Wall Turbulence and Turbulent Drag Reduction

Luchini, Paolo; Quadrio, Maurizio

doi:10.1007/978-3-030-94195-6_22

Cited by 4 publications

(3 citation statements)

References 51 publications

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“…The virtual origin could have a significant impact on the obtained results, especially for large k + (Perry, Schofield & Joubert 1969). Several frameworks have been proposed for the virtual-origin offset, d, such as: the logarithmic origin (Perry & Joubert 1963;Perry et al 1969), the moment of forces acting on the roughness elements (Jackson 1981) or the Reynolds shear stress for transitionally rough surfaces (Luchini 1996;Luchini & Quadrio 2022). Significant effort has been directed towards implementing and improving the previous techniques such as Chan et al (2015), Abderrahaman-Elena, Fairhall & García-Mayoral (2019) and Ibrahim et al (2021).…”

Section: Investigation Of the Virtual Originmentioning

confidence: 99%

Modelling the effect of roughness density on turbulent forced convection

Abu Rowin,

Zhong,

Saurav

et al. 2024

J. Fluid Mech.

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By examining a systematic set of direct numerical simulations, we develop a model which captures the effect of roughness density on global and local heat transfer in forced convection. The surfaces considered are zero-skewed three-dimensional sinusoidal rough walls with solidities, $\varLambda$ (defined as the frontal area divided by the total plan area), ranging from low $\varLambda = 0.09$ , medium $\varLambda = 0.18$ to high $\varLambda = 0.36$ . For each solidity, we vary the roughness height characterised by the roughness Reynolds number, $k^+$ , from transitionally rough to fully rough conditions. The findings indicate that, as the fully rough regime is approached, there is a pronounced breakdown in the analogy between heat and momentum transfer, whereby the velocity roughness function $\Delta U^+$ continues to increase and the temperature roughness function $\Delta \varTheta ^+$ attains a peak with increasing $k^+$ . This breakdown occurs at higher sand-grain roughness Reynolds numbers ( $k_s^+$ ) with increasing solidity. Locally, we find that the heat transfer can be meaningfully partitioned into two categories: exposed, high-shear regions experiencing higher heat transfer obeying a local Reynolds analogy and sheltered, reversed-flow regions experiencing lower and spatially uniform heat transfer. The relative contribution of these distinct mechanisms to the global heat transfer depends on the fraction of the total surface area covered by these regions, which ultimately depends on $\varLambda$ . These insights enable us to develop a model for the rough-wall heat-transfer coefficient, ${C_{h,k}(k^+, \varLambda, Pr)}$ , where $Pr$ is the molecular Prandtl number, that assumes different heat-transfer laws in exposed and sheltered regions. We show that the exposed–sheltered surface-area fractions can be modelled through simple ray tracing that is solely dependent on the surface topography and a prescribed sheltering angle. Model predictions compare well when applied to heat-transfer data of traverse ribs from the literature.

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Section: Investigation Of the Virtual Originmentioning

confidence: 99%

Modelling the effect of roughness density on turbulent forced convection

Abu Rowin,

Zhong,

Saurav

et al. 2024

J. Fluid Mech.

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“…Flow control aims to reduce drag on vehicles, enhance their efficiency, manoeuvrability and possibly modify the heat transfer. Techniques for flow control cover a variety of fields, and have been extensively reviewed (White & Mungal 2008;Dean & Bhushan 2010;Luchini & Quadrio 2022). Flow control devices are usually divided into two groups: passive devices that are fixed in place and do not change their shape or function in time, such as vortex generators (Lin 2002;Koike, Nagayoshi & Hamamoto 2004;Aider, Beaudoin & Wesfreid 2010) and riblets (García-Mayoral & Jiménez 2011Endrikat et al 2021a,b;Modesti et al 2021;Endrikat et al 2022;Rouhi et al 2022), and active devices that can be actuated in some way, such as targeted blowing (Abbassi et al 2017) or intermittent blowing and suction (Segawa et al 2007;Hasegawa & Kasagi 2011;Yamamoto, Hasegawa & Kasagi 2013;Schatzman et al 2014;Kametani et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Turbulent drag reduction by spanwise wall forcing. Part 1. Large-eddy simulations

Rouhi

Chandran

et al. 2023

J. Fluid Mech.

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Turbulent drag reduction (DR) through streamwise travelling waves of the spanwise wall oscillation is investigated over a wide range of Reynolds numbers. Here, in Part 1, wall-resolved large-eddy simulations in a channel flow are conducted to examine how the frequency and wavenumber of the travelling wave influence the DR at friction Reynolds numbers $Re_\tau = 951$ and $4000$ . The actuation parameter space is restricted to the inner-scaled actuation (ISA) pathway, where DR is achieved through direct attenuation of the near-wall scales. The level of turbulence attenuation, hence DR, is found to change with the near-wall Stokes layer protrusion height $\ell _{0.01}$ . A range of frequencies is identified where the Stokes layer attenuates turbulence, lifting up the cycle of turbulence generation and thickening the viscous sublayer; in this range, the DR increases as $\ell _{0.01}$ increases up to $30$ viscous units. Outside this range, the strong Stokes shear strain enhances near-wall turbulence generation leading to a drop in DR with increasing $\ell _{0.01}$ . We further find that, within our parameter and Reynolds number space, the ISA pathway has a power cost that always exceeds any DR savings. This motivates the study of the outer-scaled actuation pathway in Part 2, where DR is achieved through actuating the outer-scaled motions.

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“…Flow control aims to reduce drag on vehicles, enhance their efficiency, manoeuvrability, and possibly modify the heat transfer. Techniques for flow control cover a variety of fields, and have been extensively reviewed (White & Mungal 2008;Dean & Bhushan 2010;Luchini & Quadrio 2022). Flow control devices are usually divided into two groups: passive devices that are fixed in place and do not change their shape or function in time, such as vortex generators (Lin 2002;Koike et al 2004;Aider et al 2010) and riblets (García-Mayoral & Jiménez 2011Endrikat et al 2021a,b), and active devices that can be actuated in some way, such as targeted blowing (Abbassi et al 2017) or intermittent blowing and suction (Segawa et al 2007;Hasegawa & Kasagi 2011;Yamamoto et al 2013;Schatzman et al 2014;Kametani et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Turbulent drag reduction by spanwise wall forcing. Part 1: Large-eddy simulation

Rouhi¹,

Fu²,

Chandran³

et al. 2022

Preprint

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Turbulent drag reduction through streamwise travelling waves of spanwise wall oscillation is investigated over a wide range of Reynolds numbers. Here, in Part 1, wall-resolved large-eddy simulations in a channel flow are conducted to examine how the frequency and wavenumber of the travelling wave influence the drag reduction at friction Reynolds numbers Re τ = 951 and 4000. The actuation parameter space is restricted to the inner-scaled actuation (ISA) pathway, where drag reduction is achieved through direct attenuation of the near-wall scales. The level of turbulence attenuation, hence drag reduction, is found to change with the near-wall Stokes layer protrusion height ℓ 0.01 . A range of frequencies is identified where the Stokes layer attenuates turbulence, lifting up the cycle of turbulence generation and thickening the viscous sublayer; in this range, the drag reduction increases as ℓ 0.01 increases up to 30 viscous units. Outside this range, the strong Stokes shear strain enhances near-wall turbulence generation leading to a drop in drag reduction with increasing ℓ 0.01 . We further find that, within our parameter and Reynolds number space, the ISA pathway has a power cost that always exceeds any drag reduction savings. This motivates the study of the outer-scaled actuation (OSA) pathway in Part 2, where drag reduction is achieved through actuating the outer-scaled motions.

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Wall Turbulence and Turbulent Drag Reduction

Cited by 4 publications

References 51 publications

Modelling the effect of roughness density on turbulent forced convection

Modelling the effect of roughness density on turbulent forced convection

Turbulent drag reduction by spanwise wall forcing. Part 1. Large-eddy simulations

Turbulent drag reduction by spanwise wall forcing. Part 1: Large-eddy simulation

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