Comparison of Engineering Wake Models with CFD Simulations

Andersen, Søren Juhl; Sørensen, Jens Nørkær; Ivanell, Stefan; Mikkelsen, Robert Flemming

doi:10.1088/1742-6596/524/1/012161

Cited by 34 publications

(32 citation statements)

References 6 publications

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“…Typically, all these processes are computationally intensive and apply low-order models of turbine wakes (Elkinton et al, 2006;Chowdhury et al, 2012), such as the Jensen model (Jensen, 1983;Katic et al, 1986). In this way, several scenarios can be tested, but these lower-cost models oversimplify reality and may not be capable to fully represent wake characteristics in a detailed and realistic way (Barthelmie et al, 2006;Andersen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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“…Currently, there is a wide variety of models to calculate, with different accuracy levels, the wake losses inside wind farms [31][32][33][34][35][36][37]. Examples of wake losses models to calculate the wind deficits due to wakes inside wind farms include the Jensen model (also known as Park model) [38], Eddy viscosity model [39], Frandsen et al model [40], deep-array wake model [41] and the Larsen model [42].…”

Section: Wake Losses Modelingmentioning

confidence: 99%

“…The CFD models solve the parabolized NavierStokes equations which that may take various hours to compute in a large wind farm. Examples of CFD models are the Simulator for Offshore Wind Farm Applications (SOWFA), a modular and open-source tool developed by NREL [46], the EllipSys3D developed by DTU and Risø [31] and FarmFlow developed by the Energy Research Centre of the Netherlands (ECN) [47]. Finally, high-resolution models based on large eddy simulations (LES models) offer the highest fidelity but may take several weeks to complete [48].…”

Section: Wake Losses Modelingmentioning

confidence: 99%

“…For example, Andersen et al [31] conducted a comparison of different engineering wake models (including the Jensen model) and CFD simulations. They demonstrated that the highest discrepancies between the Jensen and the CFD models were observed around distances of 4-8 rotor diameters and for higher thrust coefficients, which is in line with the results obtained in this paper.…”

Section: Farmflowmentioning

confidence: 99%

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Wake losses optimization of offshore wind farms with moveable floating wind turbines

Rodrigues

Pinto

Soleimanzadeh

et al. 2015

Energy Conversion and Management

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a b s t r a c tIn the future, floating wind turbines could be used to harvest energy in deep offshore areas where higher wind mean speeds are observed. Currently, several floating turbine concepts are being designed and tested in small scale projects; in particular, one concept allows the turbine to move after installation. This article presents a novel layout optimization framework for wind farms composed of moveable floating turbines. The proposed framework uses an evolutionary optimization strategy in a nested configuration which simultaneously optimizes the anchoring locations and the wind turbine position within the mooring lines for each individual wind direction. The results show that maximum energy production is obtained when moveable wind turbines are deployed in an optimized layout. In conclusion, the framework represents a new design optimization tool for future offshore wind farms composed of moveable floating turbines.

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“…Barthelmie concluded that the spread of the wake model predictions is considerable even for these relative simple offshore single wake cases. Recent study also has compared two analytical wake models (Jensen and Frandsen) with CFD (computational fluid dynamics) simulations [5]. It assesses and attempts to link the model performance in the different scenarios (single wind turbine, long row of turbines and infinite wind farm), then provides new calibration of parameters for the engineering models.…”

mentioning

confidence: 99%

Qualification of Three Analytical Wake Models

Charhouni¹,

Sallaou²,

Arbaoui³

2016

JMEA

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Abstract:The decrease of wind velocity (wake losses) in downstream area of wind turbine is generally quantified using wake models. The overall estimated power of wind farm varies according to reliability of wake model used, however it's unclear which model is most appropriate and able to give a high performance in predicting wind velocity deficit. In this subject, a qualification of three analytical wake models (Jensen, Ishihara and Frandsen) based on three principal criteria is presented in this paper: (i) the parsimony which characterizes the inverse of model complexity, (ii) the accuracy of estimation in which wake model is compared with the experimental data and (iii) imprecision that is related to assumptions and uncertainty on the value of variables considered in each model. This qualitative analysis shows the inability of wake models to predict wind velocity deficit due to the big uncertainty of variables considered and it sensitivity to wind farm characteristic.

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Comparison of Engineering Wake Models with CFD Simulations

Cited by 34 publications

References 6 publications

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Wake losses optimization of offshore wind farms with moveable floating wind turbines

Qualification of Three Analytical Wake Models

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