Hairpin vortices in the largest scale of turbulent boundary layers

Motoori, Yutaro; Goto, Susumu

doi:10.1016/j.ijheatfluidflow.2020.108658

Cited by 17 publications

(4 citation statements)

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“…These observations are similar to turbulence in a periodic cube (Goto 2008; Goto et al. 2017), turbulent boundary layers (Motoori & Goto 2019, 2020) and turbulent channel flow (Motoori & Goto 2021). However, the turbulent wake is more complex than these flows because the largest-scale vortices are different depending on regions.…”

Section: Hierarchy Of Coherent Vorticessupporting

confidence: 60%

Hierarchy of coherent vortices in turbulence behind a cylinder

Fujino,

Motoori,

Goto

2023

J. Fluid Mech.

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To draw concrete pictures of the hierarchy of multiscale coherent vortices in turbulence behind a cylinder and to reveal their sustaining mechanism, we conduct direct numerical simulations of the turbulence at the Reynolds number, which is defined by the uniform inflow velocity and the cylinder diameter, $5000$ . The turbulence consists of three kinds of hierarchies of coherent vortices in three distinct regions: namely, the downstream region, the recirculation region just behind the cylinder and the separate shear layers. By tracking the temporal evolution of multiscale vortices in each of these regions, we demonstrate that, in all the three regions, smaller coherent vortices tend to align in the direction perpendicular to larger ones. This implies that smaller vortices are stretched and amplified in the strain-rate fields around larger ones. We also show the relevance of this observation to the energy cascade. Smaller-scale vortices receive the kinetic energy in the regions where they are stretched by larger-scale vortices; and, at the same time, they tend to compress larger ones, thus reducing larger-scale kinetic energy.

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Section: Hierarchy Of Coherent Vorticessupporting

confidence: 60%

Hierarchy of coherent vortices in turbulence behind a cylinder

Fujino,

Motoori,

Goto

2023

J. Fluid Mech.

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“…2014) have revealed three-dimensional flow structures in single-phase wall turbulence. Our previous studies also revealed the generation mechanism of the hierarchy of coherent structures in a turbulent boundary layer at (Motoori & Goto 2019, 2020) and turbulent channel flow at (Motoori & Goto 2021). Wall-attached vortices, i.e.…”

Section: Introductionmentioning

confidence: 63%

Role of the hierarchy of coherent structures in the transport of heavy small particles in turbulent channel flow

2022

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To investigate the transport of heavy small particles (inertial particles) in high-Reynolds- number wall turbulence, we conduct direct numerical simulations of inertial particles in turbulent channel flow at the friction Reynolds number $Re_\tau =1000$ . In the statistically steady state, particles distribute inhomogeneously; particles with different relaxation times form voids with different sizes in the bulk of the flow, whereas they form streak-like clusters with different widths along the streamwise direction. To explore the origin of the multiscale voids and clusters of particles, we identify objectively the axes of tubular vortices and the spines of streaks with different sizes. These identifications enable us to show quantitatively that (i) vortices sweep out the particles of the relaxation time comparable with their turnover time, irrespective of their size and existing height, and that, among these particles, (ii) those swept out by wall-detached tubular vortices form clusters isotropically around them, whereas (iii) those swept out by wall-attached vortices are attracted by a nearby low-speed streak. These explain the reason why the multiscale clusterings are well described in terms of the local Stokes number defined by the turnover time of multiscale vortices. Furthermore, these descriptions of the particle transport give us a clear view to understand velocity statistics and wall-deposition mechanism of inertial particles in high-Reynolds-number wall turbulence.

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“…This technique was used by several authors (e.g. del Álamo et al 2006;Lozano-Durán et al 2012;Osawa & Jiménez 2018;Motoori & Goto 2020). More concretely, we tag the same label to the neighbouring connected grid cells satisfying the condition in order to count the number of the identified vortices and to estimate the volume for different values of Q 10), since each object shrinks, the number again decreases.…”

Section: Appendix a Percolation Analysismentioning

confidence: 99%