A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm

Porté‐Agel, Fernando; Wu, Yu Ting; Chen, Chang Hung

doi:10.3390/en6105297

Cited by 258 publications

(189 citation statements)

References 24 publications

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“…The inflow field is obtained by saving the instantaneous velocity components in a specific y-z plane in a similar precursory simulation of ABL over a flat terrain (with the same surface roughness) with no turbine on it. The use of this technique, i.e., using an inflow boundary condition in a direction in which the flow variables are discretized using Fourier series, has been shown to be successful in the works of Tseng et al [22], Wu and Porté-Agel [23] and Porté-Agel et al [24].…”

Section: Numerical Setupmentioning

confidence: 99%

A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer

Shamsoddin

Porté‐Agel

2016

Energies

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Abstract:In a future sustainable energy vision, in which diversified conversion of renewable energies is essential, vertical axis wind turbines (VAWTs) exhibit some potential as a reliable means of wind energy extraction alongside conventional horizontal axis wind turbines (HAWTs). Nevertheless, there is currently a relative shortage of scientific, academic and technical investigations of VAWTs as compared to HAWTs. Having this in mind, in this work, we aim to, for the first time, study the wake of a single VAWT placed in the atmospheric boundary layer using large-eddy simulation (LES). To do this, we use a previously-validated LES framework in which an actuator line model (ALM) is incorporated. First, for a typical three-and straight-bladed 1-MW VAWT design, the variation of the power coefficient with both the chord length of the blades and the tip-speed ratio is analyzed by performing 117 simulations using LES-ALM. The optimum combination of solidity (defined as Nc/R, where N is the number of blades, c is the chord length and R is the rotor radius) and tip-speed ratio is found to be 0.18 and 4.5, respectively. Subsequently, the wake of a VAWT with these optimum specifications is thoroughly examined by showing different relevant mean and turbulence wake flow statistics. It is found that for this case, the maximum velocity deficit at the equator height of the turbine occurs 2.7 rotor diameters downstream of the center of the turbine, and only after that point, the wake starts to recover. Moreover, it is observed that the maximum turbulence intensity (TI) at the equator height of the turbine occurs at a distance of about 3.8 rotor diameters downstream of the turbine. As we move towards the upper and lower edges of the turbine, the maximum TI (at a certain height) increases, and its location moves relatively closer to the turbine. Furthermore, whereas both TI and turbulent momentum flux fields show clear vertical asymmetries (with larger magnitudes at the upper wake edge compared to the ones at the lower edge), only slight lateral asymmetries were observed at the optimum tip-speed ratio for which the simulations were performed.

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Section: Numerical Setupmentioning

confidence: 99%

A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer

Shamsoddin

Porté‐Agel

2016

Energies

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“…The LES code used in this study is a modified version of the one described by Albertson and Parlange 24 , Porté-Agel et al 21 , Stoll and Porté-Agel 14 , Porté-Agel et al 16 , Wu and Porté-Agel 15 and Porté-Agel et al 25 . In the simulations, tuning-free Lagrangian scaledependent dynamic models are used to model the subgrid-scale turbulent fluxes, and the turbine-induced forces are parameterized using the above-mentioned actuator-disk model.…”

Section: Numerical Setupmentioning

confidence: 99%

Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study

2015

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In this study, large-eddy simulation is combined with a turbine model to investigate the influence of atmospheric thermal stability on wind-turbine wakes. The simulation results show that atmospheric stability has a significant effect on the spatial distribution of the mean velocity deficit and turbulence statistics in the wake region as well as the wake meandering characteristics downwind of the turbine. In particular, the enhanced turbulence level associated with positive buoyancy under the convective condition leads to a relatively larger flow entrainment and, thus, a faster wake recovery. For the particular cases considered in this study, the growth rate of the wake is about 2.4 times larger for the convective case than for the stable one. Consistent with this result, for a given distance downwind of the turbine, wake meandering is also stronger under the convective condition compared with the neutral and stable cases. It is also shown that, for all the stability cases, the growth rate of the wake and wake meandering in the vertical direction are smaller compared with the ones in the lateral direction. This is mainly related to the different turbulence levels of the incoming wind in the different directions, together with the anisotropy imposed by the presence of the ground. It is also found that the wake velocity deficit is well characterized by a modified version of a recently proposed analytical model that is based on mass and momentum conservation and the assumption of a self-similar Gaussian distribution of the velocity deficit. Specifically, using a two-dimensional elliptical (instead of axisymmetric) Gaussian distribution allows to account for the different lateral and vertical growth rates, particularly in the convective case where the non-axisymmetry of the wake is stronger. Detailed analysis of the resolved turbulent kinetic energy budget in the wake reveals also that thermal stratification considerably affects the magnitude and spatial distribution of the turbulence production, dissipation and transport terms.

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“…The reason lies in the discreteness and discontinuity of wind direction in the wind modelling. Additionally, in a numerical study of the same WF [13], Porté-Agel et al demonstrated that the power production of a large WF is highly sensitive to wind direction due to the complex wake effects between WTs. These studies suggest that more advanced wind modelling methods that can better characterize wind direction variations are quite needed, especially for application in WF layout optimization.…”

Section: Introductionmentioning

confidence: 99%

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Feng

Shen

2015

Energies

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Abstract:Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distributions of wind speed and wind direction, which is based on the parameters of sector-wise Weibull distributions and interpolations between direction sectors. It is applied to the wind measurement data at Horns Rev and three different joint distributions are obtained, which all fit the measurement data quite well in terms of the coefficient of determination . Then, the best of these joint distributions is used in the layout optimization of the Horns Rev 1 wind farm and the choice of bin sizes for wind speed and wind direction is also investigated. It is found that the choice of bin size for wind direction is especially critical for layout optimization and the recommended choice of bin sizes for wind speed and wind direction is finally presented.

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A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm

Cited by 258 publications

References 24 publications

A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer

A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer

Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

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