The COTUR project: remote sensing of offshore turbulence for wind energy application

Cheynet, Etienne; Flügge, Martin; Reuder, Joachim; Jakobsen, Jasna Bogunović; Heggelund, Yngve; Svardal, Benny; Garfias, Pablo Saavedra; Obhrai, Charlotte; Daniotti, Nicolò; Berge, Jarle; Duscha, Christiane; Wildmann, Norman; Onarheim, Ingrid Husøy; Godvik, Marte

doi:10.5194/amt-14-6137-2021

Cited by 10 publications

(7 citation statements)

References 79 publications

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“…Several studies based on field data observations have reported non-neutral atmospheric conditions in the marine atmospheric boundary layer. [2][3][4][5] Ainslie 1 observed that wake meandering increased with decreasing atmospheric stability, which has been also reported by Churchfield et al, 6 Keck et al, 7 or Abkar and Porté-Agel 8 in more recent studies. Furthermore, incoming wind fields in unstable atmospheric conditions are characterized by weak wind shear, high turbulence intensity, large coherent structures, and higher turbulence kinetic energy in the wake, while stable conditions are strongly sheared with very small coherent structures and lower turbulence intensity and kinetic energy and therefore slower recovery of wake.…”

supporting

confidence: 60%

“…The characteristic meandering frequencies are low enough to be in the same frequency range as the natural frequencies of FWT rigid body motions. Several studies based on field data observations have reported non‐neutral atmospheric conditions in the marine atmospheric boundary layer 2–5 . Ainslie 1 observed that wake meandering increased with decreasing atmospheric stability, which has been also reported by Churchfield et al, 6 Keck et al, 7 or Abkar and Porté‐Agel 8 in more recent studies.…”

Section: Introductionmentioning

confidence: 53%

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Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

Rivera‐Arreba,

Wise,

Eliassen

et al. 2023

Wind Energy

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Current global analysis tools for floating wind turbines (FWTs) do not account for the combined effects of atmospheric stability and wakes from neighboring turbines. This work uses the mid‐fidelity dynamic wake meandering model, together with turbulent wind fields generated based on stable, neutral, and unstable atmospheric conditions, to study the low‐frequency content of the global responses of two semisubmersible FWTs separated by eight rotor diameters. Incoming wind fields based on the Kaimal spectrum and exponential coherence model, the Mann spectral tensor model, and a time‐series input‐based turbulence model are used. The respective input parameters for these models are fitted to high‐fidelity large eddy simulation data.In unstable, below‐rated conditions, meandering leads to an increase in the yaw standard deviation of the downwind turbine of almost three times larger than the upwind turbine. Deficit and the upwards wake deflection affect the mean pitch and yaw, especially for the below‐rated wind speed scenario. The mean pitch of the downwind turbine is reduced up to half the mean pitch value of the upwind turbine, whereas the mean yaw changes direction due to the enhanced effect of shear. The effect of meandering on the structural loading is highest on the standard deviation of the tower‐top yaw moment of the downstream turbine, which increases more than 2.2 times compared to the upwind turbine value. Based on these findings, atmospheric stability affects wake deficit and meandering which in turn have a profound effect on the low‐frequency global motions and structural response of floating wind turbines.

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confidence: 60%

Section: Introductionmentioning

confidence: 53%

Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

Rivera‐Arreba,

Wise,

Eliassen

et al. 2023

Wind Energy

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“…GE Renewable Energy, 2021). At such heights, MOST may no longer be applicable (Peña and Gryning, 2008;Cheynet et al, 2021). Above the surface layer, the velocity spectra may become independent of the height above the surface, which is coarsely accounted for in IEC 61400-1 (2005).…”

Section: Relevancy Of the Database For Load Calculation Of Owtsmentioning

confidence: 99%

Turbulence in a coastal environment: the case of Vindeby

Putri¹,

Cheynet²,

Obhrai³

et al. 2022

Wind Energ. Sci.

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Abstract. The one-point and two-point power spectral densities of the wind velocity fluctuations are studied using the observations from an offshore mast at Vindeby Offshore Wind Farm, for a wide range of thermal stratifications of the atmosphere. A comparison with estimates from the FINO1 platform (North Sea) is made to identify shared spectral characteristics of turbulence between different offshore sites. The sonic anemometer measurement data at 6, 18, and 45 m a.m.s.l. (above mean sea level) are considered. These heights are lower than at the FINO1 platform, where the measurements were collected at heights between 40 and 80 m. Although the sonic anemometers are affected by transducer-flow distortion, the spectra of the along-wind velocity component are consistent with those from FINO1 when surface-layer scaling is used, for near-neutral and moderately diabatic conditions. The co-coherence of the along-wind component, estimated for vertical separations under near-neutral conditions, matches remarkably well with the results from the dataset at the FINO1 platform. These findings mark an important step toward more comprehensive coherence models for wind load calculation. The turbulence characteristics estimated from the present dataset are valuable for better understanding the structure of turbulence in the marine atmospheric boundary layer and are relevant for load estimations of offshore wind turbines. Yet, the datasets recorded at Vindeby and FINO1 cover only the lower part of the rotor of state-of-the-art offshore wind turbines. Further improvements in the characterisation of atmospheric turbulence for wind turbine design will require measurements at heights above 100 m a.m.s.l.

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“…LiDAR remote sensing offers one pathway in this direction. Although scanning Doppler wind LiDARs, such as short-range or long-range WindScanner systems, offer valuable wind measurements [12,13], their flexibility and applicability are constrained by several factors. Unlike ultrasonic anemometers, they do not capture the virtual temperature, which is a crucial parameter for comprehensive ABL studies.…”

Section: Introductionmentioning

confidence: 99%

Experimental Characterization of Propeller-Induced Flow (PIF) below a Multi-Rotor UAV

Flem,

Ghirardelli,

Kral

et al. 2024

Atmosphere

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The availability of multi-rotor UAVs with lifting capacities of several kilograms allows for a new paradigm in atmospheric measurement techniques, i.e., the integration of research-grade sonic anemometers for airborne turbulence measurements. With their ability to hover and move very slowly, this approach yields unrevealed flexibility compared to mast-based sonic anemometers for a wide range of boundary layer investigations that require an accurate characterization of the turbulent flow. For an optimized sensor placement, potential disturbances by the propeller-induced flow (PIF) must be considered. The PIF characterization can be done by CFD simulations, which, however, require validation. For this purpose, we conducted an experiment to map the PIF below a multi-rotor drone using a mobile array of five sonic anemometers. To achieve measurements in a controlled environment, the drone was mounted inside a hall at a 90° angle to its usual flying orientation, thus leading to the development of a horizontal downwash, which is not subject to a pronounced ground effect. The resulting dataset maps the PIF parallel to the rotor plane from two rotor diameters, beneath, to 10 D, and perpendicular to the rotor plane from the center line of the downwash to a distance of 3 D. This measurement strategy resulted in a detailed three-dimensional picture of the downwash below the drone in high spatial resolution. The experimental results show that the PIF quickly decreases with increasing distance from the centerline of the downwash in the direction perpendicular to the rotor plane. At a distance of 1 D from the centerline, the PIF reduced to less than 4 ms−1 within the first 5 D beneath the drone, and no conclusive disturbance was measured at 2 D out from the centerline. A PIF greater than 4 ms−1 was still observed along the center of the downwash at a distance of 10 D for both throttle settings tested (35% and 45%). Within the first 4 D under the rotor plane, flow convergence towards the center of the downwash was measured before changing to diverging, causing the downwash to expand. This coincides with the transition from the four individual downwash cores into a single one. The turbulent velocity fluctuations within the downwash were found to be largest towards the edges, where the shear between the PIF and the stagnant surrounding air is the largest.

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The COTUR project: remote sensing of offshore turbulence for wind energy application

Cited by 10 publications

References 79 publications

Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

Turbulence in a coastal environment: the case of Vindeby

Experimental Characterization of Propeller-Induced Flow (PIF) below a Multi-Rotor UAV

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