Large Eddy Simulations of Curled Wakes from Tilted Wind Turbines

2021 ◽  
Author(s):  
Hannah M. Johlas ◽  
David P. Schmidt ◽  
Matthew A. Lackner
Keyword(s):  
Wind Turbines ◽  
Large Eddy Simulations ◽  
Large Eddy

Large eddy simulations of the flow past wind turbines: actuator line and disk modeling

Wind Energy ◽  
2014 ◽  
Vol 18 (6) ◽  
pp. 1047-1060 ◽  
Author(s):  
Luis A. Martínez-Tossas ◽  
Matthew J. Churchfield ◽  
Stefano Leonardi
Keyword(s):  
Wind Turbines ◽  
Large Eddy Simulations ◽  
Flow Past ◽  
Large Eddy

scholarly journals How does turbulence change approaching a rotor?

2017 ◽  
Author(s):  
Jakob Mann ◽  
Alfredo Peña ◽  
Niels Troldborg ◽  
Søren J. Andersen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Low Frequency ◽  
Large Eddy Simulations ◽  
Thrust Coefficient ◽  
Disk Model ◽  
Actuator Disk ◽  
Large Eddy ◽  
Rotor Support ◽  
Induction Zone

Abstract. For load calculations on wind turbines it is usually assumed that the turbulence approaching the rotor does not change its statistics as it goes through the induction zone. We investigate this assumption using a nacelle-mounted forward-looking pulsed lidar that measures low frequency wind fluctuations simultaneous at distances between one half and three rotor diameters upstream. The measurements show that below rated wind speed the low-frequency wind variance is reduced by up to 10 % at one half rotor diameter upstream and above rated enhanced by up to 20 %. A quasi-steady model that takes into account the change of thrust coefficient with wind speed explains these variations partly. Large-eddy simulations of turbulence approaching an actuator disk model of a rotor support the finding that the slope of the thrust curve influences the low-frequency fluctuations.

scholarly journals Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 41
Author(s):  
Zexia Zhang ◽  
Christian Santoni ◽  
Thomas Herges ◽  
Fotis Sotiropoulos ◽  
Ali Khosronejad
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Velocity Fields ◽  
Large Eddy Simulations ◽  
Wake Flow ◽  
Surface Model ◽  
Flow Conditions ◽  
Large Eddy ◽  
Turbine Wake

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES.

scholarly journals How does turbulence change approaching a rotor?

2018 ◽  
Vol 3 (1) ◽  
pp. 293-300 ◽  
Author(s):  
Jakob Mann ◽  
Alfredo Peña ◽  
Niels Troldborg ◽  
Søren J. Andersen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Low Frequency ◽  
Large Eddy Simulations ◽  
Thrust Coefficient ◽  
Disk Model ◽  
Actuator Disk ◽  
Large Eddy ◽  
Rotor Support ◽  
Induction Zone

Abstract. For load calculations on wind turbines it is usually assumed that the turbulence approaching the rotor does not change its statistics as it goes through the induction zone. We investigate this assumption using a nacelle-mounted forward-looking pulsed lidar that measures low-frequency wind fluctuations simultaneously at distances between 0.5 and 3 rotor diameters upstream. The measurements show that below rated wind speed the low-frequency wind variance is reduced by up to 10 % at 0.5 rotor diameters upstream and above rated enhanced by up to 20 %. A quasi-steady model that takes into account the change in thrust coefficient with wind speed explains these variations partly. Large eddy simulations of turbulence approaching an actuator disk model of a rotor support the finding that the slope of the thrust curve influences the low-frequency fluctuations.

scholarly journals Optimal control of energy extraction in wind-farm boundary layers

2015 ◽  
Vol 768 ◽  
pp. 5-50 ◽  
Author(s):  
Jay P. Goit ◽  
Johan Meyers
Keyword(s):  
Boundary Layer ◽  
Optimal Control ◽  
Boundary Layers ◽  
Wind Turbines ◽  
Wind Farm ◽  
Large Eddy Simulations ◽  
Energy Extraction ◽  
Reynolds Stresses ◽  
Turbulent Dissipation ◽  
Large Eddy

In very large wind farms, the vertical interaction with the atmospheric boundary layer plays an important role, i.e. the total energy extraction is governed by the vertical transport of kinetic energy from higher regions in the boundary layer towards the turbine level. In the current study, we investigate optimal control of wind-farm boundary layers, considering the individual wind turbines as flow actuators, whose energy extraction can be dynamically regulated in time so as to optimally influence the flow field and the vertical energy transport. To this end, we use large-eddy simulations of a fully developed pressure-driven wind-farm boundary layer in a receding-horizon optimal control framework. For the optimization of the wind-turbine controls, a conjugate-gradient optimization method is used in combination with adjoint large-eddy simulations for the determination of the gradients of the cost functional. In a first control study, wind-farm energy extraction is optimized in an aligned wind farm. Results are accumulated over one hour of operation. We find that the energy extraction is increased by 16 % compared to the uncontrolled reference. This is directly related to an increase of the vertical fluxes of energy towards the wind turbines, and vertical shear stresses increase considerably. A further analysis, decomposing the total stresses into dispersive and Reynolds stresses, shows that the dispersive stresses increase drastically, and that the Reynolds stresses decrease on average, but increase in the wake region, leading to better wake recovery. We further observe also that turbulent dissipation levels in the boundary layer increase, and overall the outer layer of the boundary layer enters into a transient decelerating regime, while the inner layer and the turbine region attain a new statistically steady equilibrium within approximately one wind-farm through-flow time. Two additional optimal control cases study penalization of turbulent dissipation. For the current wind-farm geometry, it is found that the ratio between wind-farm energy extraction and turbulent boundary-layer dissipation remains roughly around 70 %, but can be slightly increased by a few per cent by penalizing the dissipation in the optimization objective. For a pressure-driven boundary layer in equilibrium, we estimate that such a shift can lead to an increase in wind-farm energy extraction of 6 %.

Effects of the Subgrid-Scale Modeling in the Large-Eddy Simulations of Wind Turbines

2017 ◽  
pp. 109-115 ◽  
Author(s):  
U. Ciri ◽  
M. V. Salvetti ◽  
K. Carrasquillo ◽  
C. Santoni ◽  
G. V. Iungo ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Large Eddy Simulations ◽  
Subgrid Scale ◽  
Scale Modeling ◽  
Large Eddy ◽  
Subgrid Scale Modeling
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