A New Analytical Wake Model for Yawed Wind Turbines

Qian, Guowei; Ishihara, Takeshi

doi:10.3390/en11030665

Cited by 92 publications

(70 citation statements)

References 26 publications

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“…The spanwise profiles of the normalised streamwise velocity deficit for different yaw angles are shown in Figure 7. LES results from the yawed ADMR are compared with wind-tunnel measurements, predictions from the Gaussian-based analytical wake models [19,22], as well as the LES results using the standard ADM. As shown in Figure 7, the LES results using the yawed ADMR are in good agreement with the experimental data, despite small bias towards larger wake deflection. From 6D downstream, good consistency is found in the velocity deficit profiles between the measurements, the predictions of the Bastankhah-Porté-Agel model and the LES results using the two turbine parameterisations considered here.…”

Section: Spanwise Velocity Deficit and Turbulence Intensity Profilesmentioning

confidence: 99%

“…Such an advantage of using the ADMR in LES has been previously shown in the studies of non-yawed turbines [7,9], but has not been validated in the context of yawed wind turbines. In the following sections, we present the implementation of the yawed rotational actuator disk model in LES and validate it through comparisons of contours and profiles of the main wake flow statistics (the mean velocity deficit and the turbulence intensity) obtained from LES results, wind-tunnel measurements and analytical models [12,19,22]. Furthermore, we evaluate the ability of the yawed ADMR to predict the power output of yawed wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…Shapiro et al [21] developed a lifting line approach to predict the skew angle of the wake in the near-wake region behind a yawed wind turbine, and adopted the same Gaussian profile for the wake velocity deficit in the far wake as in Bastankhah and Porté-Agel [19]. Qian and Ishihara [22] derived a different Gaussian model for the velocity deficit with the assumption of a top-hat distribution for the skew angle, in contrast with the Gaussian distribution for the same quantity in Bastankhah and Porté-Agel [19]. In the same work, Qian and Ishihara [22] formulated a bimodal Gaussian parametric model for the added turbulence intensity in yawed wind…”

mentioning

confidence: 99%

“…Qian and Ishihara [22] derived a different Gaussian model for the velocity deficit with the assumption of a top-hat distribution for the skew angle, in contrast with the Gaussian distribution for the same quantity in Bastankhah and Porté-Agel [19]. In the same work, Qian and Ishihara [22] formulated a bimodal Gaussian parametric model for the added turbulence intensity in yawed wind…”

mentioning

confidence: 99%

See 3 more Smart Citations

Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

Lin

Porté‐Agel

2019

Energies

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In this study, we validated a wind-turbine parameterisation for large-eddy simulation (LES) of yawed wind-turbine wakes. The presented parameterisation is modified from the rotational actuator disk model (ADMR), which takes account of both thrust and tangential forces induced by a wind turbine based on the blade-element theory. LES results using the yawed ADMR were validated with wind-tunnel measurements of the wakes behind a stand-alone miniature wind turbine model with different yaw angles. Comparisons were also made with the predictions of analytical wake models. In general, LES results using the yawed ADMR are in good agreement with both wind-tunnel measurements and analytical wake models regarding wake deflections and spanwise profiles of the mean velocity deficit and the turbulence intensity. Moreover, the power output of the yawed wind turbine is directly computed from the tangential forces resolved by the yawed ADMR, in contrast with the indirect power estimation used in the standard actuator disk model. We found significant improvement in the power prediction from LES using the yawed ADMR over the simulations using the standard actuator disk without rotation, suggesting a good potential of the yawed ADMR to be applied in LES studies of active yaw control in wind farms.Energies 2019, 12, 4574 2 of 18 blade-resolved LES of ABL flows through wind turbines [3]. Therefore, most of the LES studies on wind turbine wakes represent the forces induced by wind turbines in ABL flows with various parameterisations.The standard actuator disk model (ADM) without rotation is the simplest wind turbine parameterisation, in which a wind turbine is modelled as a permeable disk with uniformly-distributed normal thrust forces applied on it [4]. A thrust coefficient C T is required by the standard ADM a priori to determine the magnitude of the thrust force. Wu and Porté-Agel [5] later proposed the rotational ADM parameterisation (ADMR), in which the wind turbine thrust and tangential forces are modelled by the blade-element theory. Using aerodynamic and geometric data of the blade as inputs, the ADMR accounts for the wake rotation and non-uniform force distribution on the wind turbine disk. Comparing to the LES results using the standard ADM, Wu and Porté-Agel [5] found that using the ADMR improves the prediction of the main wake flow statistics, such as the mean streamwise velocity and the turbulence intensity.The actuator line model (ALM) parameterisation, developed by Sørensen and Shen [6], was also applied in previous LES studies of wind turbine wakes (e.g., [7][8][9][10]). The ALM represents each blade of a wind turbine as a rotating line source of body forces and computes the corresponding blade-induced forces along the actuator line dynamically in the simulation. As the ALM computes the forces induced by each wind turbine blade, it can resolve more flow structures in the wake, such as tip vortices, than the standard ADM and the ADMR parameterisations. Using the ALM in LES also requires finer mesh resolution and time ste...

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Section: Spanwise Velocity Deficit and Turbulence Intensity Profilesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

Lin

Porté‐Agel

2019

Energies

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“…Recently, Qian and Ishihara (2018) proposed a modified version of the Bastankhah and Porté-Agel model that improves the velocity deficit prediction in the near wake. In this updated model, a corrective term is added in order to predict realistic near-wake velocities.…”

Section: Introductionmentioning

confidence: 99%

An alternative form of the super-Gaussian wind turbine wake model

Blondel¹,

Cathelain²

2020

Preprint

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Abstract. A new analytical wind turbine wake model, based on a super-Gaussian shape function, is presented. The super-Gaussian function evolves from a nearly top-hat shape in the near wake to a Gaussian shape in the far wake, which is consistent with observations and measurements made on wind turbine wakes. Using such a shape function allows to recover the mass and momentum conservation that is violated when applying a near-wake regularization function to the expression of the maximum velocity deficit of the Gaussian wake model. After a brief introduction of the theoretical aspects, an easy-to-implement model with a limited number of parameters is derived. The super-Gaussian model predictions are compared to wind tunnel measurements, full-scale measurements and a LES simulation, showing a good agreement and an improvement compared with predictions based on the Gaussian model.

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A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields

Lanzilao

Meyers

2021

Wind Energy

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The difference in surface roughness between land and sea, and the terrain complexities, lead to spatially heterogeneous atmospheric conditions, and therefore affect the propagation and dynamics of wind‐turbine and wind‐farm wakes. Currently, these flow heterogeneities and their effects on plant aerodynamics are not modeled in the majority of engineering wake models. In this study, we address this issue by developing a new wake‐merging method capable of superimposing the waked flow on a heterogeneous background velocity field. We couple the proposed wake‐merging method with four different wake models, i.e. the Gaussian, super‐Gaussian, double‐Gaussian and Ishihara model, and we test its performance against LES results, dual‐Doppler radar measurements and SCADA data from the Horns Rev, London Array, and Westermost Rough farms. The standard Jensen model with quadratic superposition is also included. In homogeneous conditions, the new method predicts slightly higher velocity deficits than the linear superposition method. Overall, the distributions of the difference in power ratio between the two wake‐merging methods predictions and observations show a similar mean absolute error (MAE) and interquartile range (IQR) in such conditions. On the other hand, the new wake‐merging method predictions display a lower MAE with a similar IQR in case of a spatially varying background velocity, being overall more accurate than the ones obtained with linear superposition. The most accurate estimates are obtained when the wake‐merging methods are coupled with the double‐Gaussian and Gaussian single‐wake models. In contrast, the Jensen and super‐Gaussian wake models overestimate the velocity deficits for the majority of cases analyzed.

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A New Analytical Wake Model for Yawed Wind Turbines

Cited by 92 publications

References 26 publications

Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

An alternative form of the super-Gaussian wind turbine wake model

A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields

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