Lagrangian diffusion properties of a free shear turbulent jet

Viggiano, Bianca; Basset, Thomas; Solovitz, Stephen A.; Barois, Thomas; Gibert, Mathieu; Mordant, Nicolas; Chevillard, Laurent; Volk, Romain; Bourgoin, Mickaël; Cal, Raúl Bayoán

doi:10.1017/jfm.2021.325

Cited by 18 publications

(19 citation statements)

References 45 publications

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“…We stress that smoothing does not degrade the temporal resolution for the velocity estimates presented here as the sampling frequency of the cameras (6 kHz) oversamples the dissipation time scale between 0.3 and 0.8 ms (Viggiano et al. 2021). Smoothing improves the signal-to-noise ratio of velocity estimates, whose absolute accuracy is estimated (from small-scale Lagrangian increments statistics (Viggiano et al.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…Smoothing improves the signal-to-noise ratio of velocity estimates, whose absolute accuracy is estimated (from small-scale Lagrangian increments statistics (Viggiano et al. 2021)) to be of the order of . Considering that the typical axial velocity of the jet on the axis is , this accuracy corresponds to a dynamical range of velocity resolution of approximately three orders of magnitude.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…A more complete description of the hydraulic and optical set-ups as well as Lagrangian particle tracking and post-processing methods is given in Viggiano et al. (2021) which focuses on Lagrangian statistics in the same flow.…”

Section: Experimental Methodsmentioning

confidence: 99%

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Entrainment, diffusion and effective compressibility in a self-similar turbulent jet

et al. 2022

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An experimental Lagrangian study based on particle tracking velocimetry has been completed in an incompressible turbulent round water jet freely spreading into water. The jet is seeded with tracers only through the nozzle: inhomogeneous seeding called nozzle seeding. The Lagrangian flow tagged by these tracers therefore does not contain any contribution from particles entrained into the jet from the quiescent surrounding fluid. The mean velocity field of the nozzle seeded flow, $\langle \boldsymbol {U}_{\boldsymbol {\varphi }} \rangle$ , is found to be essentially indistinguishable from the global mean velocity field of the jet, $\langle \boldsymbol {U} \rangle$ , for the axial velocity while significant deviations are found for the radial velocity. This results in an effective compressibility of the nozzle seeded flow for which $\boldsymbol {\nabla }\boldsymbol {\cdot } \langle \boldsymbol {U}_{\boldsymbol {\varphi }} \rangle \neq 0$ even though the global background flow is fully incompressible. By using mass conservation and self-similarity, we quantitatively explain the modified radial velocity profile and analytically express the missing contribution associated with entrained fluid particles. By considering a classical advection–diffusion description, we explicitly connect turbulent diffusion of mass (through the turbulent diffusivity $K_T$ ) and momentum (through the turbulent viscosity $\nu _T$ ) to entrainment. This results in new practical relations to experimentally determine the non-uniform spatial profiles of $K_T$ and $\nu _T$ (and hence of the turbulent Prandtl number $\sigma _T = \nu _T/K_T$ ) from simple measurements of the mean tracer concentration and axial velocity profiles. Overall, the proposed approach based on nozzle seeded flow gives new experimental and theoretical elements for a better comprehension of turbulent diffusion and entrainment in turbulent jets.

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Section: Experimental Methodsmentioning

confidence: 99%

Section: Experimental Methodsmentioning

confidence: 99%

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Entrainment, diffusion and effective compressibility in a self-similar turbulent jet

et al. 2022

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“…Set the sum of bit rate and predistortion in the directed acyclic graph as R and D, respectively, then the calculation goal is to obtain an optimal θ* under the condition that π is greater than 0, so that the Lagrangian value in Formula ( 9) is minimized. 20 θ D λR * = arg min ( + ), θ (9) where…”

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confidence: 99%

Secure data stream transmission method for cell pathological image storage system

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Zhao

Yin

et al. 2021

Int J of Intelligent Sys

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“…We must move with the mean flow to adequately resolve the fine structure of the finite-time Lyapunov field. Lagrangian measurements of turbulent jets are not new; they have been used, for example, to track particles for two-point statistics [11], or to measure statistics near a turbulentnonturbulent interface [12], but in all cases with a fixed camera frame which severely limits the observation time.…”

Section: Introductionmentioning

confidence: 99%