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Research Papers

Simulation of Wake Interactions in Wind Farms Using an Immersed Wind Turbine Model

[+] Author and Article Information
S. Jafari

Laboratory for Energy Conversion,
Department of Mechanical and
Process Engineering,
ETH Zurich,
Zurich, Switzerland
e-mail: jafari@lec.mavt.ethz.ch

N. Chokani, R. S. Abhari

Laboratory for Energy Conversion,
Department of Mechanical and
Process Engineering,
ETH Zurich,
Zurich, Switzerland

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received August 19, 2013; final manuscript received September 16, 2013; published online November 28, 2013. Editor: Ronald Bunker.

J. Turbomach 136(6), 061018 (Nov 28, 2013) (7 pages) Paper No: TURBO-13-1193; doi: 10.1115/1.4025762 History: Received August 19, 2013; Revised September 16, 2013

The accurate modeling of the wind turbine wakes in complex terrain is required to accurately predict wake losses. In order to facilitate the routine use of computational fluid dynamics in the optimized micrositing of wind turbines within wind farms, an immersed wind turbine model is developed. This model is formulated to require grid resolutions that are comparable to that in microscale wind simulations. The model in connection with the k-ω turbulence model is embedded in a Reynolds-averaged Navier–Stokes solver. The predictions of the model are compared to available wind tunnel experiments and to measurements at the full-scale Sexbierum wind farm. The good agreement between the predictions and measurements demonstrates that the novel immersed turbine model is suited for the optimized micrositing of wind turbines in complex terrain.

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Figures

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Fig. 1

Schematic of the immersed wind turbine model superposed on the Cartesian grid. The immersed body is a streamtube around the rotor plane of a wind turbine. The predicted near-wake velocity field is mapped at the outlet plane (plane O) onto the RANS computational domain. The far-wake region is resolved on the computational grid by the RANS solver.

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Fig. 2

Predicted velocity profiles downstream of single turbine compared to measurements in wind tunnel for three operating conditions (a) TSR = 2.9, (b) TSR = 4.0, and (c) TSR = 5.1

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Fig. 3

Predicted turbulence intensity profiles downstream of a single turbine compared to measurements in a wind tunnel for three operating conditions (a) TSR = 2.9, (b) TSR = 4.0, and (c) TSR = 5.1

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Fig. 4

Layout of the Sexbierum wind farm located in the north of the Netherlands

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Fig. 5

The view of the computational grid with local clustering in the z direction and around the immersed turbine used for the single wake simulations at the Sexbierum wind farm

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Fig. 6

Predictions of the velocity profiles at two positions downstream of a full-scale wind turbine compared to measurements. (a) 2.5D and (b) 8D.

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Fig. 7

Predictions of the turbulence intensity at two positions downstream of a full-scale wind turbine compared to measurements. (a) 2.5D and (b) 8D.

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Fig. 8

Side view of contour plots of wind speed and turbulent kinetic energy downstream of the turbine in operation in an atmospheric boundary layer simulated using IWTM

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Fig. 9

Plan view of contour plots of wind speed showing the wake interactions for two different wind directions

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Fig. 10

Power versus wind direction compared to measurements for two downstream turbines, T37 and T36, at the Sexbierum wind farm

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