A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes

He, Ruiyang ORCID: https://orcid.org/0000-0002-9643-9485, Yang, Hongxing, Sun, Haiying and Gao, Xiaoxia 2021. A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes. Applied Energy 296 , 117059. 10.1016/j.apenergy.2021.117059

Full text not available from this repository.

Abstract

The development of a more advanced three-dimensional wake model for wind power generation is presented based on a multivariate Gaussian distribution. The newly-presented model is closer to reality as it truly depends on two independent dimensions (namely horizontal and vertical directions) rather than the radius of a circle. For this reason, the general expression of wake expansion rate in each dimension is specifically developed. In addition, by taking into account the inflow wind shear effect, this current model is able to accurately capture the asymmetric distribution of the vertical wake profile. Four cases including experimental data from wind tunnels and field observations as well as high-fidelity numerical simulation are used to validate the present model. Compared with conventional models, this new model is capable of predicting the wake distribution of a single wind turbine reasonably well. The proposed model is highly simple with a low computational cost. Before applying this model, no additional numerical calculation or trial calculation is required. Wake velocity at any given spatial position can be calculated in an accurate and fast manner. Because of its accuracy, universality and low cost, the present three-dimensional wake model is able to make contributions to farm-level applications such as layout optimization and control strategies and therefore benefit the power output of wind farms.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of Acceptance:	2 May 2021
Last Modified:	11 Oct 2024 08:30
URI:	https://orca.cardiff.ac.uk/id/eprint/172289

Actions (repository staff only)

Edit Item