Experimental investigation of Surface Roughness effects and Transition on Wind Turbine performance

Pires, Oscar; Munduate, Xabier; Boorsma, K.; Yilmaz, Özlem Ceyhan; Madsen, Helge Aagaard; Timmer, W.A.

doi:10.1088/1742-6596/1037/5/052018

Cited by 20 publications

(20 citation statements)

References 2 publications

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“…Sandpaper was used to emulate the roughness state on the airfoil leading-edge similarly to the work of Pires et al [20]. The sandpaper was attached to the first 8%c on both airfoil sides.…”

Section: Roughness Emulation 30% Thick Airfoilmentioning

confidence: 99%

Induced stalled flow due to roughness sensitivity for thick airfoils in modern wind turbines

Gutierrez

Llorente

Ragni

2022

J. Phys.: Conf. Ser.

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The mid-span region of wind turbine blades can be thickened to fulfil the structural requirements of the blade. Hence, thick airfoils, that were designed to operate at the root region of the blade, are moved to the mid-span region. This could not imply remarkable variations of the blade performance once its surface is smooth. However, the sensitivity of thick airfoils to roughness could cause significant aerodynamic impacts such as flow separation. This research aims to quantify the impact of the blade thickness, under smooth and rough conditions, in the annual energy production and the fatigue loads of the blade. Ten blade designs, linearly interpolated in thickness, are studied employing aero-elastic computations. The results reveal that the thickest blade increases the annual energy production by 5% with respect to the thinnest blade under rough conditions. Whereas this increase is less than 1% under smooth conditions. The loss of annual energy production varies with the blade thickness linearly for thin blades while it varies exponentially for thick blades up to 22%. Fatigue loads assessment confirmed a reduction of the damage equivalent load under smooth conditions, whereas the thickest blade increased it 28% under rough conditions.

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“…Sandpaper was used to emulate the roughness state on the airfoil leading-edge similarly to the work of Pires et al [20]. The sandpaper was attached to the first 8%c on both airfoil sides.…”

Section: Roughness Emulation 30% Thick Airfoilmentioning

confidence: 99%

Induced stalled flow due to roughness sensitivity for thick airfoils in modern wind turbines

Gutierrez

Llorente

Ragni

2022

J. Phys.: Conf. Ser.

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“…This work presents 2D CFD computations of a NACA63418 airfoil with distributed roughness on its surface together with BEM computations for a 2.5 MW virtual rotor in clean and rough configurations to obtain the annual energy production (AEP) losses due to roughness. The results are compared to 2D wind tunnel test data and wind turbine field experiments performed within the IRPWind Joint Experiments European Call [1]. The comparison shows good results for small size roughness but the CFD computations under-predict the effect for higher size roughness.…”

Section: Introductionmentioning

confidence: 99%

Impact of high size distributed roughness elements on wind turbine performance

Méndez

Pires

2022

J. Phys.: Conf. Ser.

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“…Bubbles are a sensitive phenomenon and small changes to boundary conditions can make them disappear completely (Ward, 1963). Inflow turbulence, leading-edge surface erosion, fouling or ice will often cause forced transition (Pires et al, 2018). Earlier transition will tend to reduce or remove bubbles (Ward, 1963).…”

Section: Introductionmentioning

confidence: 99%

Cartographing dynamic stall with machine learning

et al. 2020

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Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

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Experimental investigation of Surface Roughness effects and Transition on Wind Turbine performance

Cited by 20 publications

References 2 publications

Induced stalled flow due to roughness sensitivity for thick airfoils in modern wind turbines

Induced stalled flow due to roughness sensitivity for thick airfoils in modern wind turbines

Impact of high size distributed roughness elements on wind turbine performance

Cartographing dynamic stall with machine learning

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