Characterisation of the Properties and Performance of Nanofluid Coolants with Analysis of Their Feasibility for Datacentre Cooling

It is a generally accepted belief that the use of nanofluids enhances heat transfer rates in comparison with a traditional fluid and can be considered to be one of the most important energy conservation measures in many industrial applications. Despite increased interest, detailed and systematic studies of nanofluids’ flow and thermal characteristics are limited and their effect on heat transfer is often misunderstood. The concentration of a nanofluid is often chosen independently of the application conditions, nanofluid type or cost and other economic parameters such as the cost of energy and lifetime of the application. This thesis has three main objectives; the first is the measurement of the thermal properties of nanofluids and the proposal of a correlation model for nanofluid viscosity. The results of these measurements show that the nanofluid viscosity depends on the type of nanoparticles and their concentration and the fluid temperature. It is shown that the viscosity increases with increasing nanoparticle concentration and decreases with increasing temperatures over the nanoparticle concentration and temperature ranges investigated. The second objective is concerned with the measurement and evaluation of heat transfer performance and pressure drop of various nanofluids via an experiment using forced convection heat transfer within the turbulent regime. In general, it is shown that the heat transfer coefficient of nanofluids decreases with increasing nanoparticle concentration at a specific flow rate and the base fluid gives higher heat transfer with respect to the nanofluids. In the other hand, Assessing the thermal performance of nanofluids by considering the Nusselt number and its variation with Reynolds number is misleading because both Nusselt number and Reynolds number depend on the nanofluid properties (i.e. thermal conductivity, density and viscosity that are function of the volume fraction). This can lead to a false impression that some nanofluids produce an improvement in heat transfer performance. Moreover, using nanofluid will require additional pumping power to achieve the corresponding base fluid’s Reynolds number. Finally, existing single-phase liquid correlations of the heat transfer coefficient and pressure drop are compared and show good agreement in predicting nanofluid behaviour. The third objective aims to determine recommended nanoparticle concentrations of a typical nanofluid used within immersion cooling of a data centre server; this is based on two different designs for immersion cooling by Iceotope at varying Reynolds numbers. This is determined by calculating the heat transfer and pressure drop through the server for various values of the volume fraction of nanoparticles by using a model created within the finite element solver, COMSOL. The server’s total power consumption as a function of its CPU temperature and cooling system, including the increased pumping power required for varying nanofluid concentrations, are predicted and used in a proposed novel methodology to evaluate potential economic trade-offs in utilising nanofluids within an immersed liquid cooled data centre. This methodology is used to calculate the minimum total costs and economically optimum volume fraction of a nanofluid with this type of data centre. The results show that Al2O3-water nanofluids showed the highest thermal performance with respect to other nanofluids because Al2O3 has the highest thermal conductivity, however Al2O3-water nanofluids are the most viscous giving it the highest pressure drop and consequently the highest pumping costs. Under the economic factors used, it is found that the most cost effective fluid for both the server with cooled plate having two parallel tubes and the server with cooled plate having serpentine tube configuration is the base fluid (water)