Reynolds Number Effect on the Optimization of a Wind Turbine Blade for Maximum Aerodynamic Efficiency

Ge, Mingwei; Tian, De; Ying, Deng

doi:10.1061/(asce)ey.1943-7897.0000254

Cited by 26 publications

(17 citation statements)

References 20 publications

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“…Park et al [9] showed that the power coefficient of a wind turbine is very sensitive to the change in the model scale, but the thrust coefficient is not. Ge et al [10] selected six airfoils and found they all exhibited better performance at a higher Reynolds number. Li et al [11] studied the Reynolds number effect on four thick airfoils at two different wind tunnels and found that increasing Re makes the separation point move towards the leading edge.…”

Section: Introductionmentioning

confidence: 99%

Wake Effect of a Horizontal Axis Wind Turbine on the Performance of a Downstream Turbine

et al. 2019

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This paper presents wind tunnel tests on the wake characteristics of a three-blade horizontal axis wind turbine and the wake effect on the performance of a downstream turbine. For a single turbine model, the performance was determined and this was followed by measurement of the wind characteristics including velocities, turbulence intensities, and correlation in the wake flow field. Subsequently, taking two horizontal axis wind turbines in a tandem arrangement into account, their performance was tested and the aerodynamic mechanism was discussed. The results showed that the upstream turbine with blades set at a small pitch angle provided smaller disturbance to the flow, but as the blade turned faster, larger changes in the velocity and the turbulence intensity occurred in its wake due to the more frequent disturbance of the wind turbine. The correlation of wake velocities in the turbine swept area also obviously decreased from the free-stream situation. For the downstream turbine, the output power loss largely depended on the wake characteristics of the upstream turbine, which was closely related to lower wind velocities or higher turbulence intensities. The decrease in correlation of the streamwise velocity within the blade swept area is accompanied by the increased correlation of the tangential velocity, which may be beneficial to the downstream turbine’s performance.

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Section: Introductionmentioning

confidence: 99%

Wake Effect of a Horizontal Axis Wind Turbine on the Performance of a Downstream Turbine

et al. 2019

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“…In order to simulate the operational conditions of multi-MW wind turbine airfoils found in the inboard region of the blade [4,107], Re = 6 × 10 6 and M a = 0.1 was used in XFOIL. Furthermore, an N-crit number of 9, representing typical wind tunnel turbulence values, was used for all simulations.…”

Section: Design and Optimization Parametersmentioning

confidence: 99%

“…The forces recorded by the force sensor were divided by the dynamic pressure multiplied by the airfoil area to obtain the non-dimensional coefficients. The drag force multiplied by 2, as calculated by the wake momentum method, was divided by the velocity squared multiplied by the chord (see Equation 4.3) since it was only measured in a plane. The uncertainty in the wake method must account for the fact that the integration in Equation 4.3 is performed by summing the velocity and geometric measurements therefore the associated uncertainty is the combination of the 10 (typically) measurements within the wake.…”

Section: Lift and Drag Forcesmentioning

confidence: 99%

“…Since A = πr 2 , the aerodynamic power is shown to increase with the square of the rotor radius. Furthermore, the increased Reynolds numbers that come with the larger chord lengths of longer blades generally results in better aerodynamic performance and in turn, slightly superior aerodynamic efficiency of the wind turbine [4,5]. These long wind turbine blades, sometimes exceeding 80 m [6], have introduced several structural challenges due to the increased weight and aerodynamic forces acting on the blade.…”

Section: Introductionmentioning

confidence: 99%

“…2: Sample L16 Taguchi orthogonal array consisting of 4 parameters (A, B, C, D) at 4 different levels(1,2,3,4) …”

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The Multi-Objective Design of Flatback Wind Turbine Airfoils

Miller¹

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With the ever increasing lengths of today's wind turbine rotor blades, there is a need for airfoils which are both aerodynamically, and structurally efficient. In this work, a multi-objective genetic algorithm coupled with XFOIL was developed to design flatback wind turbine airfoils. The effect of the aerodynamic evaluator, specifically lift-to-drag ratio, torque, and torque-to-thrust ratio, on the airfoil shape and performance was examined. Under the specified set of constraints and objectives, notable differences, particularly in the levels of lift and roughness insensitivity, were observed.Further analysis, employing the Taguchi method, was performed to determine how various parameters impact the design outcome. The obtained knowledge was used to design a wind turbine specific airfoil family which has comparable, or superior structural and aerodynamic performance as compared to airfoils found in the literature. A wind tunnel experimental set-up was developed at the Carleton University LowSpeed Wind Tunnel for the 2D testing of airfoils. Good agreement between the results obtained at Carleton University and other publicly available data indicates that the set-up is capable of producing meaningful data. A select airfoil, designed by the current author, was tested at Carleton University and its performance is compared to the numerical predictions of XFOIL. Differences in the stall and post-stall regions of the XFOIL predictions are highlighted, and emphasize the importance of wind tunnel testing in the airfoil design process.iii

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Robust control of wind turbines to reduce wind power fluctuation

Tang,

Wang,

Zhen

et al. 2024

Energy Science & Engineering

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The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.

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Reynolds Number Effect on the Optimization of a Wind Turbine Blade for Maximum Aerodynamic Efficiency

Cited by 26 publications

References 20 publications

Wake Effect of a Horizontal Axis Wind Turbine on the Performance of a Downstream Turbine

Wake Effect of a Horizontal Axis Wind Turbine on the Performance of a Downstream Turbine

The Multi-Objective Design of Flatback Wind Turbine Airfoils

Robust control of wind turbines to reduce wind power fluctuation

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