Thermal conductivity of an ethylene glycol/water-based nanofluid with copper-titanium dioxide nanoparticles: An experimental approach

A model reported by the present investigators has earlier shown that the extent of heat pick up by a nanoparticle during its collision with the heat source in a given nanofluid would depend on the thermal conductivity (kp, unit W/m.K), density (ρ, unit kg/m3), elastic modulus (E, unit GPa) and Poissons ratio (μ) of the nanoparticle and heat source. Considering the expression for collision period and thermal conductivity of nanoparticle, a factor χ =kp(ρ/E)0.4 is proposed here and examined for the preliminary identification of the potential of a dispersoid in enhancing the thermal conductivity of a nanofluid. The χ-factor for Ag, Cu, CuO, Al2O3 and SiO2 are 2960, 2247, 116, 14.1 and 5.5, respectively. The higher χ-factor of CuO compared to that of Al2O3 can explain why water and ethylene glycol (EG) based CuO-nanofluid is reported to show higher enhancement in the thermal conductivity, when compared to similar Al2O3-nanofluid. The χ for SiO2 is much smaller than that for Ag, which also corroborates well with the marginal enhancement in thermal conductivity of water based nanofluid containing SiO2 nanoparticles. Therefore, a high value of χ of the nanodispersoid can serve as a parameter for the design of nanofluids for heat transfer applications.

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COMPARISON OF THERMAL CONDUCTIVITY FOR HHT-24-CNF-BASED NANOFLUID USING DEIONIZED WATER AND ETHYLENE GLYCOL

Jurnal Teknologi ◽

10.11113/jt.v77.6612 ◽

2015 ◽

Vol 77 (21) ◽

Author(s):

Syarifah Norfatin Syed Idrus ◽

Nor Salihah Zaini ◽

Imran Syakir Mohamad ◽

Norli Abdullah ◽

Mohd Haizal Mohd Husin

Keyword(s):

Thermal Conductivity ◽

Thermal Properties ◽

Ethylene Glycol ◽

Carbon Nanofibers ◽

Deionized Water ◽

Water Based

Carbon nanofibers (CNF) is one of potential nanoparticles that possess superior thermal conductivity. In this study, nanofluids with suspension of CNF in deionized water (DI water) and ethylene glycol (EG) are prepare. Thermal conductivity (TC) of the nanofluids are measured at 6°C, 25°C and 40°C using KD2 Pro Thermal Properties Analyser. The results show that, TC increases with increasing of temperature and CNF loading. Best TC is recorded by 36.7 % enhancement at 40 °C for EG based fluid with 0.9 wt% CNF loading. Meanwhile, for DI water based fluid, best TC enhancement (39.6 %) can be achieved with CNF loading of 0.7 wt% at 40°C. Overall, both based fluid show a promising enhancement in thermal conductivity. However, DI water based fluid show higher TC in comparison to EG based fluid due to the higher TC in standard DI water itself.

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Designing an artificial neural network using radial basis function (RBF-ANN) to model thermal conductivity of ethylene glycol–water-based TiO2 nanofluids

Journal of Thermal Analysis and Calorimetry ◽

10.1007/s10973-016-5725-y ◽

2016 ◽

Vol 127 (3) ◽

pp. 2125-2131 ◽

Cited By ~ 38

Author(s):

Mohammad Hemmat Esfe

Keyword(s):

Neural Network ◽

Thermal Conductivity ◽

Artificial Neural Network ◽

Ethylene Glycol ◽

Radial Basis Function ◽

Basis Function ◽

Water Based ◽

Radial Basis ◽

Artificial Neural ◽

Tio2 Nanofluids

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Intertube Falling-Film Heat Transfer Behavior of Multiwall Carbon Nanotube Suspensions (MWCNT Nanofluids)

ASME/JSME 2011 8th Thermal Engineering Joint Conference ◽

10.1115/ajtec2011-44542 ◽

2011 ◽

Cited By ~ 2

Author(s):

Binglu Ruan ◽

Anthony M. Jacobi

Keyword(s):

Heat Transfer ◽

Thermal Conductivity ◽

Heat Transfer Coefficient ◽

Ethylene Glycol ◽

Multiwall Carbon Nanotube ◽

Falling Film ◽

Water Based ◽

Film Heat Transfer ◽

The Heat Transfer Coefficient ◽

Multiwall Carbon

The thermal conductivity and viscosity of water-based and ethylene-glycol-based multiwall carbon nanotube (MWCNT) suspensions are measured for MWCNT volume concentrations up to 0.24%. The thermal conductivity is found to increase up to 8.6% and 9.3% for water-based and ethylene-glycol-based nanofluids, respectively. The viscosity of the nanofluids increases compared to that of their base fluids, with larger increases for the ethylene-glycol-based nanofluids. Intertube falling-film heat transfer characteristics of these nanofluids are measured and compared to data for the base fluids. The heat transfer coefficient of the water-based nanofluids decreases at low MWCNT concentrations but increases as the concentration increases. The heat transfer coefficient of the ethylene-glycol-based nanofluids decreases with an increase in MWCNT concentration, with a maximum deviation of 30%.

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Experimental studies on thermal conductivity of blends of ethylene glycol-water-based TiO2 nanofluids

International Communications in Heat and Mass Transfer ◽

10.1016/j.icheatmasstransfer.2013.05.009 ◽

2013 ◽

Vol 46 ◽

pp. 31-36 ◽

Cited By ~ 54

Author(s):

M. Chandra Sekhara Reddy ◽

V. Vasudeva Rao

Keyword(s):

Thermal Conductivity ◽

Ethylene Glycol ◽

Experimental Studies ◽

Water Based ◽

Tio2 Nanofluids

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Physical-Statistical Model of Thermal Conductivity of Nanofluids

Journal of Nanomaterials ◽

10.1155/2014/756765 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

B. Usowicz ◽

J. B. Usowicz ◽

L. B. Usowicz

Keyword(s):

Thermal Conductivity ◽

Ethylene Glycol ◽

Statistical Model ◽

Effective Thermal Conductivity ◽

Multinomial Distribution ◽

Water Based ◽

Classical Models ◽

Parallel Connections ◽

Solid Liquid ◽

Thermal Resistors

A physical-statistical model for predicting the effective thermal conductivity of nanofluids is proposed. The volumetric unit of nanofluids in the model consists of solid, liquid, and gas particles and is treated as a system made up of regular geometric figures, spheres, filling the volumetric unit by layers. The model assumes that connections between layers of the spheres and between neighbouring spheres in the layer are represented by serial and parallel connections of thermal resistors, respectively. This model is expressed in terms of thermal resistance of nanoparticles and fluids and the multinomial distribution of particles in the nanofluids. The results for predicted and measured effective thermal conductivity of several nanofluids (Al2O3/ethylene glycol-based and Al2O3/water-based; CuO/ethylene glycol-based and CuO/water-based; and TiO2/ethylene glycol-based) are presented. The physical-statistical model shows a reasonably good agreement with the experimental results and gives more accurate predictions for the effective thermal conductivity of nanofluids compared to existing classical models.

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