4.7 Article

Advancement of an analytical double-Gaussian full wind turbine wake model

Journal

RENEWABLE ENERGY
Volume 171, Issue -, Pages 687-708

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.02.078

Keywords

Wind turbine; Wake model; Wake deficit; Double-Gaussian; Analytical model; Aerodynamics

Ask authors/readers for more resources

The recently proposed analytical wake model for a horizontal axis utility scale wind turbine is revisited, revised and improved based on conservation of momentum and the assumption of a double-Gaussian distribution for the velocity deficit in the wake. The model fitting using lidar wake measurement data reveals characteristics of the wind turbine wake, potentially facilitating analytic calculations and improving understanding of wind turbine aerodynamics.
A recently proposed analytical wake model for a horizontal axis utility scale wind turbine is revisited, and revised and improved. The model is based upon conservation of momentum in the context of actuator disc theory, and the assumption of a distribution of the double-Gaussian type for the velocity deficit in the wake. The model is developed and improved and reveals characteristics of the wind turbine wake velocity deficit for the full wake, including the near-wake to within close proximity of the wind turbine rotor. Full 2-dimensional model fitting to lidar wake measurement data obtained from a 5 MW utility scale wind turbine is carried out for the full range of inflow wind velocities of primary interest. Such a full wind turbine wake model has the potential to facilitate analytic calculations within the wind turbine wake region, and the potential to improve understanding of wind turbine aerodynamics. (c) 2021 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available