scholarly journals An energy-efficient pathway to turbulent drag reduction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ivan Marusic ◽  
Dileep Chandran ◽  
Amirreza Rouhi ◽  
Matt K. Fu ◽  
David Wine ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Drag Reduction ◽  
Large Scale ◽  
Reynolds Numbers ◽  
Small Scale ◽  
Power Cost ◽  
Turbulent Fluid Flow ◽  
Low Reynolds Numbers ◽  
Turbulent Drag ◽  
Direct Measurements

AbstractSimulations and experiments at low Reynolds numbers have suggested that skin-friction drag generated by turbulent fluid flow over a surface can be decreased by oscillatory motion in the surface, with the amount of drag reduction predicted to decline with increasing Reynolds number. Here, we report direct measurements of substantial drag reduction achieved by using spanwise surface oscillations at high friction Reynolds numbers ($${{{\mathrm{Re}}}_{\tau }}$$ Re τ ) up to 12,800. The drag reduction occurs via two distinct physical pathways. The first pathway, as studied previously, involves actuating the surface at frequencies comparable to those of the small-scale eddies that dominate turbulence near the surface. We show that this strategy leads to drag reduction levels up to 25% at $${{{{{{{{\mathrm{Re}}}}}}}}}_{\tau }$$ Re τ = 6,000, but with a power cost that exceeds any drag-reduction savings. The second pathway is new, and it involves actuation at frequencies comparable to those of the large-scale eddies farther from the surface. This alternate pathway produces drag reduction of 13% at $${{{{{{{{\mathrm{Re}}}}}}}}}_{\tau }$$ Re τ = 12,800. It requires significantly less power and the drag reduction grows with Reynolds number, thereby opening up potential new avenues for reducing fuel consumption by transport vehicles and increasing power generation by wind turbines.

An Examination of the Effect of Reynolds Number on Airfoil Performance

2011 ◽  
Author(s):  
Tim Burdett ◽  
Jason Gregg ◽  
Kenneth Van Treuren
Keyword(s):  
Experimental Data ◽  
Reynolds Number ◽  
Wind Turbines ◽  
Large Scale ◽  
Reynolds Numbers ◽  
Energy Sources ◽  
Small Scale ◽  
Number Range ◽  
Low Reynolds Numbers ◽  
Low Reynolds

The standard of living throughout the world has increased dramatically over the last 30 years and is projected to continue to rise. This growth leads to an increased demand on conventional energy sources, such as fossil fuels. However, these are finite resources. Thus, there is an increasing demand for alternative energy sources, such as wind energy. Much of current wind turbine research focuses on large-scale (>1 MW), technologically-complex wind turbines installed in areas of high average wind speed (>20 mph). An alternative approach is to focus on small-scale (1–10kW), technologically-simple wind turbines built to produce power in low wind regions. While these turbines may not be as efficient as the large-scale systems, they require less industrial support and a less complicated electrical grid since the power can be generated at the consumer’s location. To pursue this approach, a design methodology for small-scale wind turbines must be developed and validated. This paper addresses one element of this methodology, airfoil performance prediction. In the traditional design process, an airfoil is selected and published lift and drag curves are used to optimize the blade twist and predict performance. These published curves are typically generated using either experimental testing or a numeric code, such as PROFIL (the Eppler Airfoil Design and Analysis Code) or XFOIL. However, the published curves often represent performance over a different range of Reynolds numbers than the actual design conditions. Wind turbines are typically designed from 2-D airfoil data, so having accurate airfoil data for the design conditions is critical. This is particularly crucial for small-scale, fixed-pitched wind turbines, which typically operate at low Reynolds numbers (<500,000) where airfoil performance can change significantly with Reynolds number. From a simple 2-D approach, the ideal operating condition for an airfoil to produce torque is the angle of attack at which lift is maximized and drag is minimized, so prediction of this angle will be compared using experimental and simulated data. Theoretical simulations in XFOIL of the E387 airfoil, designed for low Reynolds numbers, suggest that this optimum angle for design is Reynolds number dependent, predicting a difference of 2.25° over a Reynolds number range of 460,000 to 60,000. Published experimental data for the E387 airfoil demonstrate a difference of 2.0° over this same Reynolds number range. Data taken in the Baylor University Subsonic Wind Tunnel for the S823 airfoil shows a similar trend. This paper examines data for the E387 and S823 airfoils at low Reynolds numbers (75,000, 150,000, and 200,000 for the S823) and compares the experimental data with XFOIL predictions and published PROFIL predictions.

scholarly journals Reynolds number trend of hierarchies and scale interactions in turbulent boundary layers

2017 ◽  
Vol 375 (2089) ◽  
pp. 20160077 ◽  
Author(s):  
W. J. Baars ◽  
N. Hutchins ◽  
I. Marusic
Keyword(s):  
Reynolds Number ◽  
Large Scale ◽  
Reynolds Numbers ◽  
Shear Layers ◽  
Wall Turbulence ◽  
Small Scale ◽  
Velocity Fluctuations ◽  
Scale Interaction ◽  
High Reynolds ◽  
Near Wall

Small-scale velocity fluctuations in turbulent boundary layers are often coupled with the larger-scale motions. Studying the nature and extent of this scale interaction allows for a statistically representative description of the small scales over a time scale of the larger, coherent scales. In this study, we consider temporal data from hot-wire anemometry at Reynolds numbers ranging from Re τ ≈2800 to 22 800, in order to reveal how the scale interaction varies with Reynolds number. Large-scale conditional views of the representative amplitude and frequency of the small-scale turbulence, relative to the large-scale features, complement the existing consensus on large-scale modulation of the small-scale dynamics in the near-wall region. Modulation is a type of scale interaction, where the amplitude of the small-scale fluctuations is continuously proportional to the near-wall footprint of the large-scale velocity fluctuations. Aside from this amplitude modulation phenomenon, we reveal the influence of the large-scale motions on the characteristic frequency of the small scales, known as frequency modulation. From the wall-normal trends in the conditional averages of the small-scale properties, it is revealed how the near-wall modulation transitions to an intermittent-type scale arrangement in the log-region. On average, the amplitude of the small-scale velocity fluctuations only deviates from its mean value in a confined temporal domain, the duration of which is fixed in terms of the local Taylor time scale. These concentrated temporal regions are centred on the internal shear layers of the large-scale uniform momentum zones, which exhibit regions of positive and negative streamwise velocity fluctuations. With an increasing scale separation at high Reynolds numbers, this interaction pattern encompasses the features found in studies on internal shear layers and concentrated vorticity fluctuations in high-Reynolds-number wall turbulence. This article is part of the themed issue ‘Toward the development of high-fidelity models of wall turbulence at large Reynolds number’.

scholarly journals An Investigation of Factors Influencing the Calibration of 5-Hole Probes for 3-D Flow Measurements

1992 ◽  
Author(s):  
R. G. Dominy ◽  
H. P. Hodson
Keyword(s):  
Reynolds Number ◽  
Large Scale ◽  
Reynolds Numbers ◽  
General Behaviour ◽  
Flow Measurements ◽  
Low Reynolds Numbers ◽  
Additional Information ◽  
Reynolds Number Effects ◽  
Low Speed Wind Tunnel ◽  
Probe Head

The effects of Reynolds number, Mach number and turbulence on the calibrations of commonly used types of 5-hole probe are discussed. The majority of the probes were calibrated at the exit from a transonic nozzle over a range of Reynolds numbers (7×103 < 80×103 based an probe tip diameter) at subsonic and transonic Mach numbers. Additional information relating to the flow structure were obtained from a large scale, low speed wind tunnel. The results confirmed the existence of two distinct Reynolds number effects. Flow separation around the probe head affects the calibrations at relatively low Reynolds numbers while changes in the detailed structure of the flow around the sensing holes affects the calibrations even when the probe is nulled. Compressibility is shown to have little influence upon the general behaviour of these probes in terms of Reynolds number sensitivity but turbulence can effect the reliability of probe calibrations at typical test Reynolds numbers.

An Investigation of Factors Influencing the Calibration of Five-Hole Probes for Three-Dimensional Flow Measurements

1993 ◽  
Vol 115 (3) ◽  
pp. 513-519 ◽  
Author(s):  
R. G. Dominy ◽  
H. P. Hodson
Keyword(s):  
Reynolds Number ◽  
Large Scale ◽  
Three Dimensional ◽  
Reynolds Numbers ◽  
Flow Measurements ◽  
Low Reynolds Numbers ◽  
Additional Information ◽  
Reynolds Number Effects ◽  
Low Speed Wind Tunnel ◽  
Probe Head

The effects of Reynolds number, Mach number, and turbulence on the calibrations of commonly used types of five-hole probe are discussed. The majority of the probes were calibrated at the exit from a transonic nozzle over a range of Reynolds numbers (7 × 103 < Re < 80 × 103 based on probe tip diameter) at subsonic and transonic Mach numbers. Additional information relating to the flow structure were obtained from a large-scale, low-speed wind tunnel. The results confirmed the existence of two distinct Reynolds number effects. Flow separation around the probe head affects the calibrations at relatively low Reynolds numbers while changes in the detailed structure of the flow around the sensing holes affects the calibrations even when the probe is nulled. Compressibility is shown to have little influence upon the general behavior of these probes in terms of Reynolds number sensitivity but turbulence can affect the reliability of probe calibrations at typical test Reynolds numbers.

On the universality of local dissipation scales in turbulent channel flow

2015 ◽  
Vol 786 ◽  
pp. 234-252 ◽  
Author(s):  
S. C. C. Bailey ◽  
B. M. Witte
Keyword(s):  
Reynolds Number ◽  
Channel Flow ◽  
Large Scale ◽  
Isotropic Turbulence ◽  
Turbulent Channel Flow ◽  
Reynolds Numbers ◽  
Small Scale ◽  
Length Scale ◽  
Scaling Behaviour ◽  
Sixth Moment

Well-resolved measurements of the small-scale dissipation statistics within turbulent channel flow are reported for a range of Reynolds numbers from $Re_{{\it\tau}}\approx 500$ to 4000. In this flow, the local large-scale Reynolds number based on the longitudinal integral length scale is found to poorly describe the Reynolds number dependence of the small-scale statistics. When a length scale based on Townsend’s attached-eddy hypothesis is used to define the local large-scale Reynolds number, the Reynolds number scaling behaviour was found to be more consistent with that observed in homogeneous, isotropic turbulence. The Reynolds number scaling of the dissipation moments up to the sixth moment was examined and the results were found to be in good agreement with predicted scaling behaviour (Schumacher et al., Proc. Natl Acad. Sci. USA, vol. 111, 2014, pp. 10961–10965). The probability density functions of the local dissipation scales (Yakhot, Physica D, vol. 215 (2), 2006, pp. 166–174) were also determined and, when the revised local large-scale Reynolds number is used for normalization, provide support for the existence of a universal distribution which scales differently for inner and outer regions.

The effect of Reynolds number on mixing in Kelvin–Helmholtz billows

2014 ◽  
Vol 759 ◽  
pp. 612-641 ◽  
Author(s):  
M. Rahmani ◽  
G. A. Lawrence ◽  
B. R. Seymour
Keyword(s):  
Reynolds Number ◽  
Large Scale ◽  
Three Dimensional ◽  
Reynolds Numbers ◽  
Mixing Efficiency ◽  
Laboratory Measurements ◽  
Flow Properties ◽  
Low Reynolds Numbers ◽  
Vortex Pairing ◽  
High Reynolds

AbstractMixing induced through the life-cycle of Kelvin–Helmholtz (KH) billows is studied for a range of low and intermediate Reynolds numbers using direct numerical simulations (DNS). The amount of stirring, and therefore mixing, is significantly controlled by the process of vortex pairing of two KH billows. For low Reynolds numbers, vortex pairing of the billows is complete in the pre-turbulent stage or early stages of turbulence, generating a high amount of stirring. At higher Reynolds numbers, vortex pairing is suppressed by the growth of three-dimensional instabilities, and the amount of stirring is significantly reduced. For single KH billows, as the Reynolds number increases, there is a transition in the characteristics of the mixing, similar to the laboratory measurements of Breidenthal (J. Fluid Mech., vol. 109, 1981, pp. 1–24) and Koochesfahani & Dimotakis (J. Fluid Mech., vol. 170, 1986, pp. 83–112). The transition in mixing is associated with the growth and sustainability of three-dimensional motions at sufficiently high Reynolds numbers. We examine this ‘mixing transition’ and the influence of vortex pairing on it by examining the flow properties at different stages and the exchange between the energy partitions. As the Reynolds number increases, three-dimensional motions develop over a wider range of length scales, and smaller scale eddies form. However, this does not necessarily result in a greater amount of mixing. The maximum total amount of mixing induced over the lifetime of a KH instability, for billows both with and without vortex pairing, occurs when the large-scale eddies that cause the stirring are the most energetic. The mixing efficiency reveals a non-monotonic dependence on the Reynolds number.

Small Scale Modeling of Vertical Surface Jets in Cross-Flow: Reynolds Number and Downwash Effects

2006 ◽  
Vol 129 (3) ◽  
pp. 311-318 ◽  
Author(s):  
K. Shahzad ◽  
B. A. Fleck ◽  
D. J. Wilson
Keyword(s):  
Reynolds Number ◽  
Large Scale ◽  
Water Channel ◽  
Cross Flow ◽  
Reynolds Numbers ◽  
Vertical Surface ◽  
Small Scale ◽  
Mean Velocity ◽  
Entrainment Coefficient ◽  
Surface Jets

Jet-crossflow experiments were performed in a water channel to determine the Reynolds number effects on the plume trajectory and entrainment coefficient. The purpose was to establish a lower limit down to which small scale laboratory experiments are accurate models of large scale atmospheric scenarios. Two models of a turbulent vertical surface jet (diameters 3.175mm and 12.7mm) were designed and tested over a range of jet exit Reynolds numbers up to 104. The results show that from Reynolds number 200–4000 there is about a 40% increase in the entrainment coefficient, whereas from Reynolds number 4000–10,000, the increase in entrainment coefficient is only 2%. The conclusion is that Reynolds numbers significantly affect plume trajectories when the model Reynolds numbers are below 4000. Changing the initial turbulence in the exit flow from 12% to 2% without changing its mean velocity profile caused a less than one source diameter increase in the final plume rise.

Low Reynolds number flow around and heat transfer from a heated circular cylinder

2010 ◽  
Vol 1 (1-2) ◽  
pp. 15-20 ◽  
Author(s):  
B. Bolló
Keyword(s):  
Reynolds Number ◽  
Circular Cylinder ◽  
Lift Coefficient ◽  
Reynolds Numbers ◽  
Low Reynolds Numbers ◽  
Reynolds Number Flow ◽  
Two Dimensional Flow ◽  
Number Flow ◽  
Low Reynolds ◽  
Increasing Temperature

Abstract The two-dimensional flow around a stationary heated circular cylinder at low Reynolds numbers of 50 < Re < 210 is investigated numerically using the FLUENT commercial software package. The dimensionless vortex shedding frequency (St) reduces with increasing temperature at a given Reynolds number. The effective temperature concept was used and St-Re data were successfully transformed to the St-Reeff curve. Comparisons include root-mean-square values of the lift coefficient and Nusselt number. The results agree well with available data in the literature.

scholarly journals Plunging Airfoil: Reynolds Number and Angle of Attack Effects

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 216
Author(s):  
Emanuel A. R. Camacho ◽  
Fernando M. S. P. Neves ◽  
André R. R. Silva ◽  
Jorge M. M. Barata
Keyword(s):  
Reynolds Number ◽  
High Performance ◽  
Angle Of Attack ◽  
Systems Design ◽  
Reynolds Numbers ◽  
Navier Stokes ◽  
Low Reynolds Numbers ◽  
Operational Conditions ◽  
Naca0012 Airfoil ◽  
The Mean

Natural flight has consistently been the wellspring of many creative minds, yet recreating the propulsive systems of natural flyers is quite hard and challenging. Regarding propulsive systems design, biomimetics offers a wide variety of solutions that can be applied at low Reynolds numbers, achieving high performance and maneuverability systems. The main goal of the current work is to computationally investigate the thrust-power intricacies while operating at different Reynolds numbers, reduced frequencies, nondimensional amplitudes, and mean angles of attack of the oscillatory motion of a NACA0012 airfoil. Simulations are performed utilizing a RANS (Reynolds Averaged Navier-Stokes) approach for a Reynolds number between 8.5×103 and 3.4×104, reduced frequencies within 1 and 5, and Strouhal numbers from 0.1 to 0.4. The influence of the mean angle-of-attack is also studied in the range of 0∘ to 10∘. The outcomes show ideal operational conditions for the diverse Reynolds numbers, and results regarding thrust-power correlations and the influence of the mean angle-of-attack on the aerodynamic coefficients and the propulsive efficiency are widely explored.

scholarly journals Fibre-induced drag reduction

2008 ◽  
Vol 602 ◽  
pp. 209-218 ◽  
Author(s):  
J. J. J. GILLISSEN ◽  
B. J. BOERSMA ◽  
P. H. MORTENSEN ◽  
H. I. ANDERSSON
Keyword(s):  
Experimental Data ◽  
Numerical Simulation ◽  
Reynolds Number ◽  
Drag Reduction ◽  
Elastic Layer ◽  
Turbulent Drag Reduction ◽  
Rigid Polymer ◽  
Fibre Concentration ◽  
Induced Drag ◽  
Turbulent Drag

We use direct numerical simulation to study turbulent drag reduction by rigid polymer additives, referred to as fibres. The simulations agree with experimental data from the literature in terms of friction factor dependence on Reynolds number and fibre concentration. An expression for drag reduction is derived by adopting the concept of the elastic layer.

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