Modelling and multi-objective optimisation of heat transfer characteristics and pressure drop of nanofluids in microtubes

Abstract

A literature study was performed on the inner mechanisms of nanofluids and flow in microchannels. With ever changing technology, the need for smaller and more efficient devices has come about in the last couple of years. With the shrinking in size of components in electronics, an increase in heat has become a notable problem. With conventional heat transfer fluids not being able to handle the required heat removal rates, research into fluid enhancing has been of great interest. A nanofluid is a fluid with enhanced heat transfer potential, which can solve the problem of extracting enough of the added heat of new-age components. This will allow electronics to work with increased power and accomplish tasks faster. Nanofluids have been a very controversial method of heat transfer as problems with stability were keeping the fluid from replacing traditional heat transfer fluids. Some research has been done on the models used for simulating and defining the thermal properties of nanofluids. Added accuracy of the models has been seen in recent years. However, no optimal setup for nanofluids has been found in terms of combining parameters like the base fluid and nanoparticle, as well as the concentration and diameter of the nanoparticle. An optimal setup of this kind would produce the best heat transfer rates at the lowest pressure drop. The simulation of nanofluids was done in Ansys CFD. The validation was done with previous literature that had experimental and numerical results. The validation had a very good outcome as some of the temperature data inside the microchannel presented a good correlation to previous work. The setup of the model for simulation and duplication to create a design study was also described and shown. This was done to ensure that the model can be used again if further investigation is needed. This will enable one to determine the effect of a new nanoparticle on the field of study to continuously improve on the model. The results indicated the best nanoparticle to use with the best base fluid to ensure the lowest pressure drop and highest heat transfer. This was done with a multi-objective optimisation general algorithm. The outcome of the optimisation was that silicon dioxide, as nanoparticle, and water, as base fluid, would give the optimal setup. The diameter also appeared to have a very small effect on the outcome.