162
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

A Two-Way Coupled Model of Particle-Laden Turbulence

, &
Pages 1493-1512 | Received 01 Nov 2014, Accepted 24 Dec 2014, Published online: 05 May 2015
 

Abstract

A model for turbulent suspensions involving two-way coupling between a liquid carrier phase and a solid dispersed phase is presented. Closure relations are obtained from particle kinetic theory, with both drag and virtual mass effects taken into account. It is shown that the feedback on the flow due to the particles mainly depends on the particle concentration, the characteristic Stokes numbers, and the density ratio between the particle and liquid phases. Over a broad parameter range, we find that drag coupling gives turbulence damping and an increased streaming velocity. Added mass coupling has the opposite effect. Some of the model predictions are compared with data given in the literature, and with PIV experimental data. It is found that the model is generally consistent with experiments; in particular, when it comes to the observed turbulence damping and drag reduction effects. The model can be used as a stand-alone tool to calculate turbulent stresses, mean velocities and concentration profiles of both phases. Alternatively, it can serve as basis for a reduced parametric model of particle feedback effects on; for example, the eddy viscosity, and thus the pressure drop and superficial speed.

GRAPHICAL ABSTRACT

ACKNOWLEDGEMENT

We thank Chris Lawrence for valuable input.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 666.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.