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The 18-year database of Envisat & Sentinel 1A-1B Synthetic Aperture Radar (SAR) provides worldwide surface wind measurements at a 1-km resolution. Through an innovative vertical extrapolation methodology published in 2022, these long-term, wide, and high-resolution observations can complement in-situ observations and mesoscale modelling for offshore wind resource assessment. The methodology is based on three steps: (i) derivation of the 10-min SAR surface winds from SAR sea surface roughness, and a site-independent machine learning algorithm based on a large buoy network to correct SAR winds due to biases inherent to the characteristics of the satellite sensors and wind retrieval methodology, (ii) extrapolation up to a few hundred meters based on a second machine learning algorithm trained with in-situ observations and physical parameters from a high-resolution mesoscale model related to e.g. atmospheric stability, and (iii) a final post-processing step to correct for low temporal sampling of the SAR database (one passage every two days for one satellite) and retrieve wind statistics. The resulting output is a 1-km resolution wind atlas in a large (> 3000 km2) offshore and/or coastal area where strong coastal gradients will be accounted for, or it can integrate directly the estimation of the extractible wind power. Here, an improvement of the extrapolation method is presented and applied to a French offshore area in South Brittany. The wind atlases obtained with SAR are found to display a much finer level of details and estimate more precisely the coastal gradient.

International audience; This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.

A new method is proposed to provide estimates of the turbulence intensity (TI) from measurements of pulsed lidars (light detection and ranging) employing the Doppler beam swinging technique. This method relies on combining the variances of the line-of-sight (LOS) velocities collected by the five independent beams of the lidars and, as such, is referred to as the variance method. The variance method comes with an explicit removal of the Doppler noise (inherent to the instrument) to the variance of the LOS velocities. Turbulence metrics derived from the variance method are compared to that derived from a standard method, commonly used in the wind energy industry. Reference turbulence measurements are provided by a sonic anemometer mounted on a meteorological mast, installed nearby the lidars. Two configurations of the WindCube v2.1 lidars are proposed: the commercial configuration and a prototype configuration, sampling 4 times faster, thus allowing to capture the turbulent energy of smaller eddies. The standard method applied on wind measurements collected by both configurations shows mean errors in TI estimates of more than 50 %. The application of the variance method on measurements collected by the commercial and prototype configuration drops the mean error to 16.7 % and 13.2 % respectively.

A new analytical wind turbine wake model, based on a super-Gaussian shape function, is presented. The super-Gaussian function evolves from a nearly top-hat shape in the near wake to a Gaussian shape in the far wake, which is consistent with observations and measurements of wind turbine wakes. Using such a shape function allows the recovery of the mass and momentum conservation that is violated when applying a near-wake regularization function to the expression of the maximum velocity deficit of the Gaussian wake model. After a brief introduction of the theoretical aspects, an easy-to-implement model with a limited number of parameters is derived. The super-Gaussian model predictions are compared to wind tunnel measurements, full-scale measurements, and a large-eddy simulation (LES), showing a good agreement and an improvement compared with predictions based on the Gaussian model.

International audience; Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade‐off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle‐mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large‐eddy simulations performing the best. In the more challenging stable case, steady‐state Reynolds‐averaged Navier–Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time‐stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake‐meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.

With the flourishing of offshore wind projects there is a new socio-economic interest to better our knowledge and forecasting ability of winds within the coastal marine atmospheric boundary layer (MABL). Air-sea fluxes of enthalpy and momentum greatly influence the turbulent and mean winds in the MABL. Already at moderate but certainly at high winds, wave breaking is a key driver of air-sea fluxes and the sea spray generated by whitecaps is thought to be a crucial component when modelling air-sea interactions. Most studies so far have focused on the role of sea spray in enhancing tropical cyclone intensity. Here we investigate its impacts on the MABL under strong orographic wind forcing. A coupled model framework was developed within the scope of the CASSIOWPE project aiming at characterizing the physical environment in the Gulf of Lion (NW Mediterranean Sea) in the prospective of future floating wind farms development. It consists of the non-hydrostatic mesoscale atmospheric model of the French research community Meso-NH, the 3rd generation wave model WAVEWATCH III®, and the oceanic model CROCO. Sea-spray physics were incorporated into the Meso-NH’s surface model SURFEX. Added parametrizations will be detailed and a series of test cases will be presented to illustrate how sea spray alters the MABL under Mistral and Tramontane winds. Several sea-state dependent sea spray generation functions (SSGF) are considered in the present study. The variability in simulated fields linked to the choice of wave forcing or coupling will be showcased to evaluate their suitability in varying fetch conditions. Sea spray production remains to be adequately quantified. Existing measurement derived SSGFs span several orders of magnitude resulting in uncertainties in simulated fields which will be discussed.

International audience; The growing size of wind turbines and wind farms in the offshore environment, eventually occupying tens of kilometers and extending beyond 200 m in height, has challenged traditional wind farm models to incorporate larger atmospheric scales with greater influence from the full extent of the atmospheric boundary layer (ABL). The modeling system is subject to variability from mesoscale weather phenomena like land-sea transitions or farm-farm effects that produce horizontal gradients in the wind resource, as well as phenomena like low-level jets, gravity waves, etc, that modify the turbulence structure of the ABL as it interacts with the wind farm [1][2]. The transition to multi-scale wind farm modeling requires a systematic methodology that allows determining the relative importance of these effects in wind farm performance and the predictive capacity of models [3]. This is especially important for offshore wind developers that face significant financial and operational costs due touncertainties in wind resource assessment [4]. Understanding how these uncertainties originate from wind farm design tools is of fundamental importance to mitigate these losses.

International audience; Offshore wind turbine near wakes can extend downstream up to 5D due to low atmospheric turbulence intensities. They are characterised by strong velocity deficits, a transitioning Gaussian shape, and strong added turbulence intensities. Classical analytical wake models are still used due to their low computational costs, but they mainly focus on far-wake characteristics. A super-Gaussian wake model valid in near-and far-wake regions has recently been developed at IFP Energies nouvelles. This wake model requires calibration and validation. To this end, large-eddy simulations of the large DTU-10MW reference wind turbine under different neutrally stratified atmospheric flows are carried out with the LES Meso-NH model. A database is generated based on these results and used to calibrate and validate the super-Gaussian model. 1. Introduction Estimation of wake losses is a critical part in a wind farm design process. Indeed, power losses due to wake effects are typically in the range of 10 to 20% and can rise up to 70% in the case of aligned turbines for wind velocities lower than the rated wind speed of the turbines [1]. Combining an accurate wake model to an optimisation algorithm results in a powerful tool, able to address the challenge of wind farm layout optimisation within a constrained area and the prediction of the annual energy production. A wake is commonly characterised by a reduction of the wind speed and an increase of the turbulence intensity, but these properties can be investigated further: the wake is indeed defined by two regions, the near and the far wake. The near wake, in the vicinity of the turbine, has features that are directly related to the rotor geometry, its aerodynamics, and the inflow conditions. It is characterised by strong velocity deficits, a transitioning top-hat/Gaussian shape, and strong added turbulence intensities. The near-wake shape may be altered by the presence of the hub and tower wakes. The far wake is more influenced by the surrounding flow: turbulent mixing governs the wake recovery. The transition is often considered to be located around 3 to 4 turbine diameters D, but it actually depends on the atmospheric flow. For example, offshore turbine near wakes can extend up to 5D due to lower atmospheric turbulence intensities. Geographical constraints (e.g. due to zoning regulation, water depth or soil conditions) can lead to wind farms with closely-spaced wind turbines (e.g. 3.3D and 4.2D minimal inter-distances respectively for Lillgrund and Ormonde offshore wind farms instead of 6 to 8D for most offshore wind farms in the last decades). Under these conditions, wind turbines can operate in the near wake of upstream turbines. It is therefore necessary to accurately model near wake behaviour.

The wake of a yawed wind turbine tends to be deflected and does not follow the incoming wind direction. This phenomenon, often referred to as “wake deflection”, has recently received a lot of attention from the wind energy research community: it is possible to dynamically optimize a wind farm power production by yawing some turbines to deflect their wakes, thus minimizing wake losses over the whole wind farm. In the present work, an analytical model for estimating the deflection of a yawed wind turbine wake is proposed. This new model is based on a super-Gaussian shape assumption for the velocity profile, enabling the estimation of the wake deflection in both near and far wake regions. First, the super-Gaussian model will be briefly introduced. Then, the new wake deflection model will be presented, as well as an updated version of the well known Jimenez model. This revised model allows a proper comparison with the proposed super-Gaussian model, since it is based on a similar definition of the wake width. Finally, results will be compared with experimental data from the literature.

The aim of the work presented here is to evaluate the capacity of three models to reproduce the blade loads and near wake velocities of a wind turbine, tested in a wind tunnel during the IEA Wind MexNext project ant to improve a Blade Element Momentum (BEM) model based on the analysis of the results. Two in-house solvers (AeroDeeP, CASTOR), based on BEM and vortex methods, are compared to an open-source CFD Actuator Line solver (CFD-AL) named SOWFA, based on the OpenFOAM toolbox. The main conclusions are given below: the BEM model reach the accuracy of other numerical methods for blade loads, even in yawed conditions. To this end, a new simple model has been introduced. Although the results do not compare perfectly with experimental data, a substantial improvement has been obtained. Wake velocities are well predicted by the vortex solver, thanks to the low diffusion of the methods., Les travaux présentés ici ont pour but d'évaluer la capacité de différents modèles à reproduire le sillage ainsi que les efforts sur les pales d'une éolienne testée en soufflerie dans le cadre du projet MexNext (IEA Wind), et améliorer un modèle BEM (Blade Element Momentum) par l'analyse des résultats. Deux solveurs développés en interne à IFPEN (AeroDeeP, CASTOR), basés sur les méthodes BEM (Blade Element Momentum) et vortex ainsi qu'un modèle CFD-AL (Computational Fluid Dynamics-Actuator Line) open-source, SOWFA, basé sur l'outil OpenFOAM ont été mis en œuvre. Les principaux résultats sont donnés ci-après : la BEM permet d'obtenir des résultats proches des méthodes "avancées", même pour le cas en dérapage. Pour ce faire, un nouveau modèle a été introduit. Bien que les essais ne soient pas parfaitement reproduits, ce modèle simple améliore nettement les résultats. Dans le sillage, la méthode vortex permet d'obtenir des résultats très proches des mesures du fait de la faible diffusion du modèle.

International audience; Aero-elastic solver predictions are compared to measured data from the NENUPHAR's 1HS prototype, with a focus on the blade loads. Two solvers are investigated, namely the HAWC2 solver, and DeepLinesWind TM , respectively based on a linear and a non-linear formulation of the Timoshenko beam theory. Various aerodynamic models are used, from simple Multiple Streamtube models up to the Actuator-Cylinder flow model and 2D/3D Vortex flow solvers. A special attention is also given to the influence of the dynamic stall on the results. Aero-elastic solvers predictions are accurate and fit well with the measured blade loads, but this work emphasizes the fact that suitable aerodynamic model and stall model should be used.

Observations of swell dissipation across oceans reveal a significant loss of energy that can be the result of many of processes. Among these candidate mechanisms, this paper examines the properties of the viscous air-sea boundary layer driven by swells in order to characterize the induced atmospheric flow regime and its associated viscous dissipation over swells. A series of 3-D numerical experiments is carried out with a RANS model and appropriate turbulence closure. These experiments reveal a laminar to turbulent transition in the near free-surface region for a common range of characteristic amplitudes and periods of swells under stationary conditions. At low Reynolds number, laminar conditions prevail and computed decay rates conform to the analytical formulation μν of the Stokes interfacial boundary layer for this problem. The turbulent regimes are characterized as well, and the new decay rates follow a nondimensional relation μ=1.42μν(Re⁡1.5×105)0.41 above Re⁡=1.5×105 (e.g., amplitude larger than 1.1 m for a 14 s monochromatic wave period). Typical decay rates are up to 4 times above the laminar values, which is a factor 10 less than the largest rates estimated for oceanic conditions. A sensitivity analysis is finally conducted to evaluate the influence of the stationary hypothesis. It demonstrates a short setup length and low relative variations of the unsteady decay rates for laminar, transitioning and developed turbulent conditions, which confirms the evaluation of steady decay rates.