Date

2013

Document Type

Thesis

Degree

Master of Science

Department

Mechanical Engineering

First Adviser

Neti, Sudhakar

Abstract

Nanofluids are colloidal dispersions of nano sized particles (<100nm in diameter) in dispersion mediums. They are of great interest in industrial applications as heat transfer fluids owing to their enhanced thermal conductivities. Stability of nanofluids is a major problem hindering their industrial application. Agglomeration and then sedimentation are some reasons, which drastically decrease the shelf life of these nanofluids. Current research addresses the agglomeration effect and how it can affect the shelf life of a nanofluid. The reasons for agglomeration in nanofluids are attributable to the interparticle interactions which are quantified by the various theories. By altering the governing properties like volume fraction, pH and electrolyte concentration different nanofluids with instant agglomeration, slow agglomeration and no agglomeration can be produced. A numerical model is created based on the discretized population balance equations which analyses the particle size distribution at different times. Agglomeration effects have been analyzed for alumina nanoparticles with average particle size of 150nm dispersed in de-ionized water. As the pH was moved towards the isoelectric point of alumina nanofluids, the particle size distribution became broader and moved to bigger sizes rapidly with time. Particle size distributions became broader and moved to bigger sizes more quickly with time with increase in the electrolyte concentration. The two effects together can be used to create different temporal trends in the particle size distributions. Faster agglomeration is attributed to the decrease in the electrostatic double layer repulsion forces which is due to decrease in the induced charge and the double layer thickness around the particle. Bigger particle clusters show lesser agglomeration due to reaching the equilibrium size. The procedures and processes described in this work can be used to generate more stable nanofluids.

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