Analysis of high Reynolds numbers effects on a wind turbine airfoil using 2D wind tunnel test data

Pires, Oscar; Munduate, Xabier; Ceyhan, O.; Jacobs, Markus; Snel, H.

doi:10.1088/1742-6596/753/2/022047

Cited by 45 publications

(26 citation statements)

References 5 publications

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“…The prediction of the linear slope and the maximum L/D value are comparable with those of the e N -based transition models from Pires et al (2016). At Re = 12×10 6 and Re = 15×10 6 , the two-equation model in HAM2D predicts a lower linear slope than all the reference results and the angle of attack for the maximum L/D is delayed.…”

Section: Du00-w-212 Airfoil -Avatarsupporting

confidence: 68%

“…We performed fully-turbulent and free-transition flow simulations with both transition models at the five different Reynolds numbers in Table 2 and the angles of attack ranging from -4 • to 20 • . Figure 5 shows the comparison of the lift-to-drag ratio and drag polar between the fully turbulent flow simulation results from HAM2D with the experimental data with the tripped boundary layer (Pires et al, 2016). Figure 5 (a) shows that HAM2D overpredicts the maximum lift-to-drag ratio, but the overall trend from the experiment is captured well: the slope in the linear region increases as the Reynolds number increases and the maximum lift-to-drag ratio increases as the Reynolds number increases.…”

Section: Du00-w-212 Airfoil -Avatarmentioning

confidence: 99%

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Local Correlation-based Transition Models for High-Reynolds-Number Wind Turbine Airfoils

Jung

Vijayakumar

Ananthan

et al. 2021

Preprint

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Abstract. Modern wind-turbine airfoil design requires robust performance predictions for varying thicknesses, shapes, and appropriate Reynolds numbers. The airfoils of current large offshore wind turbines operate with chord-based Reynolds numbers in the range of 3–15 million. Turbulence transition in the airfoil boundary layer is known to play an important role in the aerodynamics of these airfoils near the design operating point. While the lack of prediction of lift stall through Reynold-averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) is well-known, airfoil design using CFD requires the accurate prediction of the glide ratio (L / D) in the linear portion of the lift polar. The prediction of the drag bucket and the glide ratio is greatly affected by the choice of the transition model in RANS-CFD of airfoils. We present the performance of two existing local correlation-based transition models – one-equation (γ) and two-equation model coupled with the Spalart-Allmaras (SA) RANS turbulence model – for offshore wind-turbine airfoils operating at a high Reynolds number. We compare the predictions of the two transition models with available experimental and CFD data in the literature in the Reynolds number range of 3–15 million including the AVATAR project measurements of the DU00-W-212 airfoil. Both transition models predict a larger L / D compared to fully turbulent results at all Reynolds numbers. The two models exhibit similar behavior at Reynolds numbers around 3 million. However, at higher Reynolds numbers, the one-equation model fails to predict the natural transition behavior due to early transition onset. The two-equation transition model predicts the aerodynamic coefficients for airfoils of various thickness at higher Reynolds numbers up to 15 million more accurately compared to the one-equation model. The two-equation model also predicts the correct trends with the variation of Reynolds number comparable to the eN transition model. However, a limitation of this model is observed at very high Reynolds numbers of around 12–15 million where the predictions are very sensitive to the inflow turbulent intensity. The combination of the transition model coupled with the Spalart-Allmaras (SA) RANS turbulence model is a robust method for performance prediction of modern wind-turbine airfoils using CFD.

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Section: Du00-w-212 Airfoil -Avatarsupporting

confidence: 68%

Section: Du00-w-212 Airfoil -Avatarmentioning

confidence: 99%

Local Correlation-based Transition Models for High-Reynolds-Number Wind Turbine Airfoils

Jung

Vijayakumar

Ananthan

et al. 2021

Preprint

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show abstract

“…The aerodynamic behavior of airfoil has been measured and the relation between roughness, efficiency results, and the Reynolds number has been associated proving that drag gets reduced while the maximum lift is increased. 22 It is also known that higher Reynolds numbers have, in general for thin airfoils, a positive effect on the blade's roughness sensitivity 23 and this is where this study comes to link it with dust accumulation. It is also proven that the curve of liftdrag coefficients reveals that the roughness is an important influenc- numbers (approx.…”

Section: Qblade and Work Planmentioning

confidence: 86%

On the wind blade's surface roughness due to dust accumulation and its impact on the wind turbine's performance: A heuristic QBlade‐based modeling assessment

Papadopoulou

Alasis

Xydis

2019

Env Prog and Sustain Energy

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The development of wind projects requires good knowledge of energy production that depends on the rotor aerodynamics and its potential of making optimal use of wind power. It has been noted that dust accumulation across the rotor's blade surfaces can seriously affect the overall wind farm (WF) performance and lead to severe energy losses. In the present work, surface roughness that occurs on the wind blade surfaces due to dust accumulation and its possible effect on wind turbine's performance, are the subjects studied. For this purpose, a heuristic modeling assessment was conducted by means of the widely used open source tool QBlade. All available data, pertain to dust accumulation in four different areas, at which, the evolution of the phenomenon and its effect on the aerodynamic performance regarding energy production were studied. It was revealed that for low wind speeds (<10 m/s) no significant effect on the aerodynamic power was observed. This has led all manufacturers, decision makers, and wind farm operators to comprehend phenomena influencing the wind turbine's performance, which were neglected so far.

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“…If tip speed could increase, this would imply an increase in the Reynolds number for the airfoils. There are limited data on the aerodynamic performance of wind turbine airfoils at higher Reynolds numbers, yet the data that are available suggest an improvement in overall aerodynamic performance (Pires et al 2016). Still, more research in this area is needed.…”

Section: Innovations To Reduce Lcoementioning

confidence: 99%

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

Dykes

Veers

Lantz

et al. 2019

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Research advanced erosion-resistant composite materials for use on the leading edge of wind turbine blades Research, development, and scale-up of thermoplastic resin systems to reduce wind turbine blade manufacturing cycle times, reduce blade costs, enable thermal welding of bond lines, increase recyclability, and enable advanced in-field blade repair techniques Research advanced thermoset materials that can be recovered and reused Research metals and other noncomposite materials for development of lightweight, high-strength, and high-stiffness turbine components Grand Challenge 2: Automation Develop comprehensive techno-economic models for automation and a virtual factory (or digital twin of a factory) to understand advantages and disadvantages of targeted automation for both wind turbine blade molding and finishing operations Identify and quantify all cost inputs for wind turbine component manufacturing (e.g., blades, towers) to identify production steps that would benefit from automation Develop automated production methods for the continuous manufacturing of one-piece wind turbine towers for on-site manufacturing Develop advanced design tools to optimize blade and tower design with respect to manufacturing with automation Research and develop automation technology able to locate complex wind turbine blade geometry in space to allow for effective automated finishing operations, such as sanding, drilling, and cutting Develop methods to automate the delivery, placement, and inspection of core material into blade skin molds during the composite laminate skin lay-up Integrate real-time inspection and quality control into automated production steps for wind turbine blades, towers, and other components Develop large-scale, low-cost, high-throughput, high-performance automated additive manufacturing technologies (e.g., 3D printing, automated fiber placement and tape layup, filament winding) for the production of towers, blade skins, blade spars, and other turbine components Develop embedded metrology and out-of-mold indexing technologies Grand Challenge 3: On-Site Manufacturing Develop robust composite materials for use in broad on-site environmental conditions Research the chemistry and processing of in-situ thermoplastic resin systems to enable thermally welded joints in an on-site manufacturing environment Develop targeted wind turbine blade finishing automation techniques designed to reduce the overall footprint of on-site finishing operations and minimize production facility floor space Research additive manufacturing to print wind turbine blade molds and tooling at on-site manufacturing locations IEA Wind TCP Task 11

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Analysis of high Reynolds numbers effects on a wind turbine airfoil using 2D wind tunnel test data

Cited by 45 publications

References 5 publications

Local Correlation-based Transition Models for High-Reynolds-Number Wind Turbine Airfoils

Local Correlation-based Transition Models for High-Reynolds-Number Wind Turbine Airfoils

On the wind blade's surface roughness due to dust accumulation and its impact on the wind turbine's performance: A heuristic QBlade‐based modeling assessment

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

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