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Static axial induction control and tilt control are two strategies that have the potential to increase power production in wind farms, mitigating wake effects and increasing the available power for downstream turbines. In this study, wind tunnel experiments are performed to evaluate the efficiency of these two techniques. First, the axial induction of upstream turbines in wind farms comprising two, three, and five turbines is modified through the tip-speed ratio. This strategy is found to be ineffective in increasing power extraction. Next, the power extraction and flow through a two-turbine wind farm are evaluated, considering different tilt angles for the upstream turbine, under two levels of incoming flow turbulence intensities and turbine spacing distances. It is shown that forward tilting increases the overall power extraction by deflecting the wake downwards and promoting the entrainment of high-speed fluid in the upper shear layer, regardless of the turbine spacing distance and turbulence intensity level. Also, the wake is seen to recover faster due to the increased shear between the wake and the outer flow. Tilting a turbine backward deflects the wake upwards and pulls low-speed flow from under the turbine into the wake space, increasing the available power for downstream turbines, but it is not enough to increase global power extraction. Moreover, since the wake deflection under backward tilting is not limited by ground blockage, it leads to larger secondary steering compared with forward tilting. Finally, it is demonstrated that the secondary steering of the downstream turbine's wake influences the flow encountered by a turbine positioned farther downstream. [ABSTRACT FROM AUTHOR]

Recently, wind turbine research has switched focus to vertical axis wind turbines due to the extensive research that has been performed on horizontal axis wind turbines and the potential of vertical axis wind turbines in built-up areas. This study aims to analyse the performance of a small-scale hybrid vertical axis wind turbine that can switch from functioning as a Darrieus (lift) turbine to a Savonius (drag) turbine by rotating the blades. The turbine was analysed using 3D computational fluid dynamics (CFD) simulations in ANSYS Fluent as the primary method, and the findings were verified using wind tunnel experiments. During the analysis, design parameters such as the blade length, diameter, and number of blades were varied to determine if the design had room for improvement. It was found that the current design of the turbine has an optimal efficiency of 12.5% in the Darrieus configuration, which was found to increase when the diameter or blade length was increased. The Savonius configuration was found to be more efficient at low tip-speed ratios (<0.14), and its efficiency could be increased by adding more blades. The experiments found similar trends to the simulations; however, the efficiencies obtained were on average a tenfold increase from the simulation. Implementing the changes that increased efficiency leads to an increased wake recovery distance, making it less suitable for use in a wind farm. [ABSTRACT FROM AUTHOR]

Data driven approaches are utilized for optimal sensor placement as well as for velocity prediction of wind turbine wakes. In this work, several methods are investigated for suitability in the clustering analysis and for predicting the time history of the flow field. The studies start by applying a proper orthogonal decomposition (POD) technique to extract the dynamics of the flow. This is followed by evaluations of different hyperparameters of the clustering and machine learning algorithms as well as their impacts on the prediction accuracy. Two test cases are considered: (1) the wake of a cylinder and (2) the wake of a rotating wind turbine rotor exposed to complex flow conditions. The training and test data for both cases are obtained from high fidelity CFD approaches. The studies reveal that the combination of a classification-based machine learning algorithm for optimal sensor placement and Bi-LSTM is sufficient for predicting periodic signals, but a more advanced technique is required for the highly complex data of the turbine near wake. This is done by exploiting the dynamics of the wake from the set of POD modes for flow field reconstruction. A satisfactory accuracy is achieved for an appropriately chosen prediction horizon of the Bi-LSTM networks. The obtained results show that data-driven approaches for wind turbine wake prediction can offer an alternative to conventional prediction approaches. [ABSTRACT FROM AUTHOR]

Fast and effective numerical models describing the effect of platform motion on the performance of floating offshore wind turbines (FOWTs) are fundamental to assess energy harvesting potential in large offshore wind farms. The purpose of this paper is to implement a CFD-based Computationally-Efficient approach based on an actuator line model (ALM) for FOWTs aerodynamics. Such a tool aims at complementing reasonable accuracy and affordable computational effort while being able to investigate the effects of the platform motions on the wake evolution. The actuator line model for FOWTs is developed by implementing a dedicated C++ library in the OpenFOAM toolbox. In addition, a tip treatment is applied to involve the tip effects induced by the pressure equalization from the suction and pressure sides. Results show that employing ALM decreases computational cost and preprocessing time for producing appropriate computational grids, as just about 400k and 600k grids are necessary for solving two representative test cases of fixed-bottom turbines (NREL Phase VI and NREL 5-MW) with reasonable accuracy. The inclusion of platform motion is then introduced, and the results showed that ALM is capable of capturing vortices trajectory, potential blade-vortex interactions, and vortex pairing and vortex ring state phenomenon in FOWTs. [ABSTRACT FROM AUTHOR]

Summary: The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested. [ABSTRACT FROM AUTHOR]

Hydrokinetic turbines, which convert the kinetic energy of flowing water into electrical energy, are increasingly being deployed in arrays, much like multiple wind turbines are commonly organized into a wind farm. Deploying multiple devices in an array can increase the amount of energy produced from a site and tends to make project economics more favourable. Despite growing interest in hydrokinetic turbine arrays, relatively few researchers have studied arrays and limited information exists concerning the character of the turbulent flows within arrays and methods for optimizing array layout, particularly for cross-flow turbines. This paper presents results from a set of experiments conducted in a current flume in which the wakes produced by a single cross-flow turbine and pairs of identical cross-flow turbines were measured in detail. Two different ambient turbulence intensities were studied so that the effect of turbulence level on wake recovery could be quantified. The velocity data has been analyzed to identify the mean velocity and the turbulence intensity in the turbine wake(s). These experiments form one component of a larger study that aims to develop numerical methods and guidelines to help project developers optimize the design of turbine arrays. [ABSTRACT FROM AUTHOR]

The increased turbulence behind the turbine induces a fatigue load on the downstream turbine blades. An accurate estimate of turbulent intensity in turbine wake is paramount to optimizing the placement of turbines in tidal turbine parks. A simple empirical model is developed using data fitting from numerical simulation of a non-rotational actuator disc model to estimate the added turbulence in realistic tidal stream conditions similar to the Alderney Race, with a rotor diameter to depth ratio of 40%. The study shows a self-similar Gaussian shape streamwise turbulent intensity in a lateral direction similar to the velocity deficit profile. The turbulent wake radius expands according to a power-law depending on the ambient turbulent intensity. The added turbulence model justifies that the major turbulence source in the near wake is attributed to the rotor as it is weakly dependent on the ambient turbulence in the flow. As no known existing empirical model for tidal turbine wake added turbulence, the model is compared with existing models of unbounded wind turbines. The proposed model estimates the average turbulence in the far wake can assist turbine placement in a tidal farm. • Turbulent wake expands according to power law and depends on the turbulent intensity. • High turbulent intensity facilitates faster recovery to ambient condition. • Added turbulence is weakly affected by the ambient turbulence in the flow. • The rotor is the major source of turbulence in the near wake. • The proposed model estimates the average turbulence in the far wake. [ABSTRACT FROM AUTHOR]

Wind energy has become one of the most commercially prospective renewable energies. However, the wake effect of wind turbine can reduce the power generation efficiency and increase the fatigue loading of downstream turbines. Hence, the wake effect study has attracted increasing interests. Compared with the extensive study on the single turbine wake, that on the superposition effect of multiple turbine (multi-turbine) wakes is limited. In this study, the characteristics of the wake velocity and turbulence intensity are studied and the exponential superposition model is proposed for the aligned multi-turbine wakes. Firstly, Simulator for Offshore Wind Farm Applications (SOWFA), a high-fidelity simulator for the interaction between wind turbine dynamics and the flow in a wind farm, is used to analyze the distribution of aligned multi-turbine wakes. It is observed that wakes reach the steady state from second turbine in the aligned turbines. Then influences of different factors on the accuracy of existing superposition models are studied. It is found spacing has significant effect on the performance of superposition models. Furthermore, the exponential superposition model with higher applicability is proposed for the wake velocity and turbulence intensity. Finally, this model is validated by the benchmark data of real wind farms. • Characteristics of the wake velocity and turbulence intensity of aligned multiple turbines are addressed. • Influences of different factors on the accuracy of existing superposition models are addressed. • Exponential superposition model is proposed for the wake velocity and turbulence intensity. • Validations in real wind farms show exponential superposition model has good performance. [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy resources has promoted the construction of offshore wind farms e.g. in the North Sea. While the wind farm layout consists of an array of large turbines, the interrelation of wind turbine wakes with the remaining array is of substantial interest. The downstream spatial evolution of turbulent wind turbine wakes is very complex and depends on manifold parameters such as wind speed, wind direction and ambient atmospheric stability conditions. To complement and validate existing numerical models, corresponding observations are needed. While in-situ measurements with e.g. anemometers provide a time-series at the given location, the merits of ground-based and space- or airborne remote sensing techniques are indisputable in terms of spatial coverage. Active microwave devices, such as Scatterometer and Synthetic Aperture Radar (SAR), have proven their capabilities of providing sea surface wind measurements and particularly SAR images reveal wind variations at a high spatial resolution while retaining the large coverage area. Platform-based Doppler LiDAR can resolve wind fields with a high spatial coverage and repetition rates of seconds to minutes. In order to study the capabilities of both methods for the investigation of small scale wind field structures, we present a direct comparison of observations obtained by high resolution TerraSAR-X (TS-X) X-band SAR data and platform-based LiDAR devices at the North Sea wind farm alpha ventus. We furthermore compare the results with meteorological data from the COSMO-DE model run by the German Weather Service DWD. Our study indicates that the overall agreement between SAR and LiDAR wind fields is good and that under appropriate conditions small scale wind field variations compare significantly well. [ABSTRACT FROM AUTHOR]

Simulations of wind turbine wakes are presented in this paper using the three-dimensional Reynolds- Averaged Navier-Stokes (RANS) equations employing the k-ε turbulence model appropriately modified for the atmospheric flows. The actuator disk approach is used to model the action of the turbine rotor. Modified formulations of the inlet conditions and the wall functions are used to allow consistency between the fully developed atmospheric boundary layer (ABL) inlet profiles and the wall function formulation. Results are presented and compared with three wind turbines running under neutral atmospheric conditions. The results demonstrate that the accurate simulation of the atmospheric boundary layer applying enhanced inlet conditions and wall function formulation consistent with the k-ε model can give very useful information about the wakes, directly contributing to the accurate estimation of the power of the downstream turbines. [ABSTRACT FROM AUTHOR]

Abstract: In order to develop applications for micro-wind turbines, an experimental analysis of the flow field around integrated micro-wind turbines was performed. The wake flows of a single turbine and 5×5 array unit were measured by using hot-wire and ultrasonic anemometers and particle image velocimetry (PIV). The present array of turbines follows a fundamental lattice layout; however, it has the flexibility to optimize its layout according to the environmental conditions. hot-wire and ultrasonic anemometers and PIV measurements were used for stand-alone turbines and their integrated systems. Comparisons between the mean velocity field and turbulent intensity were described for stand-alone full-scale and 1/10-scale wind turbine models. Thereafter, a typical array of the 1/10-scale model was assumed and its wake flow was investigated in a wind tunnel. The velocity profile and turbulence behind the array were measured and studied at different streamwise locations. The scale effect and model similarities were discussed. The experimental results show that a zone exists with constant and linear wake deficit ratios in the downstream regions. [Copyright &y& Elsevier]

• Super-large-scale flow imaging of utility-scale wind turbine near wake behavior. • Dynamic wake modulation occurs under fluctuating inflow and turbine operation. • Dynamic wake modulation can lead to significantly faster wake recovery. • Readily-available turbine parameters can predict dynamic wake modulation. • Guidelines for improving turbine wake and farm models proposed. Understanding wind turbine wake mixing and recovery is critical for improving the power generation and structural stability of downwind turbines in a wind farm. In the field, where incoming flow and turbine operation are constantly changing, the rate of wake recovery can be significantly influenced by dynamic wake modulation, which has not yet been explored. Here we present the first investigation of dynamic wake modulation in the near wake of an operational utility-scale wind turbine, and quantify its relationship with changing conditions. This experimental investigation is enabled using novel super-large-scale flow visualization with natural snowfall, providing unprecedented spatiotemporal resolution to resolve instantaneous changes of the wake envelope in the field. These measurements reveal the significant influence of dynamic wake modulation, which causes an increase in flux across the wake boundary of 11% on average, on wake recovery, providing insights into necessary modifications to traditional wake and farm models. Further, our study uncovers the direct connection between dynamic wake modulation and operational parameters readily available to the turbine controller such as yaw error, blade pitch, and tip speed ratio. These connections pave the way for more precise wake prediction and control algorithms under field conditions for wind farm optimization. [ABSTRACT FROM AUTHOR]

It is well known that a wake will develop downstream of a tidal stream turbine owing to extraction of axial momentum across the rotor plane. To select a suitable layout for an array of horizontal axis tidal stream turbines, it is important to understand the extent and structure of the wakes of each turbine. Studies of wind turbines and isolated tidal stream turbines have shown that the velocity reduction in the wake of a single device is a function of the rotor operating state (specifically thrust), and that the rate of recovery of wake velocity is dependent on mixing between the wake and the surrounding flow. For an unbounded flow, the velocity of the surrounding flow is similar to that of the incident flow. However, the velocity of the surrounding flow will be increased by the presence of bounding surfaces formed by the bed and free surface, and by the wake of adjacent devices. This paper presents the results of an experimental study investigating the influence of such bounding surfaces on the structure of the wake of tidal stream turbines. [ABSTRACT FROM AUTHOR]