Nanofluids are a new class of heat transfer fluids that aim to improve the poor thermal efficiency of conventional heat transfer fluids. The dispersion of nanoparticles into traditional heat transfer fluids, such as water, ethylene glycol, glycerol, engine oil and gear oil, improves the thermal conductivity of base fluids, which has attracted researchers to apply nanofluids in engineering systems. Nanofluids show higher thermal and electrical conductivity. However, in terms of heat transfer performance, viscosity is also important. The viscosity of nanofluids increases due to an increase in the nanoparticle volume fraction, which needs attention and proper experimental investigation to improve the efficiency of nanofluids in heat transfer applications. Consequently, investigation into the effective viscosity of nanofluids is as important as the thermal conductivity.
On the other hand, how nanofluids are prepared can have an effect on the resultant performance. Using an ultrasonication mixer for the dispersion of nanoparticles in the base fluid is one of the most effective and popular methods of preparing nanofluids, especially from the two-step method. Almost all the experimental studies available on nanofluids chose an arbitrary time for the preparation of nanofluids. Choosing an arbitrary time for ultrasonication or any other physical preparation mechanism may be counterproductive. Therefore, in this research, nanofluids are prepared through an optimised two-step method that is assisted with ultrasonic vibration. The resulting homogenised nanofluids are further investigated for the influence of temperature, particle size, volume fraction, base fluid type and particle type on the evolution of the viscosity, pH and electrical conductivity. The temperature range investigated in this thesis is 20 to 70 oC; the nanoparticle volume fraction is up to 5%; the base fluids are ethylene glycol (EG) and glycerol, while the nanoparticle types are MgO, Al2O3 and SiO2 in different sizes.
Viscosity is a very important parameter, especially in systems that involve fluid flow (forced or natural convection) and for numerical analysis. However, most generic models in the literature underpredicted the viscosity evolution of nanofluids. Therefore, it is essential that very accurate models need to be developed for the prediction of the viscosity of nanofluids. To this end, this research also models the viscosity of the different nanofluids using dimensional analysis and regression analysis based on the experimental input-output data. Furthermore, artificial intelligence methods, such as the group method of data handling-neural network (GMDH-NN), genetic algorithm-polynomial neural network (GA-PNN) and fuzzy C-mean clustering-based adaptive neuro-fuzzy inference system (FCM-ANFIS) methods, are used to model the relationships between the experimental input parameters and the viscosity of the nanofluids.
Generally, the viscosity of the nanofluids reduced exponentially with temperature increase and the trends are similar to those displayed by the respective base fluids. However, the viscosity of the nanofluids is higher depending on the concentration of the nanoparticles contained in the nanofluids. Suspending nanoparticles in the base fluid increased the viscosity of the resulting nanofluid, and a further increase in the volume fraction of the nanoparticles increased the effective viscosity of the nanofluids. The viscosity trend of the nanofluids of Al2O3-glycerol is non-linear to volume fraction increase while MgO-EG nanofluids displayed a linear dependence. Regarding the influence of particle size, smaller particles produced a higher energy dissipation rate due to the higher number density, increased Brownian velocity and particle-particle interactions. Therefore, the viscosity was higher in nanofluid samples prepared from smaller nanoparticles. When the same nanoparticle samples were dispersed in different base fluids, it was found that the relative viscosity is different in the different nanofluids, which suggests that base fluid properties are indispensable when discussing the viscosity of nanofluids.