Equilibrium-eulerian les model for turbulent poly-dispersed particle-laden flow

Handle URI:
http://hdl.handle.net/10754/562715
Title:
Equilibrium-eulerian les model for turbulent poly-dispersed particle-laden flow
Authors:
Icardi, Matteo ( 0000-0003-3924-3117 ) ; Marchisio, Daniele Luca; Chidambaram, Narayanan; Fox, Rodney O.
Abstract:
An efficient Eulerian method for poly-dispersed particles in turbulent flows is implemented, verified and validated for a channel flow. The approach couples a mixture model with a quadrature-based moment method for the particle size distribution in a LES framework, augmented by an approximate deconvolution method to reconstructs the unfiltered velocity. The particle velocity conditioned on particle size is calculated with an equilibrium model, valid for low Stokes numbers. A population balance equation is solved with the direct quadrature method of moments, that efficiently represents the continuous particle size distribution. In this first study particulate processes are not considered and the capability of the model to properly describe particle transport is investigated for a turbulent channel flow. First, single-phase LES are validated through comparison with DNS. Then predictions for the two-phase system, with particles characterised by Stokes numbers ranging from 0.2 to 5, are compared with Lagrangian DNS in terms of particle velocity and accumulation at the walls. Since this phenomenon (turbophoresis) is driven by turbulent fluctuations and depends strongly on the particle Stokes number, the approximation of the particle size distribution, the choice of the sub-grid scale model and the use of an approximate deconvolution method are important to obtain good results. Our method can be considered as a fast and efficient alternative to classical Lagrangian methods or Eulerian multi-fluid models in which poly-dispersity is usually neglected.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Publisher:
De Gruyter
Journal:
International Journal of Nonlinear Sciences and Numerical Simulation
Issue Date:
1-Apr-2013
DOI:
10.1515/ijnsns-2012-0086
Type:
Article
ISSN:
15651339
Sponsors:
The technical and financial support of Ascomp GmbH is gratefully acknowledged. CPU time was made available by CASPUR (Rome, Italy) through the collaborative test case LESinItaly [12]. ROF acknowledges the financial support of a Lagrange Fellowship from the CRT Foundation (Torino, Italy). The authors wish to thank Prof. Massimo Germano for his precious comments.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorIcardi, Matteoen
dc.contributor.authorMarchisio, Daniele Lucaen
dc.contributor.authorChidambaram, Narayananen
dc.contributor.authorFox, Rodney O.en
dc.date.accessioned2015-08-03T11:02:48Zen
dc.date.available2015-08-03T11:02:48Zen
dc.date.issued2013-04-01en
dc.identifier.issn15651339en
dc.identifier.doi10.1515/ijnsns-2012-0086en
dc.identifier.urihttp://hdl.handle.net/10754/562715en
dc.description.abstractAn efficient Eulerian method for poly-dispersed particles in turbulent flows is implemented, verified and validated for a channel flow. The approach couples a mixture model with a quadrature-based moment method for the particle size distribution in a LES framework, augmented by an approximate deconvolution method to reconstructs the unfiltered velocity. The particle velocity conditioned on particle size is calculated with an equilibrium model, valid for low Stokes numbers. A population balance equation is solved with the direct quadrature method of moments, that efficiently represents the continuous particle size distribution. In this first study particulate processes are not considered and the capability of the model to properly describe particle transport is investigated for a turbulent channel flow. First, single-phase LES are validated through comparison with DNS. Then predictions for the two-phase system, with particles characterised by Stokes numbers ranging from 0.2 to 5, are compared with Lagrangian DNS in terms of particle velocity and accumulation at the walls. Since this phenomenon (turbophoresis) is driven by turbulent fluctuations and depends strongly on the particle Stokes number, the approximation of the particle size distribution, the choice of the sub-grid scale model and the use of an approximate deconvolution method are important to obtain good results. Our method can be considered as a fast and efficient alternative to classical Lagrangian methods or Eulerian multi-fluid models in which poly-dispersity is usually neglected.en
dc.description.sponsorshipThe technical and financial support of Ascomp GmbH is gratefully acknowledged. CPU time was made available by CASPUR (Rome, Italy) through the collaborative test case LESinItaly [12]. ROF acknowledges the financial support of a Lagrange Fellowship from the CRT Foundation (Torino, Italy). The authors wish to thank Prof. Massimo Germano for his precious comments.en
dc.publisherDe Gruyteren
dc.subjectApproximate deconvolution methoden
dc.subjectDirect quadrature method of momentsen
dc.subjectLarge eddy simulationen
dc.subjectParticle-laden flowen
dc.subjectPoly-dispersed particlesen
dc.subjectPopulation balance equationen
dc.subjectTurbophoresisen
dc.titleEquilibrium-eulerian les model for turbulent poly-dispersed particle-laden flowen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalInternational Journal of Nonlinear Sciences and Numerical Simulationen
dc.contributor.institutionDipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, Italyen
dc.contributor.institutionAscomp GmbH, Zurich, Switzerlanden
dc.contributor.institutionDepartment of Chemical and Biological Engineering, Iowa State University, Ames, United Statesen
kaust.authorIcardi, Matteoen
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