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City, University of London

Coupling of nanofluid flow, heat transfer and nanoparticles sedimentation using OpenFOAM

Abstract

dc:description.abstract

Nanofluid is a suspension containing a certain quantity of nanoscaled solid particles in a conventional cooling liquid. Compared to pure liquid in micro channels, nanofluid shows notably better heat transfer performance but without erosion and clogging problems as normal two-phase suspensions. Due to such advantages, nanofluid is increasingly applied as an ideal coolant in engineering. For a better understanding of nanofluid flow and heat transfer performance, many investigations have been carried out recently in both experimental and numerical ways. In numerical investigations, computational fluid dynamics (CFD) is playing a dominant role due to its maturity in the area of fluid flow and heat transfer research. However, in previous CFD studies, the problem of nanoparticles sedimentation is always ignored based on the assumption that nanofluid is stable with homogeneous properties throughout the simulation. To some extreme cases in which nanoparticles sedimentation would happen soon after nanofluid preparation, such assumption could induce inaccurate numerical results. To investigate the relationships between nanofluid flow, heat transfer and nanoparticles sedimentation, an open source CFD package, OpenFOAM is employed as the basis to develop several numerical solvers in multi-phase way for the first time. More specifically, nanofluid CFD simulations are carried out by several newly developed OpenFOAM solvers under both Eulerian-Langrangian and Eulerian-Mixture (a simplified Eulerian-Eulerian approach) frames. By comparing present numerical results to previous published experimental and numerical investigations, it can be concluded that the newly developed solvers under both Eulerian-Langrangian and Eulerian-Mixture frames are capable to investigate nanofluid flow and heat transfer performance coupling with nanoparticles sedimentation. However, with the considerations of computational resource requirement, Eulerian-Mixture approach is believed to be better to achieve the balance between accuracy and computational effort. With an assumption that no appropriate stabilizing treatments have been applied after nanofluid preparation, CFD simulations are carried out for 0.64% Al²O³/water nanofluid in three most typical geometries by the newly developed solver 'nanofluidMixtureFoam'. According to the present research, it can be confirmed that nanofluid heat transfer and nanoparticles sedimentation have considerable impacts to each other other in nanofluid natural convections (in both two- and three-dimensional cases). More specifically, temperature driven flow leads to ticker nanoparticles sedimentation layer than that in normal sedimentation case. On the other hand, nanoparticles sedimentation layer induces worse nanofluid natural convection heat transfer performance. Furthermore, for forced convection problems in a horizontal channel with an open cavity, nanoparticles sedimentation is likely to occur at cavity bottom and leads to higher temperature at heating surface. For better heat transfer performance of the cooling blocks with similar geometries, lower fins (cavity depths) in blocks are recommended to reduce possible nanoparticles sedimentation. In summary, the newly developed OpenFOAM solvers and numerical observations in this thesis are expected to guide future nanofluid CFD study and correlative practical applications.

Degree

thesis:*
Name dc:type.qualificationname
phd
Level dc:type.qualificationlevel
doctoral
Grantor dc:publisher.institution
City, University of London
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Meng, Xiangyin

Subjects

dc:subject × 1

Chain of custody

source
Harvested from
City University of London
Base URL
openaccess.city.ac.uk/cgi/oai2
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Meng, Xiangyin. Coupling of nanofluid flow, heat transfer and nanoparticles sedimentation using OpenFOAM. doctoral thesis, City, University of London, 2017.