Calibration of a super-Gaussian wake model with a focus on near-wake characteristics

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.