Near-wall behavior of turbulent wall-bounded flows

Buschmann, Matthias; Indinger, Thomas; Gad‐el‐Hak, Mohamed

doi:10.1016/j.ijheatfluidflow.2009.06.004

Cited by 44 publications

(30 citation statements)

References 51 publications

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“…5), as well as other statistical turbulence properties. The quantity ' rms u  is maximum at y + ≈13 which is consistent with the results of Spalart.ne As heat transfer is dominated by near-wall flow turbulence activities, it is of critical importance that the near-wall flow structures be accurately resolved by the numerical method [3]. The near-wall region of the boundary layer is dominated by hairpin-like vortical structures and high-and low-speed streaks.…”

Section: Validation Of the Numerical Approachsupporting

confidence: 84%

“…Correlations for downward-flow in vertical tubes and flow in horizontal tubes are scarce, and to the authors' knowledge, no published [3]. A study of these coherent flow structures of turbulence via experimental methods has proven difficult due to the elevated pressures and temperatures typically associated with the supercritical thermodynamic state of substances (e.g., for water T>373.9ºC, p>22.06 MPa).…”

Section: Introductionmentioning

confidence: 99%

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Direct numerical simulation of convective heat transfer in a zero-pressure- gradient boundary layer with supercritical water

2012

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Experimental research has long shown that forced-convective heat transfer in wall-bounded turbulent flows of fluids in the supercritical thermodynamic state is not accurately predicted by correlations that have been developed for single-phase fluids in the subcritical thermodynamic state. In the present computational study, the statistical properties of turbulent flow as well as the development of coherent flow structures in a zero-pressuregradient flat-plate boundary layer are investigated in the absence of body forces, where the working fluid is in the supercritical thermodynamic state. The simulated boundary layers are developed to a friction Reynolds number of 250 for two heat-flux to mass-flux ratios corresponding to cases where normal heat transfer and improved heat transfer are observed. In the case where improved heat transfer is observed, spanwise spacing of the near-wall coherent flow structures is reduced due to a relatively less stable flow environment resulting from the lower magnitudes of the wall-normal viscosity-gradient profile.

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Section: Validation Of the Numerical Approachsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Direct numerical simulation of convective heat transfer in a zero-pressure- gradient boundary layer with supercritical water

2012

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“…It is unclear whether this growth is solely due to a finite Re effect, or it suggests that the viscous scaling is invalid for fluctuations (Degraaff & Eaton 2000;Buschmann, Indinger & Gadelhak 2009) since the inner-outer interactions are very effective (Hutchins & Marusic 2007b;Marusic, Mathis & Hutchins 2010a;McKeon 2017). For the outer peak, McKeon & Sharma (2010) and Moarref et al (2013) developed a 'critical layer' framework to understand its y-location scaling in pipes (argued to scale as Re 2/3 τ ).…”

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confidence: 99%

Quantifying wall turbulence via a symmetry approach. Part 2. Reynolds stresses

2018

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We present new scaling expressions, including high-Reynolds-number (Re) predictions, for all Reynolds stress components in the entire flow domain of turbulent channel and pipe flows. In Part 1 (She et al., J. Fluid Mech., vol. 827, 2017, pp. 322-356), based on the dilation symmetry of the mean Navier-Stokes equation a four-layer formula of the Reynolds shear stress length 12 -and hence also the entire mean velocity profile (MVP) -was obtained. Here, random dilations on the second-order balance equations for all the Reynolds stresses (shear stress −u v , and normal stresses u u , v v , w w ) are analysed layer by layer, and similar four-layer formulae of the corresponding stress length functions 11 , 22 , 33 (hence the three turbulence intensities) are obtained for turbulent channel and pipe flows. In particular, direct numerical simulation (DNS) data are shown to agree well with the four-layer formulae for 12 and 22 -which have the celebrated linear scalings in the logarithmic layer, i.e. 12 ≈ κy and 22 ≈ κ 22 y.

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“…The logarithm law fits velocity and concentration distribution in the position far away from the interface in developed turbulent flow (Wang and Yu, 2007). Some researchers have focused on mean velocity profiles of turbulent wall-bounded flows separately (Poggi et al, 2002;Buschmann and Gad-el-Hak, 2007;Kempe et al, 2007;Buschmann et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Mean velocity and suspended sediment concentration profile model of turbulent shear flow with probability density function

Zhu

2017

Earth sci. res. j.

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This work purposes a general mean velocity and a suspended sediment concentration (SSC) model to express distribution in every point of the cross section of turbulent shear flow by using a probability density function method. In order to solve turbulent flow and avoid multifarious dynamical mechanics, the probability density function method was used to describe the velocity and concentration profiles interacted on directly by fluid particles in turbulent shear flow. The velocity profile model was obtained by solving for the profile integral with the product of the laminar velocity and probability density, through adopting an exponential probability density function to express probability distribution of velocity alteration of a fluid particle in turbulent shear flow. An SSC profile model was also created following a method similar to the above and based on the Schmidt diffusion equation. Different velocity and SSC profiles were created while changing the parameters of the models. The models were verified by comparing the calculated results with traditional models. It was shown that the probability density function model was superior to log-law in predicting stream-wise velocity profiles in coastal currents; and the probability density function SSC profile model was superior to the Rouse equation for predicting average SSC profiles in rivers and estuaries. Outlooks for precision investigation are stated at the end of this article.ABSTRACT

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Near-wall behavior of turbulent wall-bounded flows

Cited by 44 publications

References 51 publications

Direct numerical simulation of convective heat transfer in a zero-pressure- gradient boundary layer with supercritical water

Direct numerical simulation of convective heat transfer in a zero-pressure- gradient boundary layer with supercritical water

Quantifying wall turbulence via a symmetry approach. Part 2. Reynolds stresses

Mean velocity and suspended sediment concentration profile model of turbulent shear flow with probability density function

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