Mechanisms of particle clustering in Gaussian and non-Gaussian synthetic turbulence

Nilsen, Christopher; Andersson, Helge I.

doi:10.1103/physreve.90.043005

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…14 The MTLM method has been used to investigate the closure for the pressure Hessian in the Lagrangian models for the velocity gradient, 44 to synthesize magnetic fields, 45 and to study particle clustering. 46 Applications to inhomogeneous turbulence have also been explored. 3,13 What is missing is the perspective of subgrid-scale (SGS) modelling.…”

Section: Introductionmentioning

confidence: 99%

Subgrid-scale stresses and scalar fluxes constructed by the multi-scale turnover Lagrangian map

Al-Bairmani

Rosales

et al. 2017

Physics of Fluids

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eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. (2011)]. The synthetic fields have been shown to possess highly realistic statistics characterizing small scale intermittency, geometrical structures, and vortex dynamics. In this paper, we present a study of the synthetic fields using the filtering approach. This approach, which has not been pursued so far, provides insights on the potential applications of the synthetic fields in large eddy simulations and subgridscale (SGS) modelling. The MTLM method is first generalized to model scalar fields produced by an imposed linear mean profile. We then calculate the subgrid-scale stress, SGS scalar flux, SGS scalar variance, as well as related quantities from the synthetic fields. Comparison with direct numerical simulations (DNSs) shows that the synthetic fields reproduce the probability distributions of the SGS energy and scalar dissipation rather well. Related geometrical statistics also display close agreement with DNS results. The synthetic fields slightly under-estimate the mean SGS energy dissipation and slightly over-predict the mean SGS scalar variance dissipation. In general, the synthetic fields tend to slightly under-estimate the probability of large fluctuations for most quantities we have examined. Small scale anisotropy in the scalar field originated from the imposed mean gradient is captured. The sensitivity of the synthetic fields on the input spectra is assessed by using truncated spectra or model spectra as the input. Analyses show that most of the SGS statistics agree well with those from MTLM fields with DNS spectra as the input. For the mean SGS energy dissipation, some significant deviation is observed. However, it is shown that the deviation can be parametrized by the input energy spectrum, which demonstrates the robustness of the MTLM procedure. Published by AIP Publishing. Subgrid-scale stresses and scalar fluxes constructed by the multi-scale turnover Lagrangian map

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Section: Introductionmentioning

confidence: 99%

Subgrid-scale stresses and scalar fluxes constructed by the multi-scale turnover Lagrangian map

Al-Bairmani

Rosales

et al. 2017

Physics of Fluids

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A structural subgrid-scale model for the collision-related statistics of inertial particles in large-eddy simulations of isotropic turbulent flows

et al. 2020

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In large-eddy simulations of particle-laden isotropic turbulent flows, the collision of inertial particles is strongly influenced by missing small-scale turbulence. In this paper, we apply the Kinematic Simulation with Approximate Deconvolution (KSAD) model to determine the contribution of small-scale turbulence to the motion of inertial particles and improve the prediction accuracy of the radial distribution function (RDF) and radial relative velocity (RRV), which are closely related to particle collisions. Different values of Stokes numbers (St), which are defined as the ratio of the particle response time to the Kolmogorov time scale, are considered. The KSAD model significantly improves the prediction accuracy of the RRV for all considered St. For the prediction of RDF, good agreement between the KSAD model and direct numerical simulations is only observed for large St, i.e., St ≥ 2.0. To explore the reason for the poor prediction of the KSAD model for small St, we compare the Eulerian statistics of the flow fields and the Lagrangian properties of the particles from different simulations and find the key reason is that the Gaussian turbulence generated in the kinematic simulation model is inadequate in recovering the vortex centrifugal effect of small-scale turbulence on the inertial particle clustering at small St.

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Mechanisms of particle clustering in Gaussian and non-Gaussian synthetic turbulence

Cited by 2 publications

References 25 publications

Subgrid-scale stresses and scalar fluxes constructed by the multi-scale turnover Lagrangian map

Subgrid-scale stresses and scalar fluxes constructed by the multi-scale turnover Lagrangian map

A structural subgrid-scale model for the collision-related statistics of inertial particles in large-eddy simulations of isotropic turbulent flows

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