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
Artificial hair sensors have been developed in the Air Force Research Laboratory for use in prediction of local flow
around airfoils and subsequent use in gust rejection applications. The on-going sensor development is based on a micro-sized
unmanned vehicle, resulting in a sensor design that is sensitive in that aircraft’s nominal flight condition (speed).
However, the active, or operating, region of the artificial hair sensor concept is highly dependent on the geometry and
properties of the hair, capillary, and carbon nanotubes that make up the sensor design. This paper aims at expanding the
flow measurement concept using artificial hair sensors to UAVs with different dimensions by properly sizing the
parameters of the sensors, according to the nominal flight conditions of the UAVs. In this work, the hair, made of glass
fiber, will be modeled as a cantilever beam with an elastic foundation, subject to external distributed aerodynamic drag.
Hair length, diameter, capillary depth, and carbon nanotube length will be scaled by keeping the maximum strain of the
carbon nanotubes constant for different sensors under different working conditions. Numerical studies will demonstrate
the feasibility of the scaling methodology by designing artificial hair sensors for UAVs with different dimensions and
flight conditions, starting from a baseline sensor design.
Artificial hair sensors (AHS) have been recently developed in Air Force Research Laboratory (AFRL) using carbon nanotube (CNT). The deformation of CNT in air flow causes voltage and current changes in the circuit, which can be used to quantify the dynamic pressure and aerodynamic load along the wing surface. AFRL has done a lot of essential work in design, manufacturing, and measurement of AHSs. The work in this paper is to bridge the current AFRL’s work on AHSs and their feasible applications in flight dynamics and control (e.g., the gust alleviation) of highly flexible aircraft. A highly flexible vehicle is modeled using a strain-based geometrically nonlinear beam formulation, coupled with finite-state inflow aerodynamics. A feedback control algorithm for the rejection of gust perturbations will be developed. A simplified Linear Quadratic Regulator (LQR) controller will be implemented based on the state-space representation of the linearized system. All AHS measurements will be used as the control input, i.e., wing sectional aerodynamic loads will be defined as the control output for designing the feedback gain. Once the controller is designed, closed-loop aeroelastic simulations will be performed to evaluate the performance of different controllers with the force feedback and be compared to traditional controller designs with the state feedback. From the study, the feasibility of AHSs in flight control will be assessed. The whole study will facilitate in building a fly-by-feel simulation environment for autonomous vehicles.
In performing an effective structural analysis for wind turbine, the simulation of turbine aerodynamic loads is of great importance. The interaction between the wake flow and the blades may impact turbine blades loading condition, energy yield and operational behavior. Direct experimental measurement of wind flow field and wind profiles around wind turbines is very helpful to support the wind turbine design. However, with the growth of the size of wind turbines for higher energy output, it is not convenient to obtain all the desired data in wind-tunnel and field tests. In this paper, firstly the modeling of dynamic responses of large-span wind turbine blades will consider nonlinear aeroelastic effects. A strain-based geometrically nonlinear beam formulation will be used for the basic structural dynamic modeling, which will be coupled with unsteady aerodynamic equations and rigid-body rotations of the rotor. Full wind turbines can be modeled by using the multi-connected beams. Then, a hybrid simulation experimental framework is proposed to potentially address this issue. The aerodynamic-dominant components, such as the turbine blades and rotor, are simulated as numerical components using the nonlinear aeroelastic model; while the turbine tower, where the collapse of failure may occur under high level of wind load, is simulated separately as the physical component. With the proposed framework, dynamic behavior of NREL’s 5MW wind turbine blades will be studied and correlated with available numerical data. The current work will be the basis of the authors’ further studies on flow control and hazard mitigation on wind turbine blades and towers.