Bulletin of the American Physical Society
72nd Annual Meeting of the APS Division of Fluid Dynamics
Volume 64, Number 13
Saturday–Tuesday, November 23–26, 2019; Seattle, Washington
Session Q09: Wind Turbines: Wakes |
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Chair: Philippe Chatelain, Université catholique de Louvain Room: 213 |
Tuesday, November 26, 2019 7:45AM - 7:58AM |
Q09.00001: Tilt-induced wind turbine wake shape characterization Juliaan Bossuyt, Naseem Ali, Ryan Scott, Raul Bayoan Cal The wakes of yaw-misaligned wind turbines have been shown to deflect downstream by the formation of a counter-rotating vortex pair, resulting in wake curling. Intentional misalignment of wind turbines thus allows for wake steering and can be successfully used for wind farm control and overall power optimization. Floating wind turbines operate in a dynamic state of misalignment because of surge, heave, yaw or tilt motions from wave and wind dynamics. In this work, wake deflection from static tilt is quantified in a scaled wind tunnel experiment to verify the importance of such turbine misalignments in floating wind farms. Cross-plane stereo particle image velocimetry measurements are performed for a scaled wind turbine rotor model, in a sheared boundary layer flow. Wake deflection caused by a counter-rotating vortex pair in the wake of tilted wind turbines is observed. In a sheared boundary layer flow, and with the presence of ground obstruction, a non-symmetrical behavior is observed, for positive and negative tilt, resulting in either a flying or crashing wake. [Preview Abstract] |
Tuesday, November 26, 2019 7:58AM - 8:11AM |
Q09.00002: Field experiment of wind farm power optimization through wake steering Michael Howland, Sanjiva Lele, John Dabiri Due to greedy individual wind turbine operation, aerodynamic wakes reduce total wind farm power production, thereby increasing the cost of electricity for this resource. Considering the wind farm as a collective, we designed a wake steering control method to increase the power production of wind farms. The method was tested in a multi-turbine array at an operational wind farm where it statistically significantly increased the power production. The analytic gradient-based wind farm power optimization methodology developed can optimize the yaw misalignment angles for large wind farms on the order of seconds. [Preview Abstract] |
Tuesday, November 26, 2019 8:11AM - 8:24AM |
Q09.00003: Development of wake centerline detection algorithms for the study of wind turbine wake meandering Nicolas Coudou, Laurent Bricteux, Jeroen van Beeck, Philippe Chatelain The low-frequency oscillatory motion of wind turbine wakes, also known as wake meandering, is crucial in wind farms as it increases fatigue loads on downstream turbines. The study of this phenomenon requires, as a first step, the determination of the position of the wake. The wake centroid tracking proposed in this work is based on the computation of the wind ipower inside a disk of a diameter equal to the rotor diameter and shifted in a cross-flow plane. The wake center corresponds to the disk position for which the available power is minimum. Mathematically, it consists in locating the maximum of the convolution between the power available in the flow and a masking function. The wake centerline can then be obtained by repeating this technique in each cross-flow plane in the wake of a machine. This method being sensitive to strong local minima of wind speed occurring for high level of inflow turbulence, an improved method based on a 3D convolution is proposed in this work. These techniques are applied to the data obtained from Large-Eddy simulations of the NREL 5-MW wind turbine subjected to synthetic turbulent inflows. The computations were performed with a vortex-particle mesh code, the presence of the wind turbine rotor being accounted for through immersed lifting lines. [Preview Abstract] |
Tuesday, November 26, 2019 8:24AM - 8:37AM |
Q09.00004: Horizontal Axis Turbine Wake Measurements Using Unmanned Aerial Vehicles Stewart Nelson, Christopher Heintz, Luke Norman, Rupp Carriveau, Sean Bailey We use up to four highly instrumented, semi-autonomous unmanned aerial vehicles (UAVs) to measure the wakes shed by operational horizontal axis wind turbines during a one-week measurement campaign conducted at a line of four turbines located in a wind farm in Southern Ontario, Canada. During this campaign we investigated both the evolution of the wakes during a morning boundary layer evolution, and under the effects of wake steering. For the wake steering experiments, one turbine was yawed by 30 degrees with respect to the mean wind. In this talk, we will present results from the UAVs, which acquired horizontal wind velocity profiles across the turbine wakes at hub height, and with 0.3 m horizontal resolution. We also relate these wake measurements with Supervisory Control and Data Acquisition (SCADA) and tower strain data acquired concurrently to the measurements. [Preview Abstract] |
Tuesday, November 26, 2019 8:37AM - 8:50AM |
Q09.00005: Experimental study on the effect of turbulence properties on model wind turbine performance Stefano Gambuzza, Bharathram Ganapathisubramani The effects of turbulence on wind turbine performance (thrust and power generation) are often assumed based on analytical considerations, and studies focusing on actual measurements or computations are scarce. For this reason, a measurement campaign was carried out to parametrically study the effect of characteristics such as turbulence intensity and integral scale on the thrust and power generated by a model-scale wind turbine. An active grid has been used to systematically vary the properties of the incoming turbulent flows, generating integral length scales ranging from one order of magnitude smaller to slightly larger than the turbine diameter. Likewise, it has been possible to vary the free-stream turbulence intensity in the range of 0.5\% to more than 12\%. The model is a speed-controlled wind turbine driven by the incoming flow, moving a permanent magnet DC generator used to actively control the turbine speed and to measure the torque generated by the turbine rotor. The forces on the turbine are measured with a load cell force balance. Rotor geometries with a NACA 63 series aerofoil and different diameters (150, 180 and 200 mm) are tested. Presented data will focus on the variation in power and thrust coefficients with the turbulence properties of the incoming flow. [Preview Abstract] |
Tuesday, November 26, 2019 8:50AM - 9:03AM |
Q09.00006: Kinematic Shear Stress Budget and Relaxation Time-Scales in a Spatially Heterogeneous Canopy Turbulence: an Application to Finite Sized Wind Farms Tirtha Banerjee, Tanmoy Chatterjee In this talk, we investigate the kinematic shear stress budget of a highly heterogeneous finite-sized (3 X 3) wind farm, driven by neutrally-stratified atmospheric boundary layer. The study is performed with a spectral element LES code with a near-wall modeling framework and an actuator line model to represent the effect of rotating wind turbine blades. We observe, that the main imbalance of the horizontally averaged kinematic shear stress budget occurs at and around the wind turbine wake-regions, which imposes strong heterogeneity to the flow system. The goal of the present study is to develop a model of this imbalance term. This is primarily theorized and investigated by modeling the relaxation time-scales of Rotta-model for the pressure velocity correlations in the kinematic shear stress budget. This study will not only help us towards better physics-based insight for the failure of K-Theory in heterogeneous atmospheric flows in wind farms, the generalization of Rotta-models in Reynolds-stress modeling based turbulence closures of heterogeneous flows in numerical algorithms but also serve as a stepping stone toward the overarching goal- to comprehend the role of heterogeneity and wake influence in the parametrization of effects in wind farms. [Preview Abstract] |
Tuesday, November 26, 2019 9:03AM - 9:16AM |
Q09.00007: Effects of Freestream Turbulence on Wind Turbine Wakes Alexander Pique, Mark A. Miller, Marcus Hultmark The wake of a model-scale, horizontal-axis wind turbine was investigated at $3\times 10^6 |
Tuesday, November 26, 2019 9:16AM - 9:29AM |
Q09.00008: An Entrainment-Based Wake Model for Airborne Wind Energy and Annular Wind Turbines Sam Kaufman-Martin, Nicholas Naclerio, Pedro May, Paolo Luzzatto-Fegiz Several novel wind energy systems have annular wakes instead of the disc-shaped wakes common to conventional horizontal-axis wind turbines (HAWT). Systems with annular wakes include airborne wind energy (AWE) devices, which harvest power from tethered kites flying in a circular path (such as the Makani energy kite), as well as rim-drive wind turbines. Since wind farms use arrays of hundreds of turbines, good analytical wake models are essential for efficient wind farm planning. Several models already exist for HAWTs, such as the Park, Frandsen, and Entrainment models. However, none have yet been proposed for turbines with annular wakes, making it impossible to estimate their array performance. We use the entrainment hypothesis to develop an analytical model for the shape and flow velocity of an annular wake. Our model is in good agreement with Large Eddy Simulation results from Haas \& Meyers ({\it J. Phys.: Conf. Ser.} 2017), especially in the far wake region, and enables the prediction of AWE kite array performance. [Preview Abstract] |
Tuesday, November 26, 2019 9:29AM - 9:42AM |
Q09.00009: Identification of clusters of turbines in waked conditions through SCADA data Federico Bernardoni, Umberto Ciri, Mario Rotea, Stefano Leonardi Control algorithms seeking to maximize the power production of wind farms assume that clusters of turbines in waked conditions are known. However, the wind changes direction and the clusters within the array vary continuously. In a practical application, one would need to identify the clusters in real time and then control the turbines with a coordinated approach. Identifying the clusters may be challenging for several reasons such as absence of a meteorological tower, misalignment between the turbines and the actual wind direction or flow variability due to topography or meso-scale coherent structures. In this contribution we propose a technique to identify clusters of turbines correlating power production and angular speed of the turbines. The technique is demonstrated in an ideal 4x4 wind turbine array. Results from large-eddy simulations with rotating actuator disk are used to mimic SCADA data and assess the proposed algorithm under different wind directions. The goal is to provide a tool to be integrated in the control system of operating and future wind farms. [Preview Abstract] |
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