Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test

Gao, Xiaoxia; Zhang, Shaohai; Li, Luqing; Xu, Shinai; Chen, Yao; Xiao-hong, Zhu; Sun, Haiying; Wang, Yu; Lu, Hao

doi:10.1016/j.energy.2022.124277

Cited by 28 publications

(7 citation statements)

References 30 publications

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“…In order to demonstrate the effectiveness of the Gaussian terrain wake model proposed in this paper, the predicted horizontal and vertical profiles of the model were validated; three recently proposed wake models were selected for comparison to show the improvement of model accuracy. The three wake models were the 3DJG-H model (complex terrain wake model) proposed by Gao et al [29], 3DJGF model (flat terrain wake model) proposed by Gao et al [31], and 3DEG model (flat terrain wake model) proposed by He et al [33].…”

Section: Resultsmentioning

confidence: 99%

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Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

et al. 2022

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Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model that can predict the three-dimensional wind speed distribution of the entire wake region in the complex wind farm, taking into account the Coanda effect, wind shear effect, and wake subsidence under the Gaussian terrain. Two types of Doppler lidar were used to conduct wind field experiments, and the inflow wind profile and three-dimensional expansion of the wake downstream of the wind turbine on the Gaussian terrain were measured. The experimental results showed that the wake centerline and terrain curve showed similar variation characteristics, and the near wake profile was similar to a super-Gaussian shape (asymmetric super-Gaussian shape) under low-wind-speed conditions, while the near wake profile presented a bimodal shape (asymmetric bimodal shape) under high-wind-speed conditions. The predicted profiles of the Gaussian terrain wake model were compared with the experimental data and the three typical wake models. The comparison results showed that the newly proposed Gaussian terrain wake model fit well with the experimental data in both near wake and far wake regions, and it had better performance in predicting the wake speed of the Gaussian terrain wind farm than the other three wake models. It can effectively predict the three-dimensional velocity distribution in the whole wake region of complex terrain.

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Section: Resultsmentioning

confidence: 99%

“…Due to the properties of the Gaussian function, 2.81 standard deviations can reach a probability of 99% in each dimension, so it can be assumed that the characteristic width expression of the wake is as follows [31]:…”

Section: The 3djgf Wake Modelmentioning

confidence: 99%

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

et al. 2022

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“…Piqué et al [16] measured the variation in Reynolds number on the velocity field at different flow positions of a horizontal axis wind turbine in wind tunnel experiments. Gao et al [17] validated the accuracy of the derived wake model through wind tunnel scale experiments and compared it with measurements from actual wind farms. They emphasized the impact of the model wind turbine's geometric shape on experimental results.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

Zhang,

Feng,

Zhao

et al. 2024

JMSE

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During the expansion of a wind farm, the strategic placement of wind turbines can significantly improve wind energy utilization. This study investigates the evolution of wake turbulence in a wind farm after introducing smaller wind turbines within the gaps between larger ones, focusing on aspects such as wind speed, turbulence intensity, and turbulence integral length scale. The flow field conditions are described using parameters like turbulence critical length and power spectral density, as determined through wind tunnel experiments. In these experiments, a single large wind turbine model and nine smaller wind turbine models were used to create a small wind farm unit, and pressure distribution behind the wind turbines was measured under various operating conditions. The results indicate that downstream wind speed deficits intensify as the number of small wind turbines in operation increases. The impact of these smaller turbines varies with height, with a relatively minor effect on the upper blade tip and increasingly adverse effects as you move from the upper blade tip to the lower blade tip. Through an analysis of power spectral density, the contribution of vortex motion to wake turbulence kinetic energy is further quantified. In the far wake region, the number of small wind turbines has a relatively small impact on wind speed fluctuations.

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“…The model gives a specific expression for the wake expansion rate without the need for extensive experimental calculations to determine some empirical parameters. Subsequently, the 3DJG model was improved by Gao et al [25][26][27] considering the velocity distribution characteristics of the near wake, topographic effects, and yawing. Xu et al 28 combined the 3DJG model and OpenFAST to investigate the effect of upstream wind turbine location on the aerodynamic performance of downstream wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al 28 combined the 3DJG model and OpenFAST to investigate the effect of upstream wind turbine location on the aerodynamic performance of downstream wind turbines. In addition, by correcting for the effect of wind shear using the same method as Gao et al, [24][25][26][27] we 29 propose a 3D polynomial-shaped wake model by considering the anisotropic expansion of the wake boundary.…”

Section: Introductionmentioning

confidence: 99%

A three‐dimensional analytical model based on Gaussian distribution for wind‐turbine wakes

Ling,

Zhao,

Liu

et al. 2023

Env Prog and Sustain Energy

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A Gaussian‐shaped, three‐dimensional (3DG‐k) wake model is proposed considering the shear wind effect. The model defines the wake radius rw to be equal to twice the Gaussian standard deviation σ and establishes a link with the expansion coefficient k of the physical wake boundary to describe the wake boundary. Garrad Hassan (GH) wind tunnel test data, Shiren wind farm measurement data, Nibe wind farm measurement data, and Vestas V80‐2MW wind turbine computational data by large eddy simulation method are used to validate the accuracy of the 3DG‐k, Jensen, 3D Gaussian (3DG), and 3D cosine (3D‐C) models. Results show that the 3DG‐k model exhibits higher accuracy for the four cases compared to other models. Due to its simplicity and universality, the model can be used for layout optimization and wake control of wind farms.

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Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test

Cited by 28 publications

References 30 publications

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

A three‐dimensional analytical model based on Gaussian distribution for wind‐turbine wakes

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