Analysis of nanofluids as a means of thermal conductivity enhancement in heavy machineries

Abstract

Purpose – Nanofluids exhibit enhanced heat transfer characteristics and are expected to be the future heat transfer fluids particularly the lubricants and transmission fluids used in heavy machinery. For studying the heat transfer behaviour of the nanofluids, precise values of their thermal conductivity are required. For predicting the correct value of thermal conductivity of a nanofluid, mathematical models are necessary. In this paper, the effective thermal conductivity of various nanofluids has been reported by using both experimental and mathematical modelling. The paper aims to discuss these issues.

Design/methodology/approach – Hamilton and Crosser equation was used for predicting the thermal conductivities of nanofluids, and the obtained values were compared with the experimental findings. Nanofluid studied in this paper are Al2O3 in base fluid water, Al2O3 in base fluid ethylene glycol, CuO in base fluid water, CuO in base fluid ethylene glycol, TiO2 in base fluid ethylene glycol. In addition, studies have been made on nanofluids with CuO and Al2O3 in base fluid SAE 30 particularly for heavy machinery applications.

Findings – The study shows that increase in thermal conductivity of the nanofluid with particle concentration is in good agreement with that predicted by Hamilton and Crosser at typical lower concentrations.

Research limitations/implications – It has been observed that deviation between experimental and theoretical results increases as the volume concentration of nanoparticles increases. Therefore, the mathematical model cannot be used for predicting thermal conductivity at high concentration values.

Originality/value – Studies on nanoparticles with a standard mineral oil as base fluid have not been considered extensively as per the previous literatures available.

Publication
In Industrial Lubrication and Tribology
Md Imbesat Hassan Rizvi
Md Imbesat Hassan Rizvi
Technical (Research) Associate

My research interests include scientific machine learning, natural language processing, reinforcement learning, robotics and human-robot interaction.