Theory and verification of a new 3D RANS wake model

Abstract. This paper details the background to the WakeBlaster model: a purpose-built, parabolic three-dimensional RANS solver, developed by ProPlanEn. WakeBlaster is a field model, rather than a single turbine model; it therefore eliminates the need for an empirical wake superposition model. It belongs to a class of very fast (a few core seconds, per flow case) mid-fidelity models, which are designed for industrial application in wind farm design, operation, and control. The domain is a three-dimensional structured grid, a node spacing of a tenth of a rotor diameter, by default. WakeBlaster uses eddy viscosity turbulence closure, which is parameterized by the local shear, time-lagged turbulence development, and stability corrections for ambient shear and turbulence decay. The model prescribes a profile at the end of the near wake, and the spatial variation of ambient flow, by using output from an external flow model.

Download Full-text

Theory and Verification of a new 3D RANS Wake Model

10.5194/wes-2020-33 ◽

2020 ◽

Author(s):

Philip Bradstock ◽

Wolfgang Schlez

Keyword(s):

Wind Farm ◽

Three Dimensional ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farms ◽

Ambient Flow ◽

Turbulence Decay ◽

Local Shear ◽

Time Lagged ◽

Wake Model

Abstract. This paper details the background to the WakeBlaster model: a purpose built, parabolic three-dimensional RANS solver, developed by ProPlanEn. WakeBlaster is a field model, rather than a single turbine model; it therefore eliminates the need for an empirical wake superposition model. It belongs to a class of very fast (a few core seconds, per flow case) mid-fidelity models, which are designed for industrial application in wind farm design, operation and control. The domain is a three-dimensional structured grid, with approximately 80 nodes covering the rotor disk, by default. WakeBlaster uses eddy viscosity turbulence closure, which is parameterized by the local shear, time-lagged turbulence development, and stability corrections for ambient shear and turbulence decay. The model prescribes a profile at the end of the near-wake, and the spatial variation of ambient flow, by using output from an external flow model. The WakeBlaster model is verified, calibrated and validated using a large volume of data from multiple onshore and offshore wind farms. This paper presents example simulations for one offshore wind farm.

Download Full-text

A Wake Modeling Paradigm for Wind Farm Design and Control

Energies ◽

10.3390/en12152956 ◽

2019 ◽

Vol 12 (15) ◽

pp. 2956 ◽

Cited By ~ 5

Author(s):

Carl R. Shapiro ◽

Genevieve M. Starke ◽

Charles Meneveau ◽

Dennice F. Gayme

Keyword(s):

Wind Farm ◽

Design Problems ◽

Turbulent Fluctuations ◽

Model Fidelity ◽

Wind Farm Design ◽

Modeling Paradigm ◽

Wake Interactions ◽

Wake Model ◽

And Control ◽

Farm Design

Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications.

Download Full-text

Wind farm layout optimization with a three-dimensional Gaussian wake model

Renewable Energy ◽

10.1016/j.renene.2020.06.003 ◽

2020 ◽

Vol 159 ◽

pp. 553-569 ◽

Cited By ~ 3

Author(s):

Siyu Tao ◽

Qingshan Xu ◽

Andrés Feijóo ◽

Gang Zheng ◽

Jiemin Zhou

Keyword(s):

Wind Farm ◽

Three Dimensional ◽

Layout Optimization ◽

Wind Farm Layout ◽

Wind Farm Layout Optimization ◽

Wake Model

Download Full-text

Optimal Irregular Wind Farm Design for Continuous Placement of Wind Turbines with a Two-Dimensional Jensen-Gaussian Wake Model

Applied Sciences ◽

10.3390/app8122660 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2660 ◽

Cited By ~ 6

Author(s):

Longyan Wang ◽

Yunkai Zhou ◽

Jian Xu

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Power Losses ◽

Wake Effect ◽

Coordinate Method ◽

Wind Farm Design ◽

Wake Model ◽

Farm Design

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.

Download Full-text

Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model

Energy ◽

10.1016/j.energy.2020.118415 ◽

2020 ◽

Vol 209 ◽

pp. 118415

Author(s):

Bingzheng Dou ◽

Timing Qu ◽

Liping Lei ◽

Pan Zeng

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Three Dimensional ◽

Wake Model

Download Full-text

A new three-dimensional wake model for the real wind farm layout optimization

Energy Exploration & Exploitation ◽

10.1177/01445987211056989 ◽

2021 ◽

pp. 014459872110569

Author(s):

Zhaohui Luo ◽

Wei Luo ◽

Junhang Xie ◽

Jian Xu ◽

Longyan Wang

Keyword(s):

Wind Turbine ◽

Power Output ◽

Wind Farm ◽

Three Dimensional ◽

Wind Condition ◽

Wind Farm Layout ◽

Wind Farm Layout Optimization ◽

Optimization Study ◽

Wake Model

The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].

Download Full-text

Three-Dimensional LiDAR Wake Measurements in an Offshore Wind Farm and Comparison with Gaussian and AL Wake Models

Energies ◽

10.3390/en14248313 ◽

2021 ◽

Vol 14 (24) ◽

pp. 8313

Author(s):

Xin Liu ◽

Lailong Li ◽

Shaoping Shi ◽

Xinming Chen ◽

Songhua Wu ◽

...

Keyword(s):

Wind Farm ◽

Eastern China ◽

Three Dimensional ◽

Gaussian Model ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wake Effect ◽

Farm Operators ◽

The Difference ◽

Wake Model

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%.

Download Full-text