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Research Papers

Effect of Wake Flow Nonuniformity on Wind Turbine Performance and Aerodynamics

[+] Author and Article Information
S. Barber

e-mail: sarah.barber@gmx.ch

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 July 19, 2011; final manuscript received August 1, 2011; published online November 6, 2012. Editor: David Wisler.

J. Turbomach 135(1), 011012 (Nov 06, 2012) (9 pages) Paper No: TURBO-11-1149; doi: 10.1115/1.4006334 History: Received July 19, 2011; Revised August 01, 2011

Dynamically scaled experiments and numerical analyses are performed to study the effects of the wake from an upstream wind turbine on the aerodynamics and performance of a downstream wind turbine. The experiments are carried out in the dynamically scaled wind turbine test facility at ETH Zurich. A five-hole steady-state probe is used to characterize the cross-sectional distribution of velocity at different locations downstream of the wake-generating turbine. The performance of the downstream wind turbine is measured with an in-line torquemeter. The velocity field in the wind turbine wake is found to differ significantly from the velocity field assumed in numerical wake models. The velocity at hub height does not increase monotonically up to the freestream velocity with downstream distance in the wake. Furthermore, the flowfield is found to vary significantly radially and azimuthally. The application of wake models that assume a constant axial velocity profile in the wake based on the measured hub-height velocity can lead to errors in annual energy production predictions of the order of 5% for typical wind farms. The application of wake models that assume an axisymmetric Gaussian velocity profile could lead to prediction errors of the order of 20%. Thus modeling wind turbine wakes more accurately, in particular by accounting for radial variations correctly, could increase the accuracy of annual energy production predictions by 5%–20%.

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References

Figures

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Fig. 2

ETH Zurich dynamically scaled wind turbine test facility: (a) schematic diagram and (b) tandem turbine arrangement

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Fig. 4

(a) Head geometry of 5H-SSP and (b) aero calibration coefficients

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Fig. 5

Grid for plane measurements

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Fig. 6

Flowchart of the unsteady BEM method

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Fig. 7

Unsteady BEM code predictions compared to NREL PROPID for (a) power versus velocity and (b) power coefficient versus tip speed ratio

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Fig. 10

(a) Normalized axial velocity for a plane 2D downstream and (b) variation with radial position at 0 deg, 30 deg, and 90 deg

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Fig. 11

(a) Normalized axial velocity for a plane 11D downstream and (b) variation with radial position at 0 deg, 30 deg, and 90 deg

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Fig. 15

Schematic diagram of time-averaged flow over truncated cylinder [23]

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Fig. 14

Vorticity components in the axial direction at (a) 2D and (b) 11D downstream in the wake

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Fig. 17

Summary of errors in magnitudes of power and annual energy productions predictions due to common wake model assumptions

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Fig. 13

Radial and azimuthal velocity components at (a) 2D and (b) 11D downstream in the wake

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Fig. 12

Normalized radial and azimuthal velocity components at (a) 2D and (b) 11D downstream in the wake

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Fig. 16

Average incidence over rotor for one revolution: 2D compared to 11D

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Fig. 9

Normalized velocity versus axial downstream distance in the wake of the upstream wind turbine

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Fig. 8

Normalized power versus axial downstream distance in the wake of the upstream wind turbine for fixed-speed compared to variable-speed downstream wind turbines

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Fig. 3

CP versus tip speed ratio curve for the downstream wind turbine in freestream conditions

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Fig. 1

(a) Normalized axial velocity in the wake for Jensen, Frandsen, and WAsP models; (b) ECN model velocity profile compared to measurements; and (c) ECN model showing development of velocity in the wake compared to measurements [6]

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