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Implicit large eddy simulations for the actuator line method of wind turbine

Abstract:
This paper evaluates the effects of the Sub-grid Stress (SGS) model on wind turbine simulations using the Actuator Line Method (ALM), taking into account numerical discretisation error, SGS modelling error and blade modelling error. The investigation covers the implicit Large Eddy Simulation (iLES) model, the Wall-adapting Local Eddy-viscosity (WALE) model, and the Smagorinsky model (SM). A detailed analysis of the aerodynamic loads and wake behaviour of the NTNU benchmarking turbine Blind Test 1 is conducted, with uniform laminar and turbulent inlets imposed at varying grid resolutions, both with and without nacelle geometry. A turbulent inlet triggers an earlier transition and results in faster wake recovery. The explicit SGS model is not necessary for predicting total power coefficients and aerodynamic loadings as blade modelling errors are qualitatively dominant. Under a turbulent inlet, the time-averaged velocity is insensitive to SGS models on both coarse and fine grids. For resolved turbulent kinetic energy, the WALE model with a coarse grid provides slightly better resolution than the iLES and SM models in the far wake region. In conclusion, iLES is workable for the ALM of wind turbines.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1088/1742-6596/3224/4/042021

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author


Publisher:
IOP Publishing
Journal:
Journal of Physics: Conference Series More from this journal
Volume:
3224
Issue:
4
Pages:
042021
Article number:
042021
Publication date:
2026-05-01
DOI:
EISSN:
1742-6596
ISSN:
1742-6588


Language:
English
Source identifiers:
4091401
Deposit date:
2026-05-28
ARK identifier:
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