13 research outputs found

    Analytical solution for the cumulative wake of wind turbines in wind farms

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    This paper solves an approximate form of conservation of mass and momentum for a turbine in a wind farm array. The solution is a fairly simple explicit relationship that predicts the streamwise velocity distribution within a wind farm with an arbitrary layout. As this model is obtained by solving flow-governing equations directly for a turbine that is subject to upwind turbine wakes, no ad hoc superposition technique is needed to predict wind farm flows. A suite of large-eddy simulations (LES) of wind farm arrays is used to examine self-similarity as well as validity of the so-called conservation of momentum deficit for turbine wakes in wind farms. The simulations are performed with and without the presence of some specific turbines in the wind farm. This allows us to systematically study some of the assumptions made to develop the analytical model. A modified version of the conservation of momentum deficit is also proposed to provide slightly better results at short downwind distances, as well as in the far wake of turbines deep inside a wind farm. Model predictions are validated against the LES data for turbines in both full-wake and partial-wake conditions. While our results highlight the limitation in capturing the flow speed-up between adjacent turbine columns, the model is overall able to acceptably predict flow distributions for a moderately sized wind farm. Finally, the paper employs the new model to provide insights on the accuracy of common wake superposition methods

    Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments

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    We compare wind farm large eddy simulations with the EPFL wind tunnel measurement by Chamorro and Porté-Agel (Bound-Lay. Meteorol. 136, 515 (2010) and Energies 4, 1916 (2011)). We find that the near turbine wake, up to 3 turbine diameters downstream, of a single turbine is captured better with the actuator line method than using the actuator disk method. Further downstream the results obtained with both models agrees very well with the experimental data, confirming findings from previous studies. For large aligned wind farms we find that the actuator disk model predicts the wake profiles behind turbines on the second and subsequent rows more accurately than the wake profile behind the first turbine row. The reason is that the wake layer profile that is created at hub height in very large wind farms is closer to the assumptions made in the actuator disk model than the logarithmic profile found in the inflow conditions. In addition, we show that, even in relatively coarse resolution simulations, adding the effect of the turbine nacelle and tower leads to a significant improvement in the prediction of the near wake features at 1 and 2 diameters downstream

    Wake steering of multirotor wind turbines

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    In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multirotor turbines, a new degree of freedom is introduced to wind farm control such that wakes can be expanded, channelled or redirected to improve inflow conditions for downstream turbines. Five different yaw configurations are investigated (including a baseline case) by employing large‐eddy simulations (LES) to generate a detailed representation of the velocity field downwind of a multirotor wind turbine. Two lower‐fidelity models from single‐rotor yaw studies (curled‐wake model and analytical Gaussian wake model) are extended to the multirotor case, and their results are compared with the LES data. For each model, the wake is analysed primarily by examining wake cross‐sections at different downwind distances. Further quantitative analysis is carried out through characterisations of wake centroids and widths over a range of streamwise locations and through a brief analysis of power production. Most significantly, it is shown that rotor yaw can have a considerable impact on both the distribution and magnitude of the wake velocity deficit, leading to power gains for downstream turbines. The lower‐fidelity models show small deviation from the LES results for specific configurations; however, both are able to reasonably capture the wake trends over a large streamwise range

    Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions

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    © 2016 Author(s). Reducing wake losses in wind farms by deflecting the wakes through turbine yawing has been shown to be a feasible wind farm controls approach. Nonetheless, the effectiveness of yawing depends not only on the degree of wake deflection but also on the resulting shape of the wake. In this work, the deflection and morphology of wakes behind a porous disk model of a wind turbine operating in yawed conditions are studied using wind tunnel experiments and uniform inflow. First, by measuring velocity distributions at various downstream positions and comparing with prior studies, we confirm that the non-rotating porous disk wind turbine model in yaw generates realistic wake deflections. Second, we characterize the wake shape and make observations of what is termed as curled wake, displaying significant spanwise asymmetry. The wake curling observed in the experiments is also reproduced qualitatively in Large Eddy Simulations using both actuator disk and actuator line models. Results suggest that when a wind turbine is yawed for the benefit of downstream turbines, the curled shape of the wake and its asymmetry must be taken into account since it affects how much of it intersects the downstream turbines.status: publishe

    Floating platform effects on power generation in spar and semisubmersible wind turbines

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    Abstract The design and financing of commercial‐scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well‐established fixed‐bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time‐averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5‐MW reference turbine mounted on the OC3‐UMaine spar and OC4‐DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below‐rated turbulent inflow wind from large‐eddy simulations of a neutral atmospheric boundary layer. For these below‐rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind‐downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed‐bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below‐rated wind speeds is proposed, incorporating effects from both the time‐averaged pitch displacement and the dynamic upwind‐downwind displacements

    FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models

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    Annual energy production (AEP) is often the objective function in wind plant layout optimization studies. The conventional method to compute AEP for a wind farm is to first evaluate power production for each discrete wind direction and speed using either computational fluid dynamics simulations or engineering wake models. The AEP is then calculated by weighted-averaging (based on the wind rose at the wind farm site) the power produced across all wind directions and speeds. We propose a novel formulation for time-averaged wake velocity that incorporates an analytical integral of a wake deficit model across every wind direction. This approach computes the average flow field more efficiently, and layout optimization is an obvious application to exploit this benefit. The clear advantage of this new approach is that the layout optimization produces solutions with comparable AEP performance yet is completed 2 orders of magnitude faster. The analytical integral and the use of a Fourier expansion to express the wind speed and wind direction frequency create a relatively smooth solution space for the gradient-based optimizer to excel in comparison to the existing weighted-averaging power calculation

    A new coupling of a GPU-resident large-eddy simulation code with a multiphysics wind turbine simulation tool

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    The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions.Team Jan-Willem van WingerdenWind EnergyEnergie and Industri
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