On the importance of wind turbine wake boundary to wind energy and environmental impact

Fan, Zhun; Li, Shan; Gao, Zhiteng; Zhang, Lijun; Zheng, Xing; Zhu, Weijun; Shen, Wen Zhong; Sjöholm, Mikael; Mikkelsen, Torben; Wang, Tongguang; Li, Ye

doi:10.1016/j.enconman.2023.116664

Cited by 17 publications

(14 citation statements)

References 60 publications

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“…The performance of a wind turbine are significantly affected by the wind turbine wake, then a crucial step for DUST validation is the ability to correctly capture the wake development and its main features in terms of velocity field. Then, the deficit of the axial velocity in the wake calculated by DUST is compared with the high-fidelity simulation by [45], considering 4 stream wise position located at different distances x downstream from the rotor plane. Velocity profiles comparison is shown in Figure 7 as function of z, representing the distance from the shaft axis parallel to the rotor plane.…”

Section: Nrel-5 Mw Wind Turbinementioning

confidence: 99%

“…Generally, Figure 8 illustrates the wake structure computed by DUST shown as an iso-surface of the vortex Q-criterion. Readers are referred to results reported in [45] to appreciate DUST capability to capture the behaviour of the wake structure computed by CFD. The influence of blade cone angle α C and shaft tilt angle α T on the aerodynamic performance under rated inflow conditions is now explored by comparing results obtained with DUST and highfidelity CFD by Dose et al [26] over C1, C3 and C4 cases.…”

Section: Nrel-5 Mw Wind Turbinementioning

confidence: 99%

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Validation of a Mid-fidelity Numerical Approach for Wind Turbine Aerodynamics Characterization

Savino,

Ferreri,

Zanotti

2024

Preprint

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This work is aimed at investigating the capabilities and limits of the mid-fidelity numerical solver DUST for the evaluation of wind turbines aerodynamic performance. In particular, this study was conducted by analysing the benchmarks NREL- 5MW and Phase VI wind turbines, widely investigated in literature by from experimental and numerical activities. The work started by simulating simpler configuration of the NREL- 5MW turbine to progressively integrate complexities such as shaft tilt, cone effects and yawed inflow conditions, offering a detailed portrayal of their collective impact on turbine performance. A particular focus was then given to the evaluation of aerodynamic responses from the tower and nacelle, as well as aerodynamic behavior in yawed inflow condition, crucial for optimizing farm layouts. In the second phase, the work was focused on NREL Phase VI turbine due to the availability of experimental data on this benchmark case. Comparison of DUST simulations results with both experimental data and high-fidelity CFD tools shows the robustness and adaptability of this mid-fidelity solver for this applications, thus opening a new scenario for the use of such mid-fidelity tools for the preliminary design of novel wind turbines configurations or complex environments as wind farms, characterised by robust interactional aerodynamics.

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Section: Nrel-5 Mw Wind Turbinementioning

confidence: 99%

Section: Nrel-5 Mw Wind Turbinementioning

confidence: 99%

Validation of a Mid-fidelity Numerical Approach for Wind Turbine Aerodynamics Characterization

Savino,

Ferreri,

Zanotti

2024

Preprint

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“…Proper characterization of atmospheric flow velocities and turbulence is essential to understanding the structure and dynamics of the atmospheric boundary layer (ABL ;Stull, 1988;Wyngaard, 2010). It is, therefore, crucial to obtain accurate wind and temperature measurements with high spatial and temporal resolutions for a variety of basic and applied ABL research topics, such as weather and climate prediction (Teixeira et al, 2008), wind energy meteorology (Emeis, 2010;Albornoz et al, 2022), and atmospheric modeling (Etling, 1996). Historically, sonic anemometers have been the most common instrument for measuring atmospheric flow and turbulence since they were introduced in the 1950s (Suomi, 1957).…”

Section: Introductionmentioning

confidence: 99%

“…Continuous-wave (CW) Doppler lidar can remotely obtain accurate three-dimensional flow observations without disturbing the flow. Consequently, CW lidars are extensively applied to detect wind profiles (Köpp et al, 1984;Peña et al, 2009), assess wind resources (Bingöl et al, 2009;Viselli et al, 2019), test wind turbine performance based on wake measurements (Wagner et al, 2014;Shin and Ko, 2019;Fan et al, 2023), predict the incoming gusts and flow to reduce loads (Bos et al, 2016), and study turbulence around a suspension bridge (Cheynet et al, 2016) and in the near-wake region of a tree (Angelou et al, 2022), with good spatial and temporal resolutions. Recently, two CW lidars were used to measure the two-dimensional downwash wind fields in a horizontal and a vertical plane below a hovering search-andrescue helicopter (Sjöholm et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Jin,

Ghirardelli,

Mann

et al. 2024

Atmos. Meas. Tech.

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Abstract. Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 m s−1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 m s−1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers.

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“…Wind energy is the leading source of renewable energy worldwide [1]. As of the end of 2021, the cumulative global installed wind capacity was approximately 837 GW [2].…”

Section: Introductionmentioning

confidence: 99%

Online Structural Damage Classification Methodology for Offshore Wind Turbine Foundations Using Data Stream Analysis

Leon Medina,

Parés,

Pozo

2023

10th ECCOMAS Thematic Conference on Smart Structures and Materials

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Structural health monitoring (SHM) of wind turbines is crucial to improve maintenance and extend their lifespan. This study develops an online data analysis methodology using data stream analysis to classify damage in the links of an offshore wind turbine foundation. The methodology is validated using a laboratory-scaled jacket-type wind turbine foundation structure. 2460 measurements of the healthy structure were acquired, and a 5mm crack was applied to four different links to determine the four unhealthy classes. 820 measurements were taken for each of the unhealthy structures, resulting in a dataset with 5740 instances. As this is an imbalanced multiclass classification problem, a random sampler approach was used to treat the data. The only data obtained was from eight triaxial accelerometers distributed throughout the structure. Three different tree-based stream data classifiers were compared: Hoeffding Tree classifier, Extremely Fast Decision Tree classifier, and Hoeffding Adaptive Tree classifier. Each classification model underwent a tuning parameter procedure, and high values of the receiving operating characteristic area under the curve (ROC AUC) metric were achieved as a result. It is important to note that stream learning differs from batch learning.

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On the importance of wind turbine wake boundary to wind energy and environmental impact

Cited by 17 publications

References 60 publications

Validation of a Mid-fidelity Numerical Approach for Wind Turbine Aerodynamics Characterization

Validation of a Mid-fidelity Numerical Approach for Wind Turbine Aerodynamics Characterization

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Online Structural Damage Classification Methodology for Offshore Wind Turbine Foundations Using Data Stream Analysis

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