scholarly journals A stochastic wind turbine wake model based on new metrics for wake characterization

Wind Energy ◽  
2016 ◽  
Vol 20 (3) ◽  
pp. 449-463 ◽  
Author(s):  
Paula Doubrawa ◽  
Rebecca J. Barthelmie ◽  
Hui Wang ◽  
Matthew J. Churchfield
Keyword(s):  
Wind Turbine ◽  
Model Based ◽  
Wake Model ◽  
Turbine Wake ◽  
Wind Turbine Wake

scholarly journals Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation

2018 ◽  
Vol 10 (5) ◽  
pp. 668 ◽  
Author(s):  
Fernando Carbajo Fuertes ◽  
Corey Markfort ◽  
Fernando Porté-Agel
Keyword(s):  
Wind Turbine ◽  
Model Validation ◽  
Wake Model ◽  
Turbine Wake ◽  
Wind Turbine Wake

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

2020 ◽  
Vol 5 (3) ◽  
pp. 1225-1236 ◽  
Author(s):  
Frédéric Blondel ◽  
Marie Cathelain
Keyword(s):  
Wind Turbine ◽  
Shape Function ◽  
Gaussian Function ◽  
Gaussian Model ◽  
Alternative Form ◽  
Near Wake ◽  
Gaussian Shape ◽  
Wake Model ◽  
Turbine Wake ◽  
Wind Turbine Wake

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 of wind turbine wakes. Using such a shape function allows the recovery of 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 large-eddy simulation (LES), showing a good agreement and an improvement compared with predictions based on the Gaussian model.

Simple Wake Models for Tidal Turbines in Farm Arrangement

2010 ◽  
Author(s):  
Moritz Palm ◽  
Rene Huijsmans ◽  
Mathieu Pourquie ◽  
Anne Sijtstra
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Reference Data ◽  
Energy Output ◽  
Cfd Model ◽  
Tidal Turbines ◽  
Tidal Turbine ◽  
Wake Model ◽  
Turbine Wake ◽  
Wind Turbine Wake

From wind turbines it is known that the wake, induced by a turbine, has a negative impact on the energy production of downstream devices. Basically, the wake is a zone with reduced velocity behind a turbine. Further downstream, the velocity recovers gradually by turbulent mixing with the ambient flow. In order to optimize the design of a tidal farm, the aim of this paper is to find simple relations that can be used to predict the energy output of a given farm configuration. The energy output of a turbine depends on its inflow velocity. Therefore, the strategy is to find a model that is able to predict the velocity field in the tidal farm. Such ‘wake models’ exist already for wind turbines and thruster-thruster interaction. In this research, the applicability of these wake models to tidal turbines is investigated by comparing their results to reference data of tidal turbines. Only limited measurement data for tidal turbines are available; therefore a CFD model of a tidal turbine is used to generate the reference data. The velocity in the wake is simulated for different conditions with the CFD model. The CFD model is validated with the available data in the literature. The velocity in the wake for a single turbine is predicted accurately for different initial conditions. Modeling of the turbulence showed some discrepancies in the far wake, consequently the wake of turbines in farm configurations is predicted less accurate. Three wake models, selected from the literature, are compared to the CFD simulations of the wake behind a single turbine. The wind turbine wake model of Jensen performed best; the velocity in the wake is calculated accurate for different situations. Mutual interaction of wakes will occur inside tidal farms. Several methods from wind turbines theory are used to estimate the velocity in interaction situations. Three basic situations of wake interaction are distinguished: tandem operation, wake interference and overlapping inflow. The interaction methods are tested with CFD reference data for each situation separately. Most methods compared reasonably well; the most suitable interaction methods are selected. A small tidal farm case study is performed to test the combination of wake model and interaction methods. The flow in the cluster of 5 turbines is predicted satisfactorily by the wake model for different inflow velocities. All results indicate that the principle of applying wind turbine wake models to tidal turbine has good potential. However the number of test cases conducted in the thesis is limited and the incorrect turbulence modeling of the CFD model caused some uncertainties for multiple turbine situation.

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

2020 ◽  
Author(s):  
Frédéric Blondel ◽  
Marie Cathelain
Keyword(s):  
Wind Turbine ◽  
Shape Function ◽  
Gaussian Function ◽  
Gaussian Model ◽  
Alternative Form ◽  
Near Wake ◽  
Gaussian Shape ◽  
Wake Model ◽  
Turbine Wake ◽  
Wind Turbine Wake

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.

scholarly journals A Modified Reynolds-Averaged Navier–Stokes-Based Wind Turbine Wake Model Considering Correction Modules

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4430
Author(s):  
Yuan Li ◽  
Zengjin Xu ◽  
Zuoxia Xing ◽  
Bowen Zhou ◽  
Haoqian Cui ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Turbine ◽  
Turbulence Model ◽  
Wind Turbines ◽  
Navier Stokes ◽  
Aerodynamic Model ◽  
Wake Model ◽  
Turbine Wake ◽  
Wind Turbine Wake

Increasing wind power generation has been introduced into power systems to meet the renewable energy targets in power generation. The output efficiency and output power stability are of great importance for wind turbines to be integrated into power systems. The wake effect influences the power generation efficiency and stability of wind turbines. However, few studies consider comprehensive corrections in an aerodynamic model and a turbulence model, which challenges the calculation accuracy of the velocity field and turbulence field in the wind turbine wake model, thus affecting wind power integration into power systems. To tackle this challenge, this paper proposes a modified Reynolds-averaged Navier–Stokes (MRANS)-based wind turbine wake model to simulate the wake effects. Our main aim is to add correction modules in a 3D aerodynamic model and a shear-stress transport (SST) k-ω turbulence model, which are converted into a volume source term and a Reynolds stress term for the MRANS-based wake model, respectively. A correction module including blade tip loss, hub loss, and attack angle deviation is considered in the 3D aerodynamic model, which is established by blade element momentum aerodynamic theory and an improved Cauchy fuzzy distribution. Meanwhile, another correction module, including a hold source term, regulating parameters and reducing the dissipation term, is added into the SST k-ω turbulence model. Furthermore, a structured hexahedron mesh with variable size is developed to significantly improve computational efficiency and make results smoother. Simulation results of the velocity field and turbulent field with the proposed approach are consistent with the data of real wind turbines, which verifies the effectiveness of the proposed approach. The variation law of the expansion effect and the double-hump effect are also given.

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