Wind energy research at RETEG focuses on the complex interactions between wind turbines and the turbulent atmosphere. This interplay is characterized by turbine wake flows and their interaction with the turbulent atmospheric boundary layer, as well as the effects of turbulence on the performance of wind turbines.
Figure 1: (a) Miniature wind turbines in the Talbot Laboratory wind tunnel. (b) Downwind view of the wind tunnel test section.
Figure 2: (a) Photograph of the small wind turbine and an array of CSAT3 sonic anemometers. (b) Close look of the array at sonic anemometers facing upwind.
An experimental investigation into the applicability of turbines with winglets in a wind farm has recently been performed and published in the journal Energies. Previous works on using winglets to increase power production from wind turbines has focused entirely on producing as much power as possible for a single turbine. The purpose of this work was to quantify how a wingletted turbine would affect overall wind farm dynamics and power production. Although winglets increase power production in a single turbine, they also increase the thrust force exerted, leading to higher wake deficits, which could impact power production of a downstream turbine. It was found that certain inter-turbine spacings will lead to a net reduction in power production when using winglets. Figure 3 shows the wake deficits of a normal and a wingletted turbine.
Figure 3: Comparison of the mean wake deficit profiles at the central plane of the standard and wingletted turbine models at several downstream locations. (Top) General setup; (bottom) 1D velocity deficit profiles. Horizontal dash-dot lines indicate the location of the bottom and top tips of the turbine. Vertical solid lines indicate local zeros, and grid squares have a width of 0.1×Uhub.
A new model for the structure of power fluctuations in real and spectral spaces has been developed, and published in the Journal of Turbulence. It considers both the flow field and the effect of rotor inertia and has been validated with experimental data across scales. This model has the potential to be used for advanced control schemes utilizing e.g., nose cone LiDAR systems to reduce dynamic loading of turbine components. Example power predictions for a single 1 kW turbine are shown in Figure 4.
Figure 4: Model predictions of turbine power for a 1 kW wind turbine with measured power values as well as naive estimate.
Our research group aims at providing fundamental insights on the role of turbulence in basic and applied problems of high interest, which can be divided in the following sub-areas:
i) structure of the boundary layer over complex topographies;
ii) wind & hydrokinetic energy technologies,
iii) scalar transport over urban and natural environments,
iv) flow-structure interaction; and
v) instrumentation for turbulence measurements.
We have developed a comprehensive research on these topics that are going to be sustained and expanded in the future. Our versatile experimental approach combines a set of state-of-the-art experimental techniques, including particle image velocimetry (PIV), computer vision, and our recently developed 3D particle tracking velocimetry (PTV). This framework allows us to study fluid dynamics from Eulerian and Lagrangian frame of references