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Title: An Experimental Investigation into Cluster Formation Towards Modelling Thermal Conductivity of Nanofluids
Authors: Lal, Kundan
Supervisor: Mallick, S. S.
Keywords: Nanofluid;Perikinteic;Induced micro-convection;Brownian number;Thermal conductivity
Issue Date: 24-Jul-2018
Abstract: The structural appearance and growth of nanoparticles in the form of nanoclusters have a considerable effect on the thermal conductivity enhancement of nanofluids. The growth of nanoclusters of Al2O3 and TiO2 nanoparticles (size 25-30 nm) in water (DI) (along with suitable surfactants) has been investigated. A comprehensive study on the size distribution of the particles while in suspension at different pH values at their respective zeta potentials along with the stability ratio of suspensions have been carried out. Experiments were performed to observe the effect of various surfactants on stability, nanocluster formation and on the thermal conductivity of Al2O3-H2O nanofluid, which was found to be getting improved significantly with SDS surfactant. The prolonged sonication was not adequate to break the clusters of Al2O3 nanoparticles into an average size of less than 163 nm, indicating the tendency of Al2O3 nanoparticles to remain in the form of clusters instead of individual nanoparticles of the initial size of 20 nm. The stability, thermal conductivity of Al2O3-H2O nanofluid and also the average nanocluster size is found to reduce to less than 200 nm from 1,602, 1,827, 1,069, and 922 nm by using sodium dodecyl sulfate (NaDS) (SDS) as an affecting surfactant out of several other surfactants. A consistent enhancement in the thermal conductivity (i.e., 0.70 W/mK over a period of 3 hours) was observed. The Box Behnken Design (BBD) under Response Surface Mythology (RSM) has been used to model the thermal conductivity of Al2O3-H2O nanofluid by taking volumetric concentration, temperature, and surfactant as the affecting parameters. The developed model is validated against the experimental data of thermal conductivity of Al2O3-H2O nanofluid and with the other existing models. The results confirmed that the model could predict the experimental results with a high accuracy level (R2 = 0.98). The model can be used to obtain various combinations of these parameters (volumetric concentration, surfactant amount, and temperature) to achieve a significant enhancement in the thermal conductivity of Al2O3-H2O nanofluids. It is also emphasized that dispersion quality and stability are the key factors required to predict the thermal conductivity enhancement for a particular type of nanofluid accurately in addition to some other well-established affecting parameters. A quantitative analysis on the thermal conductivity enhancement of nanofluids, along with investigations on the role of nanoparticles present in the form of dead-ends and backbone chains in aggregates have been carried out. In suspensions, the concentration of nanoparticles changes with elapsed time depending on the morphological properties of the nanoclusters and the nature of nanoparticles and base fluids. The experimental investigations carried out in this thesis represent a quantitative analysis of the effect of time, temperature, and instantaneous volume fractions on perikinetic heat conduction and Brownian motion induced micro-convection mechanisms. The investigations show that the zeta potential is proportional to stability ratio and inversely proportional to the hydrodynamic size of the nanoparticles while in suspension. The stabilities of various samples of nanofluids are found to be decreasing with increase in the size of nanoclusters and temperature (20 to 50°C). The aggregation time constant and stability ratio are found to be directly proportional. The effect of static and dynamic heat transport parameters on the overall thermal conductivity of Al2O3-H2O and TiO2-H2O nanofluids have been investigated. The structural models and Brownian motion based convection models of thermal conductivity for the varying concentration of the nanoparticles in the respective suspensions have been developed to predict the thermal conductivity enhancements. In addition to the suspensions’ stability parameters, these models also take into account the effect of nanocluster growth, temperature, thermal interfacial resistance and liquid layering in the nanoclusters. At the lower working temperature, i.e., from 20 to 30°C, the Brownian motion based induced convection effects are found to be negligible compared to the perikinetic conduction effects. The phenomenon was observed to be getting reversed with an increase in temperature i.e. from 30 to 50°C. Both the mechanisms were found to be responsible for the heat transport through the nanofluids, although their magnitudes vary depending on the nature, size, temperature, and properties of nanoclusters and base fluids. The effect of base fluid layering around the nanoparticles in an aggregate has also been highlighted to include the effect of liquid layering on the thermal conductivity of the water present in an aggregate compared to the bulk water present outside the nanocluster or an aggregate. The thermal conductivity measuring equipment becomes more prone to the convection effects above 50°C when water is the working medium. Thermal Property Analyzer gives fairly good results (till 50°C) of thermal conductivity measurements within accuracy and precision of ± 5%. In KD2 Pro, by maintaining the sensor needle in inverted position and angle within 5° (with vertical) while measuring the thermal conductivity, has been found to be a useful way to control its accuracy and precision within the limit. While developing models, more focus has been laid to the average hydrodynamic size of the nanoparticles while in suspensions rather than the individual particle size. The modified models give fairly a good prediction of the thermal conductivity enhancement for the nanofluids. The error involved in experimental and theoretical results of the overall thermal conductivities varies from 8 to 13% for Al2O3-H2O nanofluid and from 4 to 7% for TiO2-H2O nanofluid, in the temperature range from 20-50°C. Thus, in the study undertaken the error between the experimental and the theoretical values of the overall thermal conductivities is observed varies from 4-12% at temperature from 20-50 °C. While taking the measurements of the thermal conductivity of nanofluids, it is observed that thermal property analyzer (KD2 Pro) becomes more prone to the convection effects especially, when water is used as working medium beyond 50°C. It gives fairly good results (up to 50°C) of thermal conductivity measurements within accuracy and precision of ± 5%.
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