Introduction

Fatica, Massimiliano; Ruetsch, Gregory

doi:10.1016/b978-0-12-416970-8.00001-8

Cited by 2 publications

(2 citation statements)

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“…The governing equations are advanced in time by means of a hybrid third-order low-storage Runge–Kutta algorithm, whereby the diffusive terms are handled implicitly, and convective terms are handled explicitly. The code was adapted to run on clusters of graphic accelerators (GPUs), using a combination of CUDA Fortran and OpenACC directives, and relying on the CUFFT libraries for efficient execution of fast Fourier transforms (Ruetsch & Fatica 2014; Pirozzoli et al. 2021).…”

Section: Methodsmentioning

confidence: 99%

Searching for the log law in open channel flow

Pirozzoli

2023

J. Fluid Mech.

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We carry out direct numerical simulations of flow in a plane open channel at friction Reynolds number up to ${{Re}}_{\tau } \approx 6000$ . We find solid evidence for the presence of universal large-scale organization in the outer layer, with eddies that are larger and stronger than in the closed channel flow. As a result, velocity fluctuations are found to be stronger than in closed channels, throughout the depth. The inner-layer peak of the streamwise velocity variance is observed to grow logarithmically, as in Townsend's attached-eddy model (Townsend, The Structure of Turbulent Shear Flow, 2nd edn, Cambridge University Press, 1976), but saturation of the growth cannot be discarded based on the present data. Although we do not observe a clear outer peak of the streamwise velocity variance, we present substantial evidence that such a peak should emerge at a Reynolds number barely higher than achieved herein. The most striking feature of the flow is the presence of an extended logarithmic layer, with associated Kármán constant asymptoting to $k \approx 0.375$ , in line with observations made in shear-free Couette–Poiseuille flow (Coleman et al., Flow Turbul. Combust., vol. 99, issue 3, 2017, pp. 553–564). The virtual absence of a wake region and of corrective terms to the log law in the present flow leads us to conclude that deviations from the log law observed in internal flows are likely due to the effects of the opposing walls, rather than the presence of a driving pressure gradient.

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Section: Methodsmentioning

confidence: 99%

Searching for the log law in open channel flow

Pirozzoli

2023

J. Fluid Mech.

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“…In order to minimize numerical errors associated with implicit time stepping, explicit and implicit discretizations of the azimuthal convective terms are blended linearly with the radial coordinate, in such a way that near the pipe wall, the treatment is fully explicit, and near the pipe axis, it is fully implicit. The code was adapted to run on clusters of graphic accelerators (GPUs), using a combination of CUDA Fortran and OpenACC directives, and relying on the CUFFT libraries for efficient execution of fast Fourier transforms (Ruetsch & Fatica 2014).…”

Section: The Numerical Datasetmentioning

confidence: 99%

Prandtl number effects on passive scalars in turbulent pipe flow

Pirozzoli

2023

J. Fluid Mech.

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We study the statistics of passive scalars (either temperature or concentration of a diffusing substance) at friction Reynolds number ${Re}_{\tau }=1140$ , for turbulent flow within a smooth straight pipe of circular cross-section, in the range of Prandtl numbers from ${Pr}=0.00625$ , to ${Pr}=16$ , using direct numerical simulations (DNS) of the Navier–Stokes equations. Whereas the organization of passive scalars is similar to the axial velocity field at ${Pr} = O(1)$ , similarity is impaired at low Prandtl number, at which the buffer-layer dynamics is filtered out, and at high Prandtl number, at which the passive scalar fluctuations become confined to the near-wall layer. The mean scalar profiles at ${Pr} \gtrsim 0.0125$ are found to exhibit logarithmic overlap layers, and universal parabolic distributions in the core part of the flow. Near-universality of the eddy diffusivity is exploited to derive accurate predictive formulas for the mean scalar profiles, and for the corresponding logarithmic offset function. Asymptotic scaling formulas are derived for the thickness of the conductive (diffusive) layer, for the peak scalar variance, and its production rate. The DNS data are leveraged to synthesize a modified form of the classical predictive formula of Kader & Yaglom (Intl J. Heat Mass Transfer, vol. 15, 1972, pp. 2329–2351), which is capable of accounting accurately for the dependence on both Reynolds and Prandtl numbers, for ${Pr} \gtrsim 0.25$ .

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Introduction

Cited by 2 publications

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Searching for the log law in open channel flow

Searching for the log law in open channel flow

Prandtl number effects on passive scalars in turbulent pipe flow

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