sensing technique and a neural network approach. We developed a 3.6-m semi-spanned wing model with eight flaps and bonded two optical fibers with 30 fiber Bragg gratings (FBGs) each along the main and aft spars. Using this model in a wind tunnel test, we demonstrate load and AoA identification through a neural network approach. We input the FBG data and the eight flap angles to a neural network and output estimated load distributions on the eight wing segments. Thereafter, we identify the AoA by using the estimated load distributions and the flap angles through another neural network. This multi-neural-network process requires only the FBG and flap angle data to be measured. We successfully identified the load distributions with an error range of −1.5–1.4 N and a standard deviation of 0.57 N. The AoA was also successfully identified with error ranges of −1.03–0.46° and a standard deviation of 0.38°.
Highlights
Load monitoring techniques for wing structures are a promising technology for enhancing the flight performance of future aircrafts
We propose an integration of the strain monitoring technique and the neural network approach for load identification and investigate its applicability through an experimental demonstration in a wind tunnel test
Considering that the flap flap angles are observable and the lift loads are identified, it was anticipated that the angle of attack (AoA) could angles are observable and the lift loads are identified, it was anticipated that the AoA could be be theoretically and practically solved by setting L and δ as inputs as theoretically and practically solved by setting L and δ as inputs as
Summary
Load monitoring techniques for wing structures are a promising technology for enhancing the flight performance of future aircrafts. Load monitoring is beneficial in terms of structural safety and reliability. Monitoring results of loading history can be utilized for structural fatigue analysis. Aerodynamic loads cause large deflections, which affect the aeroelastic stability and response [2,3,4]. Load monitoring is beneficial to estimate structural deformation and conduct reliable structural analysis based on practical assumptions of deflected wing shape. Another potential benefit of load monitoring is stable and efficient control of an aircraft, including unmanned air vehicles (UAVs). The load is a “phase-advanced” phenomena in this sense, and, its observation in addition to the conventional inertia observations could potentially enhance vehicle controllability
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