Sinusoidal-gust generation with a pitching and plunging airfoil

The generation of uniform, periodic gust disturbances in an experimental context is demonstrated using a single oscillating airfoil. A pitching and heaving symmetric airfoil is suggested as a simpler alternative to existing gust-generation methods. The Theodorsen theory of unsteady aerodynamics is used as an analytical tool to dictate the kinematics necessary to produce well-defined sinusoidal gusts downstream of the airfoil. These analytic predictions improve the symmetry of fluctuations in the vertical velocity induced by the airfoil, as well as minimize the influence of vorticity shed by the oscillating airfoil. The apparatus is shown to produce smooth, repeatable gusts with high amplitudes and reduced frequencies compared to other gust-generation mechanisms in the literature. Furthermore, the control of downstream flow properties by airfoil motion kinematics has applications in experimental aerodynamics, the design of rotorcraft and light aerial vehicles, and biological propulsion.Comment: Under revie

Control of long-range correlations in turbulence

The character of turbulence depends on where it develops. Turbulence near boundaries, for instance, is different than in a free stream. To elucidate the differences between flows, it is instructive to vary the structure of turbulence systematically, but there are few ways of stirring turbulence that make this possible. In other words, an experiment typically examines either a boundary layer or a free stream, say, and the structure of the turbulence is fixed by the geometry of the experiment. We introduce a new active grid with many more degrees of freedom than previous active grids. The additional degrees of freedom make it possible to control various properties of the turbulence. We show how long-range correlations in the turbulent velocity fluctuations can be shaped by changing the way the active grid moves. Specifically, we show how not only the correlation length but also the detailed shape of the correlation function depends on the correlations imposed in the motions of the grid. Until now, large-scale structure had not been adjustable in experiments. This new capability makes possible new systematic investigations into turbulence dissipation and dispersion, for example, and perhaps in flows that mimic features of boundary layers, free streams, and flows of intermediate character.Comment: This paper has been accepted to Experiments in Fluids. 25 pages, 10 figure

On the Lift of an Oscillating Airfoil Encountering Periodic Gust Disturbances

Rising interest in the use of small aerial vehicles and the feasibility of deploying highly flexible structures require an understanding of how these system may behave when they encounter gusts. In this work, we consider the problem of a pitching and plunging airfoil in a periodic transverse gust, and seek to understand the extent to which theoretical predictions of these unsteady effects match numerical simulations. A potential-flow model derived from a linear combination of the canonical Sears and Theodorsen problems is proposed to capture the unsteady lift on a thin two-dimensional airfoil in the small-perturbation limit. Using 2D large-eddy simulations, we study the performance of a NACA-0012 airfoil across a broad range of pitch, plunge, and gust amplitudes and frequencies, and quantify the amplitude and phase of the unsteady lift. Good agreement with the model predictions is found even at relatively high reduced frequencies, while minor deviations are observed when the angle-of-attack amplitudes approach the static flow-separation regime of the airfoil. Implications for model improvement and extensions are discussed for the cases in which the ideal-flow theory proves insufficient. In sum, the theoretical framework and numerical validation provide predictive capabilities for applications such as gust-load alleviation for increased robustness against fatigue and the optimization of flapping flight in gusty environments for enhanced maneuverability and control authority

Power-generation enhancements and upstream flow properties of turbines in unsteady inflow conditions

Energy-harvesting systems in complex flow environments, such as floating offshore wind turbines, tidal turbines, and ground-fixed turbines in axial gusts, encounter unsteady streamwise flow conditions that affect their power generation and structural loads. In some cases, enhancements in time-averaged power generation above the steady-flow operating point are observed. To characterize these dynamics, a nonlinear dynamical model for the rotation rate and power extraction of a periodically surging turbine is derived and connected to two potential-flow representations of the induction zone upstream of the turbine. The model predictions for the time-averaged power extraction of the turbine and the upstream flow velocity and pressure are compared against data from experiments conducted with a surging-turbine apparatus in an open-circuit wind tunnel at a diameter-based Reynolds number of $Re_D = 6.3\times10^5$ and surge-velocity amplitudes of up to 24% of the wind speed. The combined modeling approach captures trends in both the time-averaged power extraction and the fluctuations in upstream flow quantities, while relying only on data from steady-flow measurements. The sensitivity of the observed increases in time-averaged power to steady-flow turbine characteristics is established, thus clarifying the conditions under which these enhancements are possible. Finally, the influence of unsteady fluid mechanics on time-averaged power extraction is explored analytically. The theoretical framework and experimental validation provide a cohesive modeling approach that can drive the design, control, and optimization of turbines in unsteady flow conditions, as well as inform the development of novel energy-harvesting systems that can leverage unsteady flows for large increases in power-generation capacities.Comment: 36 pages, 19 figures. Currently under revie

Automating the assessment of biofouling in images using expert agreement as a gold standard

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-sample subset of these images, and found that they showed 89% agreement (95% CI: 87-92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% worse (p=0.009-0.054). Using these expert labels, we were able to train a deep learning model that also agreed similarly with the group of experts (p=0.001-0.014), demonstrating that automated analysis of biofouling in images is feasible and effective using this method.Comment: 12 page