Extracting the angle of attack on rotor blades from CFD simulations

Jost, Eva; Klein, Levin; Leipprand, Hagen; Lutz, Thorsten; Krämer, Ewald

doi:10.1002/we.2196

Cited by 48 publications

(47 citation statements)

References 17 publications

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“…• The Line average (LineAve) method determines the AoA by averaging the flow velocities along a symmetric, closed line around the rotor blade [38]. In the present study, a circle was chosen for this purpose as illustrated in Figure 6.…”

Section: Control Point Locusmentioning

confidence: 99%

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Evaluation of different methods for determining the angle of attack on wind turbine blades with CFD results under axial inflow conditions

et al. 2018

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This work presents an investigation on different methods for the calculation of the angle of attack and the underlying induced velocity on wind turbine blades using data obtained from three-dimensional Computational Fluid Dynamics (CFD). Several methods are examined and their advantages, as well as shortcomings, are presented. The investigations are performed for two 10MW reference wind turbines under axial inflow conditions, namely the turbines designed in the EU AVATAR and INNWIND.EU projects. The results show that the evaluated methods are in good agreement with each other at the mid-span, though some deviations are observed at the root and tip regions of the blades. This indicates that CFD results can be used for the calibration of induction modeling for Blade Element Momentum (BEM) tools. Moreover, using any of the proposed methods, it is possible to obtain airfoil characteristics for lift and drag coefficients as a function of the angle of attack. Nomenclature ρ Air density [kg m −3 ] Γ Circulation [m 2 s −1 ]

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Section: Control Point Locusmentioning

confidence: 99%

“…By averaging the flow velocities along the circle, the influence of bound circulation is eliminated and the local inflow velocity and AoA can be determined. Figure 6: Line average method -2D and 3D [38].…”

Section: Control Point Locusmentioning

confidence: 99%

Evaluation of different methods for determining the angle of attack on wind turbine blades with CFD results under axial inflow conditions

et al. 2018

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“…This uncertainty complicates the estimation of the radially varying dynamic inflow time constants. Jost et al (2018) While the approach for loads with respect to the rotor-plane coordinate system such as distributed driving force or thrust is straightforward, a comparison of aerodynamic characteristics is more challenging.…”

Section: Influence Of Pitching Directionmentioning

confidence: 99%

Dynamic inflow effects in measurements and high-fidelity computations

Pirrung

Madsen

2018

Wind Energ. Sci.

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Abstract.A wind turbine experiences an overshoot in loading after, for example, a collective step change in pitch angle. This overshoot occurs because the wind turbine wake does not immediately reach its new equilibrium, an effect usually referred to as dynamic inflow. Vortex cylinder models and actuator disc simulations predict that the time constants of this dynamic inflow effect should decrease significantly towards the blade tip. As part of the NASA Ames Phase VI experiment, pitch steps have been performed on a turbine in controlled conditions in the wind tunnel. The measured aerodynamic forces from these experiments seemed to show much less radial dependency of the dynamic inflow time constants than expected when pitching towards low loading. Moreover the dynamic inflow effect seemed fundamentally different when pitching from low to high loading, and the reason for this behavior remained unclear in previous analyses of the experiment. High-fidelity computational fluid dynamics and free-wake vortex code computations yielded the same behavior as the experiments. In the present work these observations from the experiments and high-fidelity computations are explained based on a simple vortex cylinder wake model.

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“…20,21,29,30 An advantage of the k − SST model in particular is that the formulation presented by Menter 25 performs well for wake studies using the standard model coefficients, avoiding the need for calibration of model parameters for each application. Hence, the k − SST turbulence model has become increasingly popular for wind and tidal turbine applications 15,20,22,29,31,32 and is employed in this study.…”

Section: Turbulence Modelmentioning

confidence: 99%

Investigation of wind turbine wake superposition models using Reynolds‐averaged Navier‐Stokes simulations

Vogel

Willden

2019

Wind Energy

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It is well accepted that the wakes created by upstream turbines significantly impact on the power production and fatigue loading of downstream turbines and that this phenomenon affects wind farm performance. Improving the understanding of wake effects and overall efficiency is critical for the optimisation of layout and operation of increasingly large wind farms. In the present work, the NREL 5-MW reference turbine was simulated using blade element embedded Reynolds-averaged Navier-Stokes computations in sheared onset flow at three spatial configurations of two turbines at and above rated flow speed to evaluate the effects of wakes on turbine performance and subsequent wake development. Wake recovery downstream of the rearward turbine was enhanced due to the increased turbulence intensity in the wake, although in cases where the downstream turbine was laterally offset from the upstream turbine this resulted in relatively slower recovery. Three widely used wake superposition models were evaluated and compared with the simulated flow-field data. It was found that when the freestream hub-height flow speed was at the rated flow speed, the best performing wake superposition model varied depending according to the turbine array layout. However, above rated flow speed where the wake recovery distance is reduced, it was found that linear superposition of single turbine velocity deficits was the best performing model for all three spatial layouts studied. KEYWORDS blade element model, computational fluid dynamics, Reynolds-averaged Navier-Stokes simulation, wind turbine wake INTRODUCTIONThe size of wind turbines, and the number of turbines in wind farms, has grown significantly over the past few decades as part of the transition towards increased electricity production from renewable energy sources. It has been observed that there are significant power losses when turbines operate in the wake of upstream turbines, with a reduction in time-averaged power on the order of 10% to 20%. 1 The reduction in power is the consequence of a number of factors, including interturbine spacing and wind direction. 2 Turbines operating in the turbulent wakes of upstream turbines experience lower average flow speeds, and hence reduced power production, and also encounter increased flow unsteadiness, leading to higher fatigue loads. Consequently, it has become increasingly important to understand the aerodynamic interactions that arise between turbines in wind farms. These interactions can be particularly important in the offshore environment, where the low levels of ambient turbulence and the lack of topographical features can result in turbine wakes persisting for many rotor diameters downstream.Modelling wind turbine wakes within farms is challenging because the wakes evolve and merge with neighbouring wakes in a flow that is different to the freestream flow. Wind turbine wakes are often characterised in terms of two regions: the near wake and the far wake. The near wake region starts immediately downwind of the turbine and extends 2 to 4...

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Extracting the angle of attack on rotor blades from CFD simulations

Cited by 48 publications

References 17 publications

Evaluation of different methods for determining the angle of attack on wind turbine blades with CFD results under axial inflow conditions

Evaluation of different methods for determining the angle of attack on wind turbine blades with CFD results under axial inflow conditions

Dynamic inflow effects in measurements and high-fidelity computations

Investigation of wind turbine wake superposition models using Reynolds‐averaged Navier‐Stokes simulations

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