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Investigation of wind turbine wake superposition models using Reynolds‐averaged Navier‐Stokes simulations

Abstract:
It is well accepted that the wakes created by upstream turbines significantly impact on the power production and fatigue loading of downstream turbines and that this phenomenon affects wind farm performance. Improving the understanding of wake effects and overall efficiency is critical for the optimisation of layout and operation of increasingly large wind farms. In the present work, the NREL 5‐MW reference turbine was simulated using blade element embedded Reynolds‐averaged Navier‐Stokes computations in sheared onset flow at three spatial configurations of two turbines at and above rated flow speed to evaluate the effects of wakes on turbine performance and subsequent wake development. Wake recovery downstream of the rearward turbine was enhanced due to the increased turbulence intensity in the wake, although in cases where the downstream turbine was laterally offset from the upstream turbine this resulted in relatively slower recovery. Three widely used wake superposition models were evaluated and compared with the simulated flow‐field data. It was found that when the freestream hub‐height flow speed was at the rated flow speed, the best performing wake superposition model varied depending according to the turbine array layout. However, above rated flow speed where the wake recovery distance is reduced, it was found that linear superposition of single turbine velocity deficits was the best performing model for all three spatial layouts studied.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/we.2444

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Edmund Hall
Role:
Author


Publisher:
Wiley
Journal:
Wind Energy More from this journal
Volume:
23
Issue:
3
Pages:
593-607
Publication date:
2019-11-26
Acceptance date:
2019-10-07
DOI:
EISSN:
1099-1824
ISSN:
1095-4244


Language:
English
Keywords:
Pubs id:
pubs:1063122
UUID:
uuid:9aa26a8c-d216-42cb-b4a7-1a73bbfa7f63
Local pid:
pubs:1063122
Source identifiers:
1063122
Deposit date:
2019-10-15

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