Experimental investigation and model development for thermal conductivity of α-Al2O3-glycerol nanofluids

Abstract

A B S T R A C TIn order to investigate the effect of nanoparticle volume fraction, nanoparticle size and temperature on the thermal conductivity of glycerol based alumina (α-Al 2 O 3 ) nanofluids, a set of experiments were carried out for temperature ranging from 20°C to 45°C. The nanofluids contained α-Al 2 O 3 nanoparticles of three different sizes (31 nm, 55 nm and 134 nm) were prepared by two-step method at volume fractions ranging from 0.5% to 4%. The experimental results show that α-Al 2 O 3 -glycerol nanofluids have substantially higher thermal conductivity than the base fluid and the maximum enhancement of the relative thermal conductivity was 19.5% for the case of 31 nm at 4% volume fraction. The data analyses indicated that the volume fraction and size of the nanoparticles have significant effects on the thermal conductivity ratio (TCR) of Al 2 O 3 -glycerol nanofluids, while the temperature has almost no significant effect on the data for range of this study. At room temperature, the effective thermal conductivity remains almost constant for 50 h at 4% volume fractions. The comparison of the obtained experimental data and predictions from some existing theoretical and empirical models reveals that the thermal conductivity ratio and its trend could not be accurately explained by the models in open literature. Consequently, a new empirical correlation based on the experimental data has been developed in this study.

Experimental investigation of convection heat transfer in the transition flow regime of aluminium oxide-water nanofluids in a rectangular channel

“…It can be observed that the nanofluid viscosity increases with the volume concentration. Previous studies [27][28][29][30][31][32][33] reported similar results for the viscosities and thermal conductivities of nanofluids. The density and specific heat of the nanofluid were calculated using the following mixing theory equations:…”

Nanofluid Thermal Conductivity and Effective Parameters

“…It was suggested that this increase was caused by the increased interfacial area between nanoparticles and base liquid for smaller particle sizes. Using the same method, Sharifpur et al [42] studied the thermal conductivity of Al 2 O 3 -glycerol nanofluid. Experiments were conducted using Al 2 O 3 nanoparticles of three different diameters, including 31 nm, 55 nm, and 134 nm.…”

“…It was suggested that this increase was caused by increased Brownian motion at higher temperatures. However, several researchers have suggested that the effect of temperature on the thermal conductivity of nanofluids is solely a result of the increase in thermal conductivity of the base liquid [15,26,42]. Using the transient short hot-wire method, Yu et al [15] measured the thermal conductivities of AIN-Ethylene Glycol and AIN-propylene glycol nanofluids within the temperature range on 10-60 • C. A negligible change in thermal conductivity with the increase of temperature was observed.…”

“…It was suggested that the change in thermal conductivity of nanofluids with temperature follows the trend of increase in the thermal conductivity of their respective base fluid. Also, Sharifpur et al [42] measured the thermal conductivity of Al 2 O 3 -glycerol nanofluid as a function of temperature. The thermal conductivity did not vary significantly with temperature, and followed the pattern of the base fluid, glycerol.…”

Electrical conductivity and pH modelling of magnesium oxide–ethylene glycol nanofluids

“…The pH of nanofluids is one of the important parameters to achieve nanoparticle stability in a suspension [17]. Most of the modelling of the properties of nanofluids in the literature is limited to thermal conductivity and viscosity of nanofluids [16,[18][19][20][21][22][23][24][25][26][27], and there is a lack of modelling for other properties such as pH and electrical conductivities. An adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm polynomial neural networks (GA-PNN) are two novel approaches that use the advantages of neural network, fuzzy logic and genetic algorithm (GA) methods for modelling the effective pH and the electrical conductivity of magnesium oxide-ethylene glycol (MgO-EG) nanofluids.…”