Turbine Wake Research Articles

Multicluster Distributed Optimization Strategy for Turbine Wake Environment

As a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely impacts the power generation efficiency of wind farms. Herein, a multicluster distributed optimization strategy based on wake‐DBSCAN, specifically designed for environments affected by wake interference is proposed. First, clustering analysis on the wind turbine layout and wind conditions to establish a foundation for efficient distributed computation is performed. Based on the clustering results, wake analysis is conducted to plan and optimize the operational strategy for each wind turbine cluster, resulting in a distributed optimization strategy for their operation. Additionally, simulation experiments are conducted on micrositing and time‐varying wind conditions using real‐world data from a wind farm in the Arua region. The experimental results demonstrate that the proposed algorithm can effectively improve the computational efficiency of wake optimization, while ensuring the effect of the wake optimization algorithm in actual wind farms as much as possible, which is more in line with the wake optimization needs of actual wind farms. The algorithm proposed in this article provides valuable insights into wind turbine operation and maintenance under time‐varying conditions.

Study on wind turbine wake effect and analytical model in hilly terrain

Study on wind turbine wake effect and analytical model in hilly terrain

Scaling analysis of the swirling wake of a porous disc: application to wind turbines

We report a comprehensive study of the wake of a porous disc, the design of which has been modified to incorporate a swirling motion at an inexpensive cost. The swirl intensity is passively controlled by varying the internal disc geometry, i.e. the pitch angle of the blades. A swirl number is introduced to characterise the competition between the linear (drag) and the azimuthal (swirl) momenta on the wake recovery. Assuming that swirl dominates the near wake and non-equilibrium turbulence theory applies, new scaling laws of the mean wake properties are derived. To assess these theoretical predictions, an in-depth analysis of the aerodynamics of these original porous discs has been conducted experimentally. It is found that, at the early stage of wake recovery, the swirling motion induces a low-pressure core, which controls the mean velocity deficit properties and the onset of self-similarity. The measurements collected in the swirling wake of the porous discs support the new scaling laws proposed in this work. Finally, it is shown that, as far as swirl is injected in the wake, the characteristics of the mean velocity deficit profiles match very well those of both laboratory-scale and real-scale wind-turbine data extracted from the literature. Overall, our results emphasise that, by setting the initial conditions of the wake recovery, swirl is a key ingredient to be taken into account in order to faithfully replicate the mean wake of wind turbines.

A double-Gaussian wake model considering yaw misalignment

A wake steering has been known to effectively increase wind farm production by deflecting the upstream turbines’ wakes via yaw misalignment, thus minimizing their negative impacts on the downstream turbines' performances. This study presents analytical modeling of horizontal-axis wind turbine (HAWT) wake using low-cost analytical modeling as an alternative to expensive numerical and experimental trials. The existing double-Gaussian (DG) analytical wake model was modified to include the yaw misalignment effect, allowing its usability for the yawed HAWT wake modeling. The benchmark dataset produced by high-fidelity large eddy simulation (LES) of wake flowfields behind the turbine with yaw angles of 0º, 10º, 20º, and 30º were used to validate the accuracy of the DG yaw wake model. Overall, the DG yaw wake model predictions showed good agreement with the benchmark dataset under varying HAWT rotor yaw configurations. The analytical results verified by the LES dataset confirm the effectiveness of yaw misalignment in deflecting the wake trajectory, expediting the wake recovery downstream of the HAWT. In addition, a higher rotor yaw angle improves the wake recovery rate in the prevailing wind direction. Notable deviations against the benchmark dataset were found mainly within the near-wake region owing to flow acceleration arising from turbine-induced turbulence. As a result, the model’s predictions were slightly lower than the benchmark dataset, most likely due to neglecting the acceleration term in the analytical model derivation. Otherwise, the analytical model could accurately predict the mean wake velocity within the far-wake region for all evaluated cases, demonstrating its reliability in estimating wind speed potential within a practical distance for micrositing. These results were also proved quantitatively by statistical evaluations utilizing root mean square error (RMSE) and Pearson correlation coefficient R. The present study points out the importance of the upstream HAWTs’ rotor yaw controls to properly deflect their wakes away from their mainstream trajectories, thus effectively maximizing the wind speed potentials extracted by the downstream HAWTs and improving the overall wind farm production.

The effect of Coriolis force on the coherent structures in the wake of a 5MW wind turbine

The effect of Coriolis force on the coherent structures in the wake of a 5MW wind turbine

The effects of wingtip modifications on the wake of horizontal axis wind turbines

We study the impact of a novel wingtip modification on the wake dynamics of a 10-MW horizontal axis wind turbine using delayed detached eddy simulation. We considered a baseline turbine without wingtip modifications and a turbine equipped with winglets. The results reveal that the winglet significantly alters the near and mid wake regions, increasing the velocity deficit and reducing turbulence intensity in the near wake while minimally affecting the far wake beyond the distance of nine rotor diameters. The vortex rings from the blade tips decay faster in the wake of the modified turbine, reducing the wake energy and vorticity. The budget of mean kinetic energy transport shows that the winglet reduces the turbulence production in the near wake while increasing the turbulence convection and production in the far wake region. To study the meandering in the far wake of two turbine configurations, the dominant Strouhal number and the standard deviation of the wake center were studied. The results indicated that the winglet does not notably affect the amplitude of the wake meandering. Furthermore, the winglet increased turbine power production by 4.5% and thrust by 1.5% while reducing power and torque fluctuations by 10%. Although the winglet affected near wake dynamics, its influence on the far wake is minimal, suggesting potential benefits for wind farm design where turbines are not closely spaced.

Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations

AbstractImproving the power output from wind farms is vital in transitioning to renewable electricity generation. However, in wind farms, wind turbines often operate in the wake of other turbines, leading to a reduction in the wind speed and the resulting power output whilst also increasing fatigue. By using wake steering strategies to control the wake behind each turbine, the total wind farm power output can be increased. To find optimal yaw configurations, typically analytical wake models have been utilised to model the interactions between the wind turbines through the flow field. In this work we show that, for full wind farms, higher-fidelity computational fluid dynamics simulations, in the form of large eddy simulations, are able to find more optimal yaw configurations than analytical wake models. This is because they capture and exploit more of the physics involved in the interactions between the multiple turbine wakes and the atmospheric boundary layer. As large eddy simulations are much more expensive to run than analytical wake models, a multi-fidelity Bayesian optimisation framework is introduced. This implements a multi-fidelity surrogate model, that is able to capture the non-linear relationship between the analytical wake models and the large eddy simulations, and a multi-fidelity acquisition function to determine the configuration and fidelity of each optimisation iteration. This allows for fewer configurations to be evaluated with the more expensive large eddy simulations than a single-fidelity optimisation, whilst producing comparable optimisation results. The same total wind farm power improvements can then be found for a reduced computational cost.

Investigating Tidal Stream Turbine Array Performance Considering Effects of Number of Turbines, Array Layouts, and Yaw Angles

The performance of a tidal stream turbine array can be affected by numerous factors. Investigating the connection between array power production and these factors will be helpful in improving the development of tidal stream energy. This study investigates the impact of array layout, turbine number, and yaw angles on turbine array performance using an open-source coastal ocean modelling system. The results show that the total power output of the turbine array rises with the number of turbines. Under realistic conditions, there are not many differences in power output between aligned and staggered turbine array configurations. By extending the distance between the turbines, the array power output can be improved in both layouts. It appears that considering each turbine’s yaw angle can improve array power generation, since the downstream turbines will greatly benefit from the steering wake of the upstream turbines. Furthermore, using a gradient-based optimization algorithm to simultaneously adjust the yaw angles and turbine positions will boost the turbine array’s efficiency more than just optimizing the turbine position alone.

Assessing the Effect of Coriolis Acceleration on the Coherent Structures in the Wake of a Wind Turbine Using Dynamic Mode Decomposition

Abstract The present work aims to study the effect of veer, namely, the height-dependent lateral deflection of wind velocity due to Coriolis acceleration, on the coherent structures in the wake of the national renewable energy laboratory 5-MW reference wind turbine using the sparsity-promoting dynamic mode decomposition (SP-DMD) method for the detection of dynamically relevant flow structures. Large eddy simulation (LES) of the flow impacting the wind turbine is carried out by solving the filtered Navier–Stokes equations for incompressible flows. The effects of both tower and nacelle are included in the simulation using the immersed boundary method. Simulations are performed at Re≈108, generating the inlet velocity profile by a precursor simulation of the atmospheric boundary layer (ABL) subjected to Coriolis acceleration. The analysis of the dynamic mode decomposition (DMD) results allows to study the effects of the Coriolis acceleration on the most relevant dynamic modes in the turbine wake and to understand the basic mechanisms by which the wind veer significantly affects the wake recovery rate. Moreover, as a result of the SP-DMD methodology, the most relevant modes are extracted from the wake and a limited subset of relevant flow features that optimally approximates the original data sequence is identified. This small number of modes represent the kernel that can be employed for developing an accurate reduced order model of the wake.

Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains

The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain.

A diffusion-based wind turbine wake model

Describing the evolution of a wind turbine's wake from a top-hat profile near the turbine to a Gaussian profile in the far wake is a central feature of many engineering wake models. Existing approaches, such as super-Gaussian wake models, rely on a set of tuning parameters that are typically obtained from fitting high-fidelity data. In the current study, we present a new engineering wake model that leverages the similarity between the shape of a turbine's wake normal to the streamwise direction and the diffusion of a passive scalar from a disk source. This new wake model provides an analytical expression for a streamwise scaling function that ensures the conservation of linear momentum in the wake region downstream of a turbine. The model also considers the different rates of wake expansion that are known to occur in the near- and far-wake regions. Validation is presented against high-fidelity numerical data and experimental measurements from the literature, confirming a consistent good agreement across a wide range of turbine operating conditions. A comparison is also drawn with several existing engineering wake models, indicating that the diffusion-based model consistently provides more accurate wake predictions. This new unified framework allows for extensions to more complex wake profiles by making adjustments to the diffusion equation. The derivation of the proposed model included the evaluation of analytical solutions to several mathematical integrals that can be useful for other physical applications.

A LES-ALM Study for the Turbulence Characteristics of Wind Turbine Wake Under Different Roughness Lengths

To investigate the characteristics of wind turbine wakes under different aerodynamic roughness lengths, a series of LES-ALM simulations were carried out in this study. First, a sensitivity analysis of the time step of the simulation results was performed. Then, the study compared the power and thrust of wind turbines under different roughness conditions. Finally, the mean velocity deficit, added turbulence intensity, and Reynolds shear stresses in the wake were analyzed under different roughness conditions. This study finds that a 0.1 s time step can provide satisfactory results for the LES-ALM compared to a 0.02 s time step. Furthermore, for the same hub-height wind speed, the thrust coefficient varies from 0.75 to 0.8 under the different roughness levels. As the roughness length increases, the time-averaged velocity deficit and added turbulence intensity decreases, and the wake recovers more quickly at the incoming level. However, the effect of roughness length on the Reynolds shear stress is weak within the downstream range of x = 6D to 10D. For the velocity deficit, a single Gaussian function is not able to describe its vertical distribution. Additionally, under higher roughness conditions, the height of the wake center is distinctively higher than the hub height as the wake develops downstream. The findings of this paper are beneficial for selecting the approximate numerical parameters for the wake simulations and provide deeper insights into the turbulence mechanisms of wind turbine wake, which are crucial for establishing analytical models to predict the wake field.

Influence of thrust coefficient on the wake of a wind turbine: A numerical and analytical study

Influence of thrust coefficient on the wake of a wind turbine: A numerical and analytical study

Wake effect on floating offshore wind turbine fatigue load

Wind turbines submerged in the upstream wind turbine (UWT) wake lead to reduced power production and increased fatigue loads. However, current wind farm layout optimization processes prioritize power production without addressing fatigue load due to the complexities associated with fatigue load assessment within the UWT wake. In this study, the impacts of wake-turbine overlap on the fatigue load for floating offshore wind turbines are investigated. A large-eddy simulation is used to generate the UWT wake, followed by extensive aeroelastic simulations to meticulously examine the wake-turbine overlap effect over the wake domain. The blade fatigue load results reveal significant influences on the degree of wake-turbine overlap. Blade fatigue load correlates with wake deficit and turbulence, exhibiting a bimodal distribution in the lateral direction with peaks at approximately 0.5 turbine diameter (D) offset while diminishing in the streamwise direction. Despite the complete recovery of power production at 1D lateral offset, a significant fatigue load persists. Furthermore, tower fatigue load and platform motions are notably affected by the degree of wake-turbine overlap and the wave. These findings underscore the importance of incorporating fatigue load analysis into the wind farm layout optimization process to extend the turbine lifespan and reduce operation and maintenance costs.

A comparison of h- and p-refinement to capture wind turbine wakes

This paper investigates a critical aspect of wind energy research—the development of wind turbine wake and its significant impact on wind farm efficiency. The study focuses on the exploration and comparison of two mesh refinement strategies, h- and p-refinement, in their ability to accurately compute the development of wind turbine wake. The h-refinement method refines the mesh by reducing the size of the elements, while the p-refinement method increases the polynomial degree of the elements, potentially reducing the error exponentially for smooth flows. A comprehensive comparison of these methods is presented that evaluates their effectiveness, computational efficiency, and suitability for various scenarios in wind energy. The findings of this research could potentially guide future studies and applications in wind turbine wake modeling, thus contributing to the optimization of wind farms using high-order h/p methods. This study fills a gap in the literature by thoroughly investigating the application of these methods in the context of wind turbine wake development.

Prediction of offshore wind turbine wake and output power using large eddy simulation and convolutional neural network

Predicting offshore wind turbine wake and output power is crucial for optimizing wind farm layouts and maximizing wind energy production. In recent years, several Computational Fluid Dynamics methods have been developed to predict wind turbine wake and output power and demonstrated good performance compared with traditional analytical models. However, Computational Fluid Dynamics often involve high computational costs in offshore wind farm design because a wide range of offshore wind conditions need to be considered for turbines with different inter-turbine spacings. To ensure both the fidelity and efficiency for predicting offshore wind turbine wake and output power, Large Eddy Simulation and Convolutional Neural Network are utilized in this study. The Large Eddy Simulation effectively integrates the Actuator Line Method and Discretizing and Synthesizing Random Flow Generation to generate wake velocity, wake turbulence intensity, and output power for a stand-alone turbine under different incoming wind speeds and turbulence intensities. Using the generated dataset, Convolutional Neural Network effectively captures the relationship between inputs and outputs for the stand-alone turbine. The predicted wake data for the turbine can then act as input to estimate the output power density and wake characteristics of a downstream turbine. This process can be iteratively applied to predict the wake and output power of each subsequent turbine in a wind farm, supporting the identification of optimal inter-turbine spacing. The proposed method is illustrated using a utility-scale 5 MW wind turbine. The results show that the errors of predicted output power for a stand-alone wind turbine and multiple wind turbines are blew 3 %.

Characterization of Wind Turbine Blade Deformation and Wake Flow Field with Different Numbers of Lay-Up Layers

The change in the composite lay-up method affects the blade stiffness, which in turn affects the structural dynamic and aerodynamic characteristics, but the influence law is not yet clear. In this paper, two-way fluid–structure coupling is used to study wind turbine blades with different numbers of lay-up layers. The analysis results show the following: (1) With the increase in the number of pavement layers, the maximum deformation of the blade tip decreases, the magnitude of the decrease also decreases gradually, and the growth trend of deformation along the blade spreading direction is gradually flattened. (2) The maximum stress–strain of the root of the blade gradually decreases, the tip of the blade is the opposite of the blade, the unevenness of the distribution of the stress–strain of the surface of the blade is gradually slowed down, and the concentration area is shifted back. (3) The different numbers of pavement layers affect the turbulence intensity of the blade in the flow field space. Herein, the spatial distribution trend of turbulence intensity in the flow field of wind turbine blades with different numbers of layers is basically the same, and the trend of turbulence intensity changes is gentle when the number of layers is increased. (4) The amount of the tip vortex in the wake stream of the wind turbine decreases, and the decay rate of the center vortex increases. A reasonable lay-up scheme can improve the deformation and stress of the blade, extend the service life of the blade, promote airflow mixing, and enhance the energy conversion efficiency.

Optimum design of contra-rotating wind turbines with adjacent rotors

A downstream installed contrarotating rotor can efficiently extract the residual kinetic energy of a wind turbine wake, thus increasing the overall power coefficient. A significant interaction between the two rotors exists so that, to completely exploit the device potential, it is not advisable to adopt a classical design procedure conceived for a single-rotor configuration, as commonly seen in the existing literature. In particular, current approaches also oversimplify the design of the back rotor by merely mirroring the front rotor geometry, rather than optimising it for its power extraction duties. To fill this knowledge gap, the paper presents an original design strategy expressly conceived for contra-rotating wind turbines. This new approach optimises the geometry of both rotors, fully accounting for the interaction between them and the wake rotation at the outlet of the front wheel. The procedure adopts a calculus of variations approach aimed at maximising the overall power coefficient. It relies on a classical actuator disk model and is it valid for two adjacent rotors with the same diameter. As a result, the optimal chord and pitch distributions of the two rotors are evaluated for any combination of the front and back tip speed ratios. The procedure can be applied to marine turbines, too.

Momentum deficit and wake-added turbulence kinetic energy budgets in the stratified atmospheric boundary layer

To achieve decarbonization targets, wind turbines are growing in hub height and rotor diameter, and they are being deployed in new locations with diverse atmospheric conditions not previously seen, such as offshore. Physics-based analytical wake models commonly used for design and control of wind farms simplify atmospheric boundary layer (ABL) and wake physics to achieve computational efficiency. This is accomplished primarily through a simplified model form that neglects certain flow processes, such as atmospheric stability, and through the parametrization of ABL and wake turbulence through a wake spreading rate. In this study, we systematically analyze the physical mechanisms that govern momentum and turbulence within a wind turbine wake in the stratified ABL. We use large-eddy simulation and analysis of the streamwise momentum deficit and wake-added turbulence kinetic energy (TKE) budgets to study wind turbine wakes under neutral and stable conditions. To parse the turbulence in the wake from the turbulent, incident ABL flow, we decompose the flow into the base ABL flow and the deficit flow produced by the presence of a turbine. We then analyze the decomposed flow field budgets to study the effects of changing stability on the streamwise momentum deficit and wake-added TKE. The results demonstrate that stability changes the relative balance of turbulence and advection for both the streamwise momentum deficit and wake-added TKE primarily through the nonlinear interactions of the base flow with the deficit flow. The stable cases are most affected by increased shear and veer in the base flow and the neutral case is most affected by the increased ambient turbulence intensity. These differences in the base flow that arise from stratification are relatively more important than the buoyancy forcing terms in the wake-added TKE budget. The wake-added TKE depends on the ABL stability. An existing wake-added TKE model that neglects the effects of ABL stability yields 15–25% error compared to large-eddy simulation, with errors that are higher in stable conditions than neutral. These results motivate future research to develop fast-running models of wake-added TKE that account for stability effects. Published by the American Physical Society 2024

Impact of freestream turbulence integral length scale on wind farm flows and power generation

The impact of freestream turbulence integral length scale on wind farm flow and power production is investigated by conducting Large Eddy Simulations on wind farms with two spacings, Sx=8R and Sx=12R (turbine radius R). The integral length scale of inflow turbulence Lu is varied, Lu∈[3.2R,12.0R], while maintaining identical turbulence intensity and velocity. Shorter integral length scales lead to a faster near wake breakdown and improved wake recovery in the wake of the first turbine, causing substantial increases in the second turbine power output; 42% and 18% for the two spacings. Over the first four turbines, total power output increases by 8.6% and 6.0% respectively. Spectra, cross-correlations and entrainment scales are also examined and show that the first turbine breaks down inflow scales and wake-generated turbulence dominates the inflow to the second turbine. Further into the turbine row, dominant flow structures and entrainment scales are associated with both wake turbulence and larger wind farm-generated structures matching the turbine spacing. These results show that the freestream turbulence integral length scale has a significant impact on wind farm flows and power generation, mainly by impacting the development of wakes in the farm entrance.