Influence of the horizontal component of Earth’s rotation on wind turbine wakes

Howland, Michael F.; Ghate, Aditya S.; Lele, Sanjiva K.

doi:10.1088/1742-6596/1037/7/072003

Cited by 14 publications

(17 citation statements)

References 24 publications

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“…In the context of wind turbines, the vertical gradient of the wind speed (wind shear) and wind direction (wind veer) within the ABL and their effect on the wind turbine wake is of special interest [5][6][7][8][9][10][11]. The effect of wind veer resulting from the interaction between the synoptic scale pressure gradients, the Coriolis forces, and surface friction can affect the wake recovery of large wind turbines [12,13]. Stable conditions can enhance this wind veer [14] and strong wind veer can also result from katabatic flows in nocturnal or stable ABLs (e.g., [15,16]).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

et al. 2019

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Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that incorporates the effect of wind veer. Both, measurements and model prediction, show an elliptical and tilted spanwise cross-section of the wake in the presence of wind veer. The error between model and measurements is reduced compared to a model without the effect of wind veer. The characterization of the downwind velocity deficit development and wake growth is robust. The wake tilt angle can only be determined for elliptical wakes.

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Section: Introductionmentioning

confidence: 99%

Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

et al. 2019

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“…Much work, including numerical simulations and field-scale measurements [1][2][3][4][5][6][7][8][9], has been done recently to unravel the impact of lateral wind shear (also known as vertical wind veer) on the aerodynamics of wind turbine wakes. All these studies highlighted that, for the large utility-scale wind turbines, the impact of the lateral wind shear cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes

2018

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Abstract:In this study, an analytical wake model for predicting the mean velocity field downstream of a wind turbine under veering incoming wind is systematically derived and validated. The new model, which is an extended version of the one introduced by Bastankhah and Porté-Agel, is based upon the application of mass conservation and momentum theorem and considering a skewed Gaussian distribution for the wake velocity deficit. Particularly, using a skewed (instead of axisymmetric) Gaussian shape allows accounting for the lateral shear in the incoming wind induced by the Coriolis force. This analytical wake model requires only the wake expansion rate as an input parameter to predict the mean wake flow downstream. The performance of the proposed model is assessed using the large-eddy simulation (LES) data of a full-scale wind turbine wake under the stably stratified condition. The results show that the proposed model is capable of predicting the skewed structure of the wake downwind of the turbine, and its prediction for the wake velocity deficit is in good agreement with the high-fidelity simulation data.

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“…Finally, the present static model does not incorporate the influence of Coriolis forces which have been shown to play a role in the wakes of large wind farms in simulations [20] and field observations [16]. A basic model for the influence of Coriolis forces on the wakes of wind turbines was proposed by [13] but has not been validated in experimental studies and is left for future work.…”

Section: Physics-based Modelmentioning

confidence: 99%

“…As a result, single wind turbine wakes are often modeled as a velocity deficit spread along a Gaussian kernel [7]. While the Gaussian wake profile becomes less clear when wakes merge and are superposed within a wind farm [13], it remains a helpful starting point for modeling [63]. Further, as a result of conservation of mass, the wake will expand downstream of the turbine.…”

Section: Physics-informed Initializationmentioning

confidence: 99%

“…Such wake models show good agreement for single turbines with wind tunnel experiments [7], numerical simulations [8], and field experiments [9]. However, the aerodynamic interactions within wind farm models remain challenging to capture as a result of wake superposition [10], boundary layer interactions [6,11], complex terrain [12], Coriolis forces [13], deep array effects [14,15], and other neglected physics. When wind turbines are tightly spaced in a wind farm, the individual turbine wakes merge into a large wind farm wake [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Howland

Dabiri

2019

Energies

Self Cite

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Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

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Influence of the horizontal component of Earth’s rotation on wind turbine wakes

Cited by 14 publications

References 24 publications

Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

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