20 April 2016 Estimation of morphing airfoil shape and aerodynamic load using artificial hair sensors
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Abstract
An active area of research in adaptive structures focuses on the use of continuous wing shape changing methods as a means of replacing conventional discrete control surfaces and increasing aerodynamic efficiency. Although many shape-changing methods have been used since the beginning of heavier-than-air flight, the concept of performing camber actuation on a fully-deformable airfoil has not been widely applied. A fundamental problem of applying this concept to real-world scenarios is the fact that camber actuation is a continuous, time-dependent process. Therefore, if camber actuation is to be used in a closed-loop feedback system, one must be able to determine the instantaneous airfoil shape as well as the aerodynamic loads at all times. One approach is to utilize a new type of artificial hair sensors developed at the Air Force Research Laboratory to determine the flow conditions surrounding deformable airfoils. In this work, the hair sensor measurement data will be simulated by using the flow solver XFoil, with the assumption that perfect data with no noise can be collected from the hair sensor measurements. Such measurements will then be used in an artificial neural network based process to approximate the instantaneous airfoil camber shape, lift coefficient, and moment coefficient at a given angle of attack. Various aerodynamic and geometrical properties approximated from the artificial hair sensor and artificial neural network system will be compared with the results of XFoil in order to validate the approximation approach.
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Nathan S. Butler, Nathan S. Butler, Weihua Su, Weihua Su, Kaman S. Thapa Magar, Kaman S. Thapa Magar, Gregory W. Reich, Gregory W. Reich, "Estimation of morphing airfoil shape and aerodynamic load using artificial hair sensors", Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 980329 (20 April 2016); doi: 10.1117/12.2219520; https://doi.org/10.1117/12.2219520
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