A new analytical model for wind-turbine wakes

By M. Bastankhah, Fernando Port'e-Agel. 2013

1,068 citations recorded by the source corpus.

A new analytical wake model is proposed and validated to predict the wind velocity distribution downwind of a wind turbine. The model is derived by applying conservation of mass and momentum and assuming a Gaussian distribution for the velocity deficit in the wake. This simple model only requires one parameter to determine the velocity distribution in the wake. The results are compared to high-resolution wind-tunnel measurements and large-eddy simulation (LES) data of miniature wind-turbine wakes, as well as LES data of real-scale wind-turbine wakes. In general, it is found that the velocity deficit in the wake predicted by the proposed analytical model is in good agreement with the experimental and LES data. The results also show that the new model predicts the power extracted by downwind wind turbines more accurately than other common analytical models, some of which are based on less accurate assumptions like considering a top-hat shape for the velocity deficit.

Subfields: Aerospace Engineering, Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/c0a72ff6e7ab2fecda43e65a2d5861b2c025f331

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Citations in the corpus

Analytical Modeling of Wind Farms: A New Approach for Power Prediction (2016, methodology): “k∗ denotes the wake growth rate which is a function of thrust coefficient and local streamwise turbulence intensity [15].”
Modelling yawed wind turbine wakes: a lifting line approach (2018, methodology): “The average streamwise velocity deficit δu(x, t) is distributed using a Gaussian profile (Bastankhah & Porté-Agel 2014, 2016), which along z= 0 reads u(x, y, t)=U∞ − δu(x, t) D2 8σ 20 exp ( − (y− yc(x, t))2 2σ 2(x) ) , (3.5) 841 R1-9 where the width of the Gaussian σ(x) = σ0dw(x) is proportional to…”
Wind-Turbine and Wind-Farm Flows: A Review (2019, methodology): “Different numerical and experimental data were used by Bastankhah and Porté-Agel (2014b) to show that self-similar Gaussian distribution can acceptably represent velocity-deficit profiles in turbine far-wakes.”
Experimental and theoretical study of wind turbine wakes in yawed conditions (2016, background): “…studies on wakes of spheres (e.g. Legendre, Merle & Magnaudet 2006), cylinders (e.g. Eames, Jonsson & Johnson 2011b) and wind turbines (see Bastankhah & Porté-Agel 2014; Johnson et al. 2014) showed that turbulent far wakes grow approximately linearly with x in the presence of ambient…”