Structural Diffusion in 2D and 3D Random Flows

Malik, Nadeem A.

doi:10.1007/978-94-009-0297-8_177

Cited by 10 publications

(12 citation statements)

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“…We have verified that there is no significant variability in most of our results when the unsteadiness parameter is progressively changed from 0 to 1, and the specific results presented in this paper are those that have been obtained for ϭ0.4. For many of the statistics presented below, even a steady KS model ͑i.e., ϭ0͒ compares favorably with Yeung's DNS ͓see Malik, 8,12 who first observed that the twoparticle dispersion ͉͗⌬l͉ 2 ͘ ͑defined in the next paragraph͒ is insensitive to the unsteadiness in the range 0рр1 in 3-D KS͔.…”

Section: Results and Comparison With Dnssupporting

confidence: 53%

A Lagrangian model for turbulent dispersion with turbulent-like flow structure: Comparison with direct numerical simulation for two-particle statistics

1999

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A wide range of relative two-particle dispersion statistics from the Lagrangian Kinematic Simulation ͑KS͒ model, which contains turbulent-like flow structures, compares well with Yeung's ͓Phys. Fluids 6, 3416 ͑1994͔͒ DNS results. In particular, the Lagrangian flatness factor 4 (t) compares excellently ͑better than Heppe's ͓J. Fluid Mech. 357, 167 ͑1998͔͒ nonlinear stochastic model͒. For higher Reynolds numbers the results from KS show that 4 (t) is significantly greater than 3 over a wide range of times within the inertial range of time scales.

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Section: Results and Comparison With Dnssupporting

confidence: 53%

A Lagrangian model for turbulent dispersion with turbulent-like flow structure: Comparison with direct numerical simulation for two-particle statistics

1999

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“…and F l < 1 is a constant. For Kolmogorov turbulence, p = 5/3, this gives, K � s 4=3 l , which recovers the Richardson's 4/3-scaling law, [18][19][20], which validates the derivation.…”

Section: K �supporting

confidence: 78%

“…The frequency factor is chosen to be, λ = 0.5, which is typical in many KS studies. A choice of λ < 1 does not affect the scaling in the pair diffusivity, even frozen turbulence, λ = 0, has been found to yield the same scaling, which has been attributed to the open streamline topology of streamlines in 3D flows, [20].…”

Section: The Ks Velocity Fields and Energy Spectramentioning

confidence: 80%

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Turbulent particle pair diffusion: Numerical simulations

Malik

2019

PLoS ONE

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A theory for turbulent particle pair diffusion in the inertial subrange [Malik NA, PLoS ONE 13 (10):e0202940 (2018)] is investigated numerically using a Lagrangian diffusion model, Kinematic Simulations [Kraichnan RH, Phys. Fluids 13:22 (1970); Malik NA, PLoS ONE 12(12): e0189917 (2017)]. All predictions of the theory are observed in flow fields with generalised energy spectra of the type, E(k) � k −p. Most importantly, two non-Richardson regimes are observed: for short inertial subrange of size 10 2 the simulations yield quasi-local regimes for the pair diffusion coefficient, KðlÞ � s ð1þpÞ=2 l ; and for asymptotically infinite inertial subrange the simulations yield non-local regimes KðlÞ � s g l , with γ intermediate between the purely local scaling γ l = (1 + p)/2 and the purely non-local scaling γ nl = 2.

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“…all modes are advected with a constant velocity U (Turfus & Hunt 1987;Elliott & Majda 1996). Previous studies of three-dimensional KS fields suggest that the choice of ω n has no significant influence on most statistical properties of particle pairs for times t smaller than the integral time scale T L , and as long as ω n / k 3 n E(k n ) or ω n /(Uk n ) are not much larger than 1 (Malik 1996;Malik & Vassilicos 1999). In preliminary simulations for the present study we have confirmed this observation on the r.m.s.…”

Section: The Separation Pdf In Isotropic Flowsmentioning

confidence: 99%

A scalar subgrid model with flow structure for large-eddy simulations of scalar variances

Flohr

Vassilicos

2000

J. Fluid Mech.

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A new model to simulate passive scalar fields in large-eddy simulations of turbulence is presented. The scalar field is described by clouds of tracer particles and the subgrid contribution of the tracer displacement is modelled by a kinematic model which obeys Kolmogorov's inertial-range scaling, is incompressible and incorporates turbulent-like flow structure of the turbulent small scales. This makes it possible to study the scalar variance field with inertial-range effects explicitly resolved by the kinematic subgrid field while the LES determines the value of the Lagrangian integral time scale TL. In this way, the modelling approach does not rely on unknown Lagrangian input parameters which determine the absolute value of the scalar variance.The mean separation of particle pairs displays a well-defined Richardson scaling in the inertial range, and we find that the Richardson constant GΔ ≈ 0.07 which is small compared to the value obtained from stochastic models with the same TL. The probability density function of the separation of particle pairs is found to be highly non-Gaussian in the inertial range of times and for long times becomes Gaussian. We compute the scalar variance field for an instantaneous line source and find good agreement with experimental data.

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Structural Diffusion in 2D and 3D Random Flows

Cited by 10 publications

References 0 publications

A Lagrangian model for turbulent dispersion with turbulent-like flow structure: Comparison with direct numerical simulation for two-particle statistics

A Lagrangian model for turbulent dispersion with turbulent-like flow structure: Comparison with direct numerical simulation for two-particle statistics

Turbulent particle pair diffusion: Numerical simulations

A scalar subgrid model with flow structure for large-eddy simulations of scalar variances

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