Reynolds number dependence of inner peak turbulence intensity in pipe flow

Ono, Marie; Furuichi, Noriyuki; Wada, Yasuhiro; Kurihara, Noboru; Tsuji, Yoshiyuki

doi:10.1063/5.0084863

Cited by 6 publications

(20 citation statements)

References 41 publications

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“…The middle distance z m depends on the flow. 5,[22][23][24][25][26][27] From our laboratory data (Sec.…”

Section: Formulationmentioning

confidence: 99%

“…1.4-1.9 in pipes. 5,[22][23][24] For the latter, we need to use the pipe radius as the thickness δ of the turbulence region.…”

Section: B Application To Numerical Simulationmentioning

confidence: 99%

“…For the other distances z, we correct the factor K by using u 2 (z) /u 2 * and u 2 (z m ) /u 2 * ≃ 4.5. 5,[22][23][24][25][26][27] The mean flux uw = −u 2 * is to be obtained via Eq. (1b).…”

Section: Introductionmentioning

confidence: 99%

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Momentum flux fluctuations in wall turbulence: A formula beyond the law of the wall

Mouri

Ito

2022

Physics of Fluids

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Within wall turbulence, there is a sublayer where the mean wall-normal flux of the streamwise momentum is constant and related to the logarithmic wall-normal profile of the mean streamwise velocity. This relation, i.e., the law of the wall, has been used to estimate the mean stress at the wall surface. However, the momentum flux exhibits large temporal fluctuations. To relate them theoretically to those of the streamwise velocity at the same position from the wall, we consider an orthogonal decomposition of the fluctuations on a plane of the streamwise and wall-normal velocities. Since a large timescale is expected for the component that would dominate the momentum flux, it is singled out by temporal smoothing. The resultant formula is consistent with time-series data of a boundary layer in a wind tunnel. We also extend the formula to thermally stratified cases.

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“…The middle distance z m depends on the flow. 5,[22][23][24][25][26][27] From our laboratory data (Sec.…”

Section: Formulationmentioning

confidence: 99%

“…1.4-1.9 in pipes. 5,[22][23][24] For the latter, we need to use the pipe radius as the thickness δ of the turbulence region.…”

Section: B Application To Numerical Simulationmentioning

confidence: 99%

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Momentum flux fluctuations in wall turbulence: A formula beyond the law of the wall

Mouri

Ito

2022

Physics of Fluids

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“…The rationale behind (1.1), as discussed by Marusic & Monty (2019), is that the number density of the attached eddies that contribute to turbulent fluctuations varies inversely with y * , and an integration with respect to y * leads to the total fluctuation intensity given by (1.1). Some consequences of this idea have been explored in laboratory measurements (EXP) (Metzger & Klewicki 2001;Hultmark et al 2012;Vincenti et al 2013;Willert et al 2017;Samie et al 2018;Ono et al 2022) as well as direct numerical simulations (DNS) (Wu & Moin 2009;Jimenez et al 2010;Schlatter & Örlü 2010;Lee & Moser 2015;Pirozzoli et al 2021;Hoyas et al 2022;Yao et al 2023). The resulting findings have been discussed in terms of mixed scaling (DeGraaff & Eaton 2000;Diaz-Daniel, Laizet & Vassilicos 2017), the k −1 velocity spectrum (Perry, Henbest & Chong 1986), a multiregime of the power-law spectrum (Vassilicos et al 2015), inner-outer interactions (Marusic, Baars & Hutchins 2017) and a random addictive process (Yang & Lozano-Durán 2017).…”

Section: Introductionmentioning

confidence: 99%

“…2018; Ono et al. 2022) as well as direct numerical simulations (DNS) (Wu & Moin 2009; Jimenez et al. 2010; Schlatter & Örlü 2010; Lee & Moser 2015; Pirozzoli et al.…”

Section: Introductionmentioning

confidence: 99%

Reynolds number asymptotics of wall-turbulence fluctuations

Chen,

Sreenivasan

2023

J. Fluid Mech.

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In continuation of our earlier work (Chen & Sreenivasan, J. Fluid Mech., vol. 908, 2021, R3; Chen & Sreenivasan, J. Fluid Mech., vol. 933, 2022a, A20 – together referred to as CS hereafter), we present a self-consistent Reynolds number asymptotics for wall-normal profiles of variances of streamwise and spanwise velocity fluctuations as well as root-mean-square pressure, across the entire flow region of channel and pipe flows and flat-plate boundary layers. It is first shown that, when normalized by peak values, the Reynolds number dependence and wall-normal variation of all three profiles can be decoupled, in excellent agreement with available data, sharing the common inner expansion of the type $\phi (y^+)=f_0(y^+)+f_1(y^+)/Re^{1/4}_\tau$ , where $\phi$ is one of the quantities just mentioned, the functions $f_0$ and $f_1$ depend only on $y^+$ , and $Re_\tau$ is the friction Reynolds number. Here, the superscript $+$ indicates normalization by wall variables. We show that this result is completely consistent with CS. Secondly, by matching the above inner expansion and the outer flow similarity form, a bounded variation $\phi (y^\ast )=\alpha _\phi -\beta _{\phi }y^{{\ast {1}/{4}}}$ is derived for the outer region where, for each $\phi$ , the constants $\alpha _\phi$ and $\beta _{\phi }$ are independent of $Re_\tau$ and $y^\ast$ $\equiv y^+/Re_\tau$ – also in excellent agreement with simulations and experimental data. One of the predictions of the analysis is that, for asymptotically high Reynolds numbers, a finite plateau $\phi \approx \alpha _\phi$ appears in the outer region. This result sheds light on the intriguing issue of the outer shoulder of the variance of the streamwise velocity fluctuation, which should be bounded by the asymptotic plateau of approximately 10.

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