A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes

Mingwei Ge, Ying Wu, Yongqian Liu and Qi Li

Applied Energy, 2019, vol. 233-234, 975-984

Abstract: Two-dimensional (2D) wake models with a self-similar Gaussian shape of velocity deficit are highly accurate in predicting wind-turbine wakes. To better leverage their advantages for large-scale engineering applications, an approximation of the physical wake boundary (rw) is proposed based on data from both numerical simulations and experiments, i.e. rw=2σ, where σ is the standard deviation of the Gaussian-like profile. In addition, instead of defining the wake expansion corresponding to σ, a physically more intuitive expansion rate k that corresponds to the physical wake boundary is introduced. Then, a linear expansion law is employed to characterize the evolution of the wake behind the wind rotor, which leaves k as the only parameter to be determined in the 2D wake model. Data from large eddy simulation, physical experiments and field observations shows that the present analytical model can predict the wake of a wind turbine with high accuracy. The cases considered in the present study indicate that k=0.075 recommended by the one-dimensional (1D) wake model (Jensen model) for onshore wind farms also works well in the present 2D model under moderate ground roughness for a turbine generally operating in regime II. Because of its simplicity, good accuracy and low cost, the present 2D model is appealing to large-scale engineering applications.

Keywords: Two-dimensional wake model; Expansion rate of wake boundary; Gaussian shape of velocity deficit; Wind-turbine wakes; 00-01; 99-00 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (49)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261918316817
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:233-234:y:2019:i::p:975-984

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2018.10.110

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().