LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Xu, Ning; Wan, Decheng

doi:10.3390/su11246939

Cited by 24 publications

(13 citation statements)

References 57 publications

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“…The accuracy of the W DS-predicted loads is conditional to the goodness of the selected shape function to represent velocity deficits. The wake deficit can deviate from a Gaussian pattern as the atmosphere becomes more unstable (Ning and Wan, 2019), whereas it exhibits a double-peak shape in the near-wake region (Keck et al, 2014), and a more complex geometry in a multiple wake scenario. Our results show that combining a physical-based wake deficit formulation with actual lidar measurements of the wake meandering path can potentially reduce the statistical uncertainty of load predictions.…”

Section: Discussionmentioning

confidence: 99%

“…We conduct a spectral analysis on the time-series of MxBR, MxTB, and MzTT, which are highly correlated with the wake meandering (Muller et al, 2015;Moens et al, 2019;Ning and Wan, 2019) and are largely affected by the wake turbulence. The PSD analysis is provided in Appendix A, and shows that neither of the field reconstruction methods shifts the energy content among frequency nor introduce instabilities (i.e.…”

Section: Spectral Coherence Analysis Of Load Predictionsmentioning

confidence: 99%

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Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Conti¹,

Pettas²,

Dimitrov³

et al. 2020

Preprint

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Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists in incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time-series, which are derived by fitting a bivariate Gaussian shape function on lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the Dynamic Wake Meandering (DWM) model. The lidar-reconstructed wake fields are input to aeroelastic simulations of the DTU 10 MW wind turbine and the resulting load predictions are compared with loads obtained with the target (no lidar-based) DWM simulated fields. The accuracy of load predictions is estimated across a variety of lidar beam configurations, probe volume sizes, and atmospheric turbulence conditions. The results indicate that the 10-min power and fatigue load statistics, predicted with lidar-reconstructed fields, are comparable with results obtained with the DWM simulations. Furthermore, the simulated power and load time-series exhibit a high level of correlation with the target observations, thus decreasing the statistical uncertainty (realization-to-realization) by a factor between 1.2 and 5, compared to results obtained with the baseline, which is DWM simulated fields with different random seeds. Finally, we show that the spatial resolutions of the lidar's scanning strategies as well as the size of the probe volume are critical aspects for the accuracy of the reconstructed wake fields and load predictions.

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Section: Discussionmentioning

confidence: 99%

Section: Spectral Coherence Analysis Of Load Predictionsmentioning

confidence: 99%

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Conti¹,

Pettas²,

Dimitrov³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The algorithm for applying constraints on a zero-mean, homogeneous, and isotropic Gaussian random field was developed in Hoffman and Ribak (1991) and extended to Manngenerated turbulence fields for aeroelastic simulations in Nielsen et al (2003) and . The algorithm uses a set of constraints that are here derived from a virtual lidar simulator and an unconstrained random turbulence realization generated with the Mann spectral tensor model.…”

Section: Constrained Gaussian Field Simulationsmentioning

confidence: 99%

“…We conduct a spectral analysis on the time series of MxBR, MxTB, and MzTT, which are highly correlated with the wake meandering (Muller et al, 2015;Moens et al, 2019;Ning and Wan, 2019) and are primarily affected by wake turbulence. The PSD analysis is provided in Appendix A and shows that neither of the wake field reconstruction methods shifts the energy content among frequencies or introduce instabilities (i.e., artificial artifacts).…”

Section: Spectral Coherence Analysis Of Load Predictionsmentioning

confidence: 99%

“…Since the numerical turbulence field and the wake meandering are stochastic processes, the instantaneous velocities of the simulated wake wind field and the resulting load prediction time series are uncorrelated with the observations. This can lead to simulation errors (Zwick and Muskulus, 2015) and introduces high statistical uncertainty into power and load predictions Pedersen et al, 2019). Further, to accurately predict power and loads in a validation analysis, it is essential to accurately reconstruct wake meandering time series.…”

Section: Introductionmentioning

confidence: 99%

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Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

et al. 2021

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Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists of incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time series, which are derived by fitting a bivariate Gaussian shape function to lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the dynamic wake meandering (DWM) model, i.e., the target fields. The lidar-reconstructed wake fields are then input into aeroelastic simulations of the DTU 10 MW wind turbine for carrying out the load validation analysis. The power and load time series, predicted with lidar-reconstructed fields, exhibit a high correlation with the corresponding target simulations, thus reducing the statistical uncertainty (realization-to-realization) inherent to engineering wake models such as the DWM model. We quantify a reduction in power and loads' statistical uncertainty by a factor of between 1.2 and 5, depending on the wind turbine component, when using lidar-reconstructed fields compared to the DWM model results. Finally, we show that the number of lidar-scanned points in the inflow and the size of the lidar probe volume are critical aspects for the accuracy of the reconstructed wake fields, power, and load predictions.

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Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

Rivera‐Arreba,

Wise,

Eliassen

et al. 2023

Wind Energy

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Current global analysis tools for floating wind turbines (FWTs) do not account for the combined effects of atmospheric stability and wakes from neighboring turbines. This work uses the mid‐fidelity dynamic wake meandering model, together with turbulent wind fields generated based on stable, neutral, and unstable atmospheric conditions, to study the low‐frequency content of the global responses of two semisubmersible FWTs separated by eight rotor diameters. Incoming wind fields based on the Kaimal spectrum and exponential coherence model, the Mann spectral tensor model, and a time‐series input‐based turbulence model are used. The respective input parameters for these models are fitted to high‐fidelity large eddy simulation data.In unstable, below‐rated conditions, meandering leads to an increase in the yaw standard deviation of the downwind turbine of almost three times larger than the upwind turbine. Deficit and the upwards wake deflection affect the mean pitch and yaw, especially for the below‐rated wind speed scenario. The mean pitch of the downwind turbine is reduced up to half the mean pitch value of the upwind turbine, whereas the mean yaw changes direction due to the enhanced effect of shear. The effect of meandering on the structural loading is highest on the standard deviation of the tower‐top yaw moment of the downstream turbine, which increases more than 2.2 times compared to the upwind turbine value. Based on these findings, atmospheric stability affects wake deficit and meandering which in turn have a profound effect on the low‐frequency global motions and structural response of floating wind turbines.

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LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Cited by 24 publications

References 57 publications

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

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