Effects of blade number on the aerodynamic performance and wake characteristics of a small horizontal-axis wind turbine

Zhang, Hongfu; Wen, Jiahao; Jin, Zuanming; Xin, Dabo

doi:10.1016/j.enconman.2022.116410

Cited by 15 publications

(7 citation statements)

References 36 publications

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“…The mesh was refined in the rotating and wake regions of the wind turbine. Additionally, boundary layers were added to the blade surfaces, with the first layer height set to 0.02 mm with a growth rate of 1.2, comprising 15 layers, to ensure y + < 1 [26,27]. This setup aimed to effectively capture the flow details and turbulence variations while ensuring a smooth mesh motion.…”

Section: Mesh Generation and Turbulence Modelmentioning

confidence: 99%

Impact of Blade-Flapping Vibration on Aerodynamic Characteristics of Wind Turbines under Yaw Conditions

Liu,

Gao,

et al. 2024

ENERGY

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Although the aerodynamic loading of wind turbine blades under various conditions has been widely studied, the radial distribution of load along the blade under various yaw conditions and with blade flapping phenomena is poorly understood. This study aims to investigate the effects of second-order flapwise vibration on the mean and fluctuation characteristics of the torque and axial thrust of wind turbines under yaw conditions using computational fluid dynamics (CFD). In the CFD model, the blades are segmented radially to comprehensively analyze the distribution patterns of torque, axial load, and tangential load. The following results are obtained. (i) After applying flapwise vibration, the torque and axial thrust of wind turbines decrease in relation to those of the rigid model, with significantly increased fluctuations. (ii) Flapwise vibration causes the blades to reciprocate along the axial direction, altering the local angle of attack and velocity of the blades relative to the incoming wind flow. This results in the contraction of the torque region from a circular shape to a complex "gear" shape, which is accompanied by evident oscillations. (iii) Compared to the tangential load, the axial load on the blades is more sensitive to flapwise vibration although both exhibit significantly enhanced fluctuations. This study not only reveals the impact of flapwise vibration on wind turbine blade performance, including the reduction of torque and axial thrust and increased operational fluctuations, but also clarifies the radial distribution patterns of blade aerodynamic characteristics, which is of great significance for optimizing wind turbine blade design and reducing fatigue risks.

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Section: Mesh Generation and Turbulence Modelmentioning

confidence: 99%

Impact of Blade-Flapping Vibration on Aerodynamic Characteristics of Wind Turbines under Yaw Conditions

Liu,

Gao,

et al. 2024

ENERGY

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“…From a meteorological perspective, atmospheric stability, wind speed, and turbulence intensity are major influencing factors [5,17]. Meanwhile, parameters related to turbine design and operation, such as rotor diameter, tower height, blade pitch, and turbine control strategies, can significantly modulate wake characteristics [18,19]. Understanding these factors is paramount for predicting wake behavior and optimizing wind farm performance.…”

Section: Wind Turbine Wake Aerodynamicsmentioning

confidence: 99%

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Gajendran,

Kabir,

Vadivelu

et al. 2023

JMSE

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As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction.

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“…The Water Analysis Simulation Program (WAsP) linear model can be used to estimate the vertical and horizontal distribution of wind speeds in a region [10], but it does not accurately reflect ground friction and thermal effects for regions of complex terrain. Large eddy simulation (LES) in Computational fluid Dynamics(CFD) can accurately capture the details of the flow field [11,12] and is also widely used in the simulation of wind farms in complex terrain [13,14]. However, since CFD models, whether in simulating wind fields or assessing wind energy, rely solely on predetermined parameter values and do not account for time-varying meteorological conditions, the impact of meteorological factors on wind farms cannot be adequately represented within the framework of CFD models.…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

Che,

Zhou,

Wang

et al. 2024

Sustainability

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In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.

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Effects of blade number on the aerodynamic performance and wake characteristics of a small horizontal-axis wind turbine

Cited by 15 publications

References 36 publications

Impact of Blade-Flapping Vibration on Aerodynamic Characteristics of Wind Turbines under Yaw Conditions

Impact of Blade-Flapping Vibration on Aerodynamic Characteristics of Wind Turbines under Yaw Conditions

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

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