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1.
Sicheng Wu;Cristina L. Archer;Jeffrey D. Mirocha; 《风能》2024,27(11):1130-1151
Large-eddy simulation (LES) has been adopted to study wind turbine wakes because it can capture fine-scale details of turbulent wind flows and interactions with wind turbines. Here, we use the LES version of the Weather Research and Forecasting (WRF) model with an actuator disk model to gain insights on several wake effects that have been traditionally difficult to measure. The first finding is that the wake has a “dual nature,” meaning that the wind speed deficit behaves differently from the added turbulent kinetic energy (TKE) and the two are not co-located in space. For example, the wind speed deficit peaks at hub height and reaches the ground within 8D (D is the rotor diameter), but added TKE peaks near the rotor tip and generally remains aloft. Second, temperature changes near the ground are driven by the added TKE in the rotor area and by atmospheric stability. The combination of these two factors determines the sign and intensity of the vertical heat flux divergence below the rotor, with convergence and warming associated with stable conditions and weak divergence and modest cooling with unstable conditions. Third, wakes do not expand indefinitely, as suggested by similarity theory applied to the wind speed deficit, but eventually stop expanding and actually contract, at different rates depending on atmospheric stability. The implication of these findings is that, in order to study wakes, it is not sufficient to focus on wind speed deficit alone, because TKE is also important and yet behaves differently from the wind speed deficit. 相似文献
2.
Niranjan S. Ghaisas Cristina L. Archer Shengbai Xie Sicheng Wu Eoghan Maguire 《风能》2017,20(7):1227-1240
Large‐eddy simulation (LES) has been used previously to study the effect of either configuration or atmospheric stability on the power generated by large wind farms. This is the first study to consider both stability and wind farm configuration simultaneously and methodically with LES. Two prevailing wind directions, two layouts (turbines aligned versus staggered with respect to the wind) and three stabilities (neutral and moderately unstable and stable) were evaluated. Compared with neutral conditions, unstable conditions led to reduced wake losses in one configuration, to enhanced wake losses in two and to unchanged wake losses in one configuration. Conversely, stable conditions led to increased wake losses in one, decreased wake losses in two and unchanged wake losses in one configuration. Three competing effects, namely, rates of wake recovery due to vertical mixing, horizontal spread of wakes and localized regions of acceleration caused by multiple upstream wakes, were identified as being responsible for the observed trends in wake losses. The detailed flow features responsible for these non‐linear interactions could only be resolved by the LES. Existing analytical models ignore stability and non‐linear configuration effects, which therefore need to be incorporated. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
3.
4.
Paul Fleming Pieter M.O. Gebraad Sang Lee Jan‐Willem van Wingerden Kathryn Johnson Matt Churchfield John Michalakes Philippe Spalart Patrick Moriarty 《风能》2015,18(12):2135-2143
Wind turbines arranged in a wind plant impact each other through their wakes. Wind plant control is an active research field that attempts to improve wind plant performance by coordinating control of individual turbines to take into account these turbine–wake interactions. In this paper, high‐fidelity simulations of a two‐turbine fully waked scenario are used to investigate several wake mitigation strategies, including modification of yaw and tilt angles of an upstream turbine to induce wake skew, as well as repositioning of the downstream turbine. The simulation results are compared through change relative to a baseline operation in terms of overall power capture and loading on the upstream and downstream turbine. Results demonstrated improved power production for all methods. Analysis of control options, including individual pitch control, shows potential to minimize the increase of, or even reduce, turbine loads.Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
5.
R. J. Barthelmie S. T. Frandsen M. N. Nielsen S. C. Pryor P.‐E. Rethore H. E. Jørgensen 《风能》2007,10(6):517-528
Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
6.
The optimization of wind farms with respect to spatial layout is addressed experimentally. Wake effects within wind turbine farms are well known to be deleterious in terms of power generation and structural loading, which is corroborated in this study. Computational models are the predominant tools in the prediction of turbine‐induced flow fields. However, for wind farms comprising hundreds of turbines, reliability of the obtained numerical data becomes a growing concern with potentially costly consequences. This study pursues a systematic complementary theoretical, experimental and numerical study of variations in generated power with turbine layout of an 80 turbine large wind farm. Wake effects within offshore wind turbine arrays are emulated using porous discs mounted on a flat plate in a wind tunnel. The adopted approach to reproduce experimentally individual turbine wake characteristics is presented, and drag measurements are argued to correctly capture the variation in power generation with turbine layout. Experimental data are juxtaposed with power predictions using ANSYS WindModeller simulation suite. Although comparison with available wind farm power output data has been limited, it is demonstrated nonetheless that this approach has potential for the validation of numerical models of power loss due to wake effects or even to make a direct physical prediction. The approach has even indicated useful data for the improvement of the physics within numerical models. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
7.
Understanding the detailed dynamics of wind turbine wakes is critical to predicting the performance and maximizing the efficiency of wind farms. This knowledge requires atmospheric data at a high spatial and temporal resolution, which are not easily obtained from direct measurements. Therefore, research is often based on numerical models, which vary in fidelity and computational cost. The simplest models produce axisymmetric wakes and are only valid beyond the near wake. Higher‐fidelity results can be obtained by solving the filtered Navier–Stokes equations at a resolution that is sufficient to resolve the relevant turbulence scales. This work addresses the gap between these two extremes by proposing a stochastic model that produces an unsteady asymmetric wake. The model is developed based on a large‐eddy simulation (LES) of an offshore wind farm. Because there are several ways of characterizing wakes, the first part of this work explores different approaches to defining global wake characteristics. From these, a model is developed that captures essential features of a LES‐generated wake at a small fraction of the cost. The synthetic wake successfully reproduces the mean characteristics of the original LES wake, including its area and stretching patterns, and statistics of the mean azimuthal radius. The mean and standard deviation of the wake width and height are also reproduced. This preliminary study focuses on reproducing the wake shape, while future work will incorporate velocity deficit and meandering, as well as different stability scenarios. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
8.
偏航状态下风力机叶片与流场之间相互作用会导致风力机近尾迹流场的湍流特征变化,采用双向流固耦合对不同偏航工况下水平轴风力机近尾迹流场进行数值模拟研究,获得不同偏航角下尾迹湍流特征演化规律。结果表明：随着偏航角的增大,正偏航侧会出现“速度亏损圆环”,且此圆环的范围呈扩大趋势;偏航角的增大对叶根处速度亏损影响最大,对叶尖处速度亏损影响最小,与正偏航侧相比,负偏航侧的速度亏损值减为约1/2;随着偏航角的增大,正负偏航侧的湍流强度变化呈不对称性,正偏航侧对湍流耗散的影响程度较负偏航侧大;涡流黏度越来越小,且在偏航10°涡流黏度相对于偏航5°减小约1/2,沿着轴向叶尖涡的管状环涡结构变得不稳定,出现明显耗散,且在偏航15°之后涡结构的耗散破裂程度越来越剧烈,进而对风力机气动噪声产生较大影响。 相似文献
9.
This study investigates the potential of using tilt‐based wake steering to alleviate wake shielding problems experienced by downwind turbines. Numerical simulations of turbine wakes have been conducted using a hybrid free‐wake analysis combining vortex lattice method (VLM) and an innovative free‐wake model called constant circulation contour method (CCCM). Simulation results indicate tilting a horizontal axis wind turbine's shaft upward causes its wake to ascend, carrying energy‐depleted air upward and pumping more energetic replacement air into downstream turbines, thereby having the potential to recover downstream turbine power generation. Wake cross section vorticity and velocity distributions reveal that the wake upward transport is caused by the formation of near‐wake streamwise vorticity components, and furthermore, the wake velocity deficit is weakened because of the skewed wake structure. Beyond the single turbine wake simulation, an inline two‐turbine case is performed as an assessment of the wake steering influence on the two‐turbine system and as an exploratory work of simulating turbine‐wake interactions using the hybrid free‐wake model. Individual and total turbine powers are calculated. A comparison between different tilting angles suggests turbine power enhancement may be achieved by tilting the upstream turbine and steering its wakes away from the downstream turbine. 相似文献
10.
This article provides an overview and analysis of different wake‐modelling methods which may be used as prediction and design tools for both wind turbines and wind farms. We also survey the available data concerning the measurement of wind magnitudes in both single wakes and wind farms, and of loading effects on wind turbines under single‐ and multiple‐wake conditions. The relative merits of existing wake and wind farm models and their ability to reproduce experimental results are discussed. Conclusions are provided concerning the usefulness of the different modelling approaches examined, and difficult issues which have not yet been satisfactorily treated and which require further research are discussed. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
11.
Rolf‐Erik Keck Martin de Maré Matthew J. Churchfield Sang Lee Gunner Larsen Helge Aagaard Madsen 《风能》2014,17(11):1689-1710
The present study investigates a new approach for capturing the effects of atmospheric stability on wind turbine wake evolution and wake meandering by using the dynamic wake meandering model. The most notable impact of atmospheric stability on the wind is the changes in length and velocity scales of the atmospheric turbulence. The length and velocity scales in the turbulence are largely responsible for the way in which wind turbine wakes meander as they convect downstream. The hypothesis of the present work is that appropriate turbulence scales can be extracted from the oncoming atmospheric turbulence spectra and applied to the dynamic wake meandering model to capture the correct wake meandering behaviour. The ambient turbulence in all stability classes is generated using the Mann turbulence model, where the effects of non‐neutral atmospheric stability are approximated by the selection of input parameters. In order to isolate the effect of atmospheric stability, simulations of neutral and unstable atmospheric boundary layers using large‐eddy simulation are performed at the same streamwise turbulence intensity level. The turbulence intensity is kept constant by calibrating the surface roughness in the computational domain. The changes in the turbulent length scales due to the various atmospheric stability states impact the wake meandering characteristics and thus the power generation by the individual turbines. The proposed method is compared with results from both large‐eddy simulation coupled with an actuator line model and field measurements, where generally good agreement is found with respect to the velocity, turbulence intensity and power predictions. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
12.
Rolf‐Erik Keck 《风能》2015,18(9):1579-1591
This paper presents validation for using the standalone implementation of the dynamic wake meandering (DWM) model to conduct numerical simulations of power production of rows of wind turbines. The standalone DWM model is an alternative formulation of the conventional DWM model that does not require information exchange with an aeroelastic code. As a consequence, the standalone DWM model has significantly shorter computational times and lower demands on the user environment. The drawback of the standalone DWM model is that it does not have the capability to predict turbine loads. Instead, it should be seen as an alternative for simulating the power production of a wind farm. The main advantage of the standalone DWM model is the ability to capture the key physics for wake dynamics such as the turbine specific induction, the build‐up of wake turbulence and wake deficit in the wind farm, and the effect of ambient turbulence intensity and atmospheric stability. The predicted power production of the standalone DWM model is compared with that of full scale measurements from Horns Rev, Lillgrund, Nysted and Weingermeer wind farms. Overall, the difference between the models predictions and the reference data is on the order of 5%. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
13.
Mean and turbulent properties of the wake generated by a single wind turbine are studied in this paper with a new large eddy simulation (LES) code, the wind turbine and turbulence simulator (WiTTS hereafter). WiTTS uses a scale‐dependent Lagrangian dynamical model of the sub‐grid shear stress and actuator lines to simulate the effects of the rotating blades. WiTTS is first tested by simulating neutral boundary layers without and with a wind turbine and then used to study the common assumptions of self‐similarity and axisymmetry of the wake under neutral conditions for a variety of wind speeds and turbine properties. We find that the wind velocity deficit generally remains self similarity to a Gaussian distribution in the horizontal. In the vertical, the Gaussian self‐similarity is still valid in the upper part of the wake, but it breaks down in the region of the wake close to the ground. The horizontal expansion of the wake is always faster and greater than the vertical expansion under neutral stability due to wind shear and impact with the ground. Two modifications to existing equations for the mean velocity deficit and the maximum added turbulence intensity are proposed and successfully tested. The anisotropic wake expansion is taken into account in the modified model of the mean velocity deficit. Turbulent kinetic energy (TKE) budgets show that production and advection exceed dissipation and turbulent transport. The nacelle causes significant increase of every term in the TKE budget in the near wake. In conclusion, WiTTS performs satisfactorily in the rotor region of wind turbine wakes under neutral stability. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
14.
Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
15.
We present a methodology to process wind turbine wake simulations, which are closely related to the nature of wake observations and the processing of these to generate the so‐called wake cases. The method involves averaging a large number of wake simulations over a range of wind directions and partly accounts for the uncertainty in the wind direction assuming that the same follows a Gaussian distribution. Simulations of the single and double wake measurements at the Sexbierum onshore wind farm are performed using a fast engineering wind farm wake model based on the Jensen wake model, a linearized computational fluid dynamics wake model by Fuga and a nonlinear computational fluid dynamics wake model that solves the Reynolds‐averaged Navier–Stokes equations with a modified k‐ε turbulence model. The best agreement between models and measurements is found using the Jensen‐based wake model with the suggested post‐processing. We show that the wake decay coefficient of the Jensen wake model must be decreased from the commonly used onshore value of 0.075 to 0.038, when applied to the Sexbierum cases, as wake decay is related to the height, roughness and atmospheric stability and, thus, to turbulence intensity. Based on surface layer relations and assumptions between turbulence intensity and atmospheric stability, we find that at Sexbierum, the atmosphere was probably close to stable, although the stability was not observed. We support these assumptions using detailed meteorological observations from the Høvsøre site in Denmark, which is topographically similar to the Sexbierum region. © 2015 The Authors. Wind Energy published by John Wiley & Sons Ltd. 相似文献
16.
Model wind turbine arrays were developed for the purpose of investigating the wake interaction and turbine canopy layer in a standard cartesian and row‐offset turbine array configurations. Stereographic particle image velocimetry was used to collect flow data upstream and downstream of entrance and exit row turbines in each configuration. Wakes for all cases were analyzed for energy content and recovery behavior including entrainment of high‐momentum flow from above the turbine canopy layer. The row‐offset arrangement of turbines within an array grants an increase in streamwise spacing of devices and allows for greater wake remediation between successive rows. These effects are seen in exit row turbine wakes as changes to statistical quantities including the in‐plane Reynolds stress, , and the production of turbulence. The recovery of wakes also strongly mitigates the perceived underperformance of wind turbines within an array. The flux of kinetic energy is demonstrated to be more localized in the entrance rows and in the offset arrangement. Extreme values for the flux of kinetic energy are about 7.5% less in the exit row of the cartesian arrangement than in the offset arrangement. Measurements of mechanical torque at entrance and exit row turbines lead to curves of power coefficient and demonstrate an increase in efficiency in row‐offset configurations. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
17.
Wind turbine spacing is an important design parameter for wind farms. Placing turbines too close together reduces their power extraction because of wake effects and increases maintenance costs because of unsteady loading. Conversely, placing them further apart increases land and cabling costs, as well as electrical resistance losses. The asymptotic limit of very large wind farms in which the flow conditions can be considered ‘fully developed’ provides a useful framework for studying general trends in optimal layouts as a function of dimensionless cost parameters. Earlier analytical work by Meyers and Meneveau (Wind Energy 15, 305–317 (2012)) revealed that in the limit of very large wind farms, the optimal turbine spacing accounting for the turbine and land costs is significantly larger than the value found in typical existing wind farms. Here, we generalize the analysis to include effects of cable and maintenance costs upon optimal wind turbine spacing in very large wind farms under various economic criteria. For marginally profitable wind farms, minimum cost and maximum profit turbine spacings coincide. Assuming linear‐based and area‐based costs that are representative of either offshore or onshore sites we obtain for very large wind farms spacings that tend to be appreciably greater than occurring in actual farms confirming earlier results but now including cabling costs. However, we show later that if wind farms are highly profitable then optimization of the profit per unit area leads to tighter optimal spacings than would be implied by cost minimization. In addition, we investigate the influence of the type of wind farm layout. © 2016 The Authors Wind Energy Published by John Wiley & Sons Ltd 相似文献
18.
A wind tunnel experiment has been performed to quantify the Reynolds number dependence of turbulence statistics in the wake of a model wind turbine. A wind turbine was placed in a boundary layer flow developed over a smooth surface under thermally neutral conditions. Experiments considered Reynolds numbers on the basis of the turbine rotor diameter and the velocity at hub height, ranging from Re = 1.66 × 104 to 1.73 × 105. Results suggest that main flow statistics (mean velocity, turbulence intensity, kinematic shear stress and velocity skewness) become independent of Reynolds number starting from Re ≈ 9.3 × 104. In general, stronger Reynolds number dependence was observed in the near wake region where the flow is strongly affected by the aerodynamics of the wind turbine blades. In contrast, in the far wake region, where the boundary layer flow starts to modulate the dynamics of the wake, main statistics showed weak Reynolds dependence. These results will allow us to extrapolate wind tunnel and computational fluid dynamic simulations, which often are conducted at lower Reynolds numbers, to full‐scale conditions. In particular, these findings motivates us to improve existing parameterizations for wind turbine wakes (e.g. velocity deficit, wake expansion, turbulence intensity) under neutral conditions and the predictive capabilities of atmospheric large eddy simulation models. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
19.
An experimental study of the near wake up to four rotor diameters behind a model wind turbine rotor with two different wing tip configurations is performed. A straight‐cut wing tip and a downstream‐facing winglet shape are compared on the same two‐bladed rotor operated at its design tip speed ratio. Phase‐averaged measurements of the velocity vector are synchronized with the rotor position, visualizing the downstream location of tip vortex interaction for the two blade tip configurations. The mean streamwise velocity is found not to be strongly affected by the presence of winglet tip extensions, suggesting an insignificant effect of winglets on the time‐averaged inflow conditions of a possible downstream wind turbine. An analysis of the phase‐averaged vorticity, however, reveals a significantly earlier tip vortex interaction and breakup for the wingletted rotor. In contradistinction, the tip vortices formed behind the reference configuration are assessed to be more stable and start merging into larger turbulent structures significantly further downstream. These results indicate that an optimized winglet design can not only contribute to a higher energy extraction in a rotor's tip region but also can positively affect the wake's mean kinetic energy recovery by stimulating a faster tip vortex interaction. 相似文献
20.
Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献