Wing Load and Angle of Attack Identification by Integrating Optical Fiber Sensing and Neural Network Approach in Wind Tunnel Test

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°.

Smart wing load alleviation through optical fiber sensing, load identification, and deep reinforcement learning

“…Based on the sensing data and the flap angles d, the spanwise distributed wing loads F and angle of attack ã were determined. To realize the state identification, a group of neural networks developed in a previous study [22] was employed. The flaps were controlled by another neural network, whose inputs included the identified states F and ã.…”

“…A high-aspect-ratio wing with a semi-span length of 3.6 m and the same configuration as that of the wing used in the previous study [22] was employed. The wing schematic is shown in figure 2.…”

“…We utilized a load and angle of attack identification technique developed in the previous study [22]. Specifically, a group of two neural networks was used to identify the load and angle of attack.…”

“…All the measurements were conducted in static conditions. A detailed description regarding the training data and hyperparameters applied in the supervised learning can be found in the previous study [22]. In the neuralnetwork-based approach, the errors (standard deviation) in the load and angle of attack for the training data were 0.14 N and 0.15°, respectively.…”

“…In addition, some researchers attempted to train neural networks to represent the strain-load relationships [19][20][21]. The feasibility of neural networks to identify the wing load distributions when fed with distributed optical fiber strain sensing information has been demonstrated [22].…”