Inverse Method Using Finite Strain Measurements to Determine Flight Load Distribution Functions

Coates, Cameron; Thamburaj, Priya

doi:10.2514/1.21905

Cited by 23 publications

(7 citation statements)

References 4 publications

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“…There have been a number of studies to obtain stable solutions. For example, using the inverse interpolation method, Shkarayev et al assumed polynomial functions, whereas Coates et al used Fourier cosine series terms for spatial load distribution functions [11,12]. Nakamura et al reported a finite-element-based inverse analysis in which aerodynamic restriction was coupled with elastic equations [13].…”

Section: Introductionmentioning

confidence: 99%

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

Wada

Tamayama

2019

Applied Sciences

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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°.

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Section: Introductionmentioning

confidence: 99%

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

Wada

Tamayama

2019

Applied Sciences

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“…However, in general, the in-flight load distribution of a wing cannot be measured directly by sensors. It can be predicted by other structural conditions, such as the strain and displacement, and this problem should be solved by an inverse analysis [33][34][35].…”

Section: Load Identificationmentioning

confidence: 99%

Structural health monitoring by using fiber-optic distributed strain sensors with high spatial resolution

2013

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In this paper, we review our researches on the topics of the structural health monitoring (SHM) with the fiber-optic distributed strain sensor. Highly-dense information on strains in a structure can be useful to identify some kind of existing damages or applied loads in implementation of SHM. The fiber-optic distributed sensors developed by the authors have been applied to the damage detection of a single-lap joint and load identification of a beam simply supported. We confirmed that the applicability of the distributed sensor to SHM could be improved as making the spatial resolution higher. In addition, we showed that the simulation technique considering both structural and optical effects seamlessly in strain measurement could be powerful tools to evaluate the performance of a sensing system and design it for SHM. Finally, the technique for simultaneous distributed strain and temperature measurement using the PANDA-fiber Bragg grating (FBG) is shown in this paper, because problems caused by the cross-sensitivity toward strain and temperature would be always inevitable in strain measurement for SHM.

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“…Since the first demonstration of photosensitivity in fiber optic waveguides [48], fiber optics have seen many applications such as structural and material process control, adaptive structural positioning, chemical and biomedical processing and many other applications [31]. They have also been used to estimate loads [49].…”

Section: Dynamic Shape Estimationmentioning

confidence: 99%

Robust Modal Filtering for Control of Flexible Aircraft

Suh

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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me invaluable advice and contributed to my work. My foremost and sincere appreciation goes to Dr. Dimitri Mavris, my thesis advisor for his course corrections and thoughtful input which shaped my dissertation. Special thanks to Mr. Steve Jacobson, Branch Chief of NASA's Dryden Flight Research Center (DFRC), who inspired the thesis concept. Gratitude is owed to Mr. Martin Brenner for confirming that the modal filtering approach was mathematically sound from the beginning. I would like to acknowledge thesis committee members Dr.

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Inverse Method Using Finite Strain Measurements to Determine Flight Load Distribution Functions

Cited by 23 publications

References 4 publications

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

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

Structural health monitoring by using fiber-optic distributed strain sensors with high spatial resolution

Robust Modal Filtering for Control of Flexible Aircraft

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