Temperature dependent thermal conductivity enhancement of copper oxide nanoparticles dispersed in propylene glycol-water base fluid

Transcription

1 Int. J. Nanoparticles, Vol. 3, No. 2, Temperature dependent thermal conductivity enhancement of copper oxide nanoparticles dispersed in propylene glycol-water base fluid M.T. Naik* Centre for Energy Studies, Department of Mechanical Engineering, JNTUH College of Engineering, Kukatpally, Hyderabad , Andhra Pradesh State, India *Corresponding author G. Ranga Janardhana Department of Mechanical Engineering, JNTUK, Vijayanagaram , Andhra Pradesh State, India Abstract: Nanofluids are new generation heat transfer fluids and are preferred over conventional fluids for heat transfer applications. Nanofluids are characterised by their enhanced thermal conductivities, high energy density and better heat transfer capabilities than the base fluids without nanoparticles. Glycol based fluids exhibit an anti-freezing characteristics at sub zero temperatures and hence widely used in heat exchangers in cold regions. In the present experimental work, CuO nanoparticles of size less than 50 nm are suspended in the base fluid of propylene glycol-water (60:40 by volume) mixture, in the range of 0.025, 0.1, 0.4, 0.8 and 1.2% volume fraction and CuO nanofluids were prepared. The effective thermal conductivity of nanofluids for different particle volume concentration is measured at different temperatures of nanofluids. The experimental results obtained show that thermal conductivity of nanofluids increases with increase in temperature and particle volume concentration of nanofluids. The conductivity data obtained in present investigation are compared with conductivity models and correlations available in the literature. Keywords: nanoparticles; temperature dependent thermal conductivity; propylene glycol-water base fluid; thermal conductivity enhancement; volume fraction; conductivity models. Reference to this paper should be made as follows: Naik, M.T. and Janardhana, G.R. (2010) Temperature dependent thermal conductivity enhancement of copper oxide nanoparticles dispersed in propylene glycol-water base fluid, Int. J. Nanoparticles, Vol. 3, No. 2, pp Biographical notes: M.T. Naik is an Associate Professor in the Center for Energy Studies, JNTU College of Engineering, at JNT University, Hyderabad, India. He received his Master s in 2002 and Bachelor s in 1994 from the same institute. Currently, he is working on the experimental investigation of heat transfer enhancement in a circular tube using nanofluids. He has published about ten research papers in international journals and conferences. Copyright 2010 Inderscience Enterprises Ltd.

2 150 M.T. Naik and G.R. Janardhana G. Ranga Janardhana is a Professor of Mechanical Engineering, JNTUK College of Engineering, Kakinada India. He received his PhD from the same institute in 2001 and Master s from PSG Institute, Coimbattur India, in 1992 and Bachelor s in 1987 from JNT University, Hyderabad. He has published about 60 research papers in international journals and conferences. His research interests include heat exchangers and nano technology. 1 Introduction The vast new world of nanotechnology provides fertile grounds for scientific advances and the development of novel devices and materials which will affect the well-being of all. In recent years, several scientists and engineers have introduced nanofluids or stable suspensions of nanoparticles into host fluids, which shows great promises. Nanofluids are extensively used to study the heat transfer properties, and could potentially enhance the efficiency of large-scale heat exchangers used in chemical processing plants and HVAC systems as well as small scale heat exchangers used in the automotive and computer cooling sectors. Traditional heat transfer fluids like water, oils and glycol mixtures have inherent low heat transfer performance. Passive heat transfer techniques by extended surfaces increases the size and cost of the equipment. Hence there is a strong need to develop new kinds of fluids with substantially improved thermal conductivities and heat transfer capabilities to manage the thermal loads in heat transfer equipments. Nanoparticles suspended in conventional heat transfer fluid are known as nanofluids and this term of nanofluid was coined for the first time by Choi (1995) at the Argonne National Laboratory in Nanofluids poses some unique features different from conventional two-phase flow mixture, offer large total surface area and exhibit better thermopysical properties like thermal conductivity, specific heat. Fluids containing nanoparticles are expected to give more thermal conductivity than those of conventional fluids. The concept of enhancement of thermal conductivity by particle suspension in the fluid dates back to 1881 with Maxwell s theoretical work on thermal conductivity of coarse grained particles suspensions in fluids. Particles of micro or milli-size suspension in fluids cause erosion, clogging of passages and sedimentation in the equipments and was studied by Wasp (1977). Ahuja (1975) and Liu et al. (1988) carried their investigations on practical implication of hydrodynamics and heat transfer of slurries modern technology facilitates to produce, process and characterise materials with average crystalline sizes below 100 nm. Argonne National Laboratory has produced nanofluids and conducted proof test (Eastman et al, 1995). Nanoparticles of Al 2 O 3 and CuO nanopowder have exhibited excellent dispersion quality and increased thermal conductivity when suspended in heat transfer fluids like water, oils and glycols mixtures. Brownian motion of the particles and large surface area are supposed to be responsible factors for enhanced thermal conductivity of nanofluids. A detailed measurement of thermal conductivity of Al 2 O 3 and CuO particles dispersed in ethylene glycol and water base fluids was done up by Lee et al. (1999). Thermal conductivities of nanofluids are measured using transient hot wire method. A considerable improvement in thermal conductivities of the nanofluids was noticed and these techniques created an interest in heat transfer studies using nanofluids.

3 Temperature dependent thermal conductivity enhancement 151 A recent study by Xuan and Li (2000) revealed that particles as large 100 nm can also offer fluid stability with small amount of laurate salt as dispersant. Maxwell (1881) developed a classical theory of thermal conductivity of spherical particles and Hamilton and Crosser (1962) modified this theory for non-spherical particles suspended in fluids. Lee et al. (1999) later confirmed that the Hamilton and Crosser models agree with Al 2 O 3 /water or ethylene glycol nanofluids but fails with CuO nanofluids. The possible reason attributed for the failure of Hamilton and Crosser model to predict the effective thermal conductivity CuO nanofluids is smaller size of CuO nanoparticles when compared to the size of Al 2 O 3 nanoparticles. Thermal conductivity of Cu nanoparticles in water is enhanced by Xuan and Li (2000). Eastman et al. (2001) brought out surprising results that nanoparticles of size of less than 10 nm can result in about 40% thermal conductivity enhancement with only 0.3% volume fraction of nanoparticles. The abnormal increase in the effective thermal conductivity of nanofluids with smaller size nanoparticles was not explained by Maxwell and Crosser s models because they did not take into account the increased surface to volume ratio of nanoparticles. This ratio increases with decrease in nanoparticle size. In all the above investigations, water or ethylene glycol was used as base fluids for measurement of thermal conductivity of nanofluids at room temperature. Das et al. (2003) reported a four fold enhancement in the thermal conductivity of CuO nanoparticles dispersed in water in the temperature range of 21 C to 51 C. Thermal conductivity of propylene glycol-water base fluid was measured by Sun and Teja (2004) at different temperatures for different mole fraction of propylene glycol. Propylene glycol-water solutions are generally used as anti-freezing heat transfer fluids in the colder regions. No work is reported on the CuO nanofluids with propylene glycol as base fluid. Propylene glycol is chemically more stable, non-toxic and ensures stability to nanofluids when compared to ethylene glycol. In the present experimental work the effect of nanoparticle volume fraction and temperature on the thermal conductivity of propylene glycol-water mixture (30:70 mole %) and CuO nanofluids is investigated. 2 Preparation of nanofluids Preparation of nanofluids is a pre requisite for measuring the thermo physical properties of nanofluids. In the present work, CuO nanoparticles of less than 50 nm and particle density of 6.3 gm/cc are used for the preparation of nanofluids. The amount of CuO nanopowder required for test sample of base fluid is calculated using law of mixture in terms of percentage of volume fraction, density of CuO nanoparticles and density of propylene glycol-water mixture base fluid. A sensitive balance with 0.1 mg resolution is used to weigh the nanoparticles and then the particles are suspended in propylene glycol and water mixture (60:40 mols%) in volume fractions in the range of 0.025, 0.1, 0.4, 0.8, and 1.25%. The CuO nanoparticles agglomerate and form clusters. Scanning electron microscopic image of CuO nanoparticles is shown in the Figure 2. The test samples were subjected to magnetic stirring and then an ultrasonic vibration to disperse the CuO nanoparticles in the base fluid. Nanoparticles agglomerates observed to be in the nano size when suspended in the base fluid. No sediment is observed even after ten hours. No surfactant is used as it hampers the properties of the nanofluids. In general nanofluids can be stabilised by adding suitable agent like oleic acid or laurate salt when used in practical

4 152 M.T. Naik and G.R. Janardhana applications. The nanofluids thus prepared are assumed to be an isentropic and the thermo physical properties are uniform and constant with time all through the fluid sample and the nanofluids considered as Newtonian fluids. 3 Experimental set up and procedure Description The experimental setup to measure the thermal conductivity of nanofluid is shown schematically (Siam Sunder and Sharma, 2008) in Figures 1(a) and 1(b) is the test section wherein nanofluid is injected. The experimental setup consists of test section which is cooled by cooling water by continuous supply of the cooling water at a rate of 3 l/min. Test section is heated with the help of DC power supply. The temperatures are measured in the test section through a number of thermocouples placed in test fluid and inside surface of the test fluid chamber followed by a filter which is finally fed to the data acquisition system comprising of a card for logging the measured data. The data logger is in turn interfaced to the computer with proper software for online display that is required to assess the recording data. Since, in the present experiments the prime objective is to observe the effect of temperature on the enhancement of thermal conductivity, the control of temperature of the heater is controlled with the dimmer stat. The test section is a flat cylindrical cell as shown in Figure 2. The plunger diameter is 39 mm and effective length of the plunger is 110 mm. Nominal radial clearances between plug and jacket (r) is 0.3 mm. Nominal resistance of heating element is Effective area of conducting path through the fluid is square meters. A voltmeter, variable transformer and digital temperature indicator is part of the console. The heater dimmer is having the flexibility to increase the voltage up to 220 V. All the electrical components are earthed and protected with circuit breakers as part of safety measures. Figure 1 (a) Schematic diagram of experimental setup (b) test section (a) (b)

5 Temperature dependent thermal conductivity enhancement 153 Figure 2 SEM Image of CuO nanoparticles cluster prier to the preparation of nanofluids The fluid whose thermal conductivity is to be measured is filled in the small radial clearance between a heated plug and water cooled jacket. The clearance is small enough to prevent natural convection in the fluid and the fluid is presented as a lamina of face area and the thickness to the heat transfer of heat from the plug to the jacket. The plug is machined from aluminium and contains a cylindrical element whose resistance at the working temperature is accurately measured. A thermocouple is inserted in the plug close to its external surface and the plug also has ports for the introduction and venting of the fluid under test. The plug is held centrally in the water jacket by O rings which seal the radial clearance. The jacket is constructed from brass and water inlet and drain connection and thermocouple is carefully fitted in the inner sleeve. A small console is connected by flexible cables to the plug/jacket assembly and provides for the control of the voltage supplied to the heating element. An analogue voltmeter enables the power to be determined and a digital temperature indicator with 0.1 resolution display the temperature of the plug/jacket surfaces. Operation The incidental heat transfers in the unit are determined by using air in the radial space. Once calibrated the results may be carefully preserved and used subsequently. Cooling water is passed through the jacket and adjusted the heat input to give small temperature difference across the air lamina. The voltage and temperature are noted after ensuring steady state. The rate of heat transfer is then calculated using the relation { air m } Q = k d LΔt Δr The difference between heat input (volts 2 /resistance) and the calculated value, is the incidental heat transfer at the given temperature. This may be repeated for other plug and jacket temperatures and the calibration curve showing the incidental heat transfer against plug/jacket temperature difference may then be drawn and kept for reference.

6 154 M.T. Naik and G.R. Janardhana Determination of thermal conductivity The unit is cleaned and reassembled. The fluid to be tested is then introduced into the radial space. Sufficient water is passed through the jacket and the heater is adjusted to give a reasonable temperature difference and heat transfer rate. After steady state, the rate of heat transfer and plug/jacket temperature may be observed. After deducting the incidental heat transfer at the given temperature difference it is known that the remainder is passing through the fluid lamina. The conductivity of the fluid may then be readily calculated. Error estimation The main source of experimental uncertainty in the measurement of thermal conductivity is the accuracy of thermocouples. In the present experimental work thermocouples of 0.1 C were used. To make the measurements more accurate the experiment is first calibrated by measuring the thermal conductivities of air and distilled water over the temperature range of 20 C to 65 C. The maximum deviation of thermal conductivities from standard values was estimated to be 4.8% over the temperature range considered. 4 Theoretical models Several classical models like Maxwell, Hamilton and Crosser, Bruggeman and Wasp are available to predict the effective thermal conductivities of suspension of solid particles dispersed in continuous medium. The Maxwell developed a model to predict the effective thermal conductivity of liquid-solid suspension for low volume concentration of spherical nanoparticles. Thermal conductivity studies on nanofluids by different research groups reported considerable enhancements in the thermal conductivity of nanofluids when compared to their base fluids. Later on other models attributed to Wang et al. (2003) and Xuan et al. (2000), appeared in the literature to predict the effective thermal conductivities of nanofluids. Some of the models for effective thermal conductivity are listed below. Maxwell-Eucken (Maxwell, 1891) model (Mamut, 2006) (( ( λ λ )) ( ( λ λ ) (( ( λ λ )) (( λ λ ) 1+ 2Vd 1 c d 2 c d + 1 λm = λc 1 Vd 1 c d c d + 1 λc, λd, λm are the thermal conductivity coefficients for continuous, dispersed and mixture phase of the fluids. Vd Vd = Vd + Vc Wasp (1977) model (1) ( ) ( ) λd + 2λc 2Vd λc λd λm = λc λd + 2λc+ 2Vd λc λd (2)

7 Temperature dependent thermal conductivity enhancement 155 Hamilton and Crosser (1962) model for liquid-solid mixtures when the ratio of conductivity is larger than 100 ( 1) ( 1)( ) d + ( n 1) c+ Vd( c d) λd + n λc Vd n λc λd λm= λc λ λ λ λ n = 3 ϕ (3) where n is the shape factor and φ is sphericity which is equal to surface area of a sphere with a volume equal to that of the average particle/surface area of the average particle. Bruggeman (Hui et al., 1999) model which is valid for spherical particles and considered interaction between particles where 1 λ f λeff = ( 3φ 1) λp + ( 2 3φ) λ f + Δ 4 (4) 4 ( 3φ 1) 2 ( λp λ f ) 2 ( 2 3φ) 2 2( 2 9φ 9φ 2 )( λp λ f ) Δ= + + where φ is the particle volume concentration, λp and λf are particle and base fluid thermal conductivity. 5 Results and discussion To ensure accuracy in the measurement, thermal conductivity of propylene glycol and water mixture which constitutes the base fluid, was measured at different temperature, without adding CuO nanoparticles in the base fluid. The results obtained in the experiment were compared with the PG-water data available in the American Society of Heating and Refrigerating and Air-Conditioning Engineers (ASHRAE) Handbook (2009). The experimental values obtained match very closely with the ASHRAE data and is as shown in the Figure 3. The thermal conductivity of propylene glycol base fluid on the mol basis is also measured and the results are compared with the thermal conductivity data reported by Sun and Teja. Our conductivity data matches very closely with their data as shown in the Figure 4. The two comparisons ensured the reliability of the equipment for measurement of thermal conductivity of the CuO nanofluids. The thermal conductivity of CuO nanofluids in the concentration range of 0.025, 0.1, 0.4, 0.8 and 1.2 percentage volume fraction was measured. The thermal conductivity of observed to be increasing linearly with increase in the temperature of nanofluids and is as shown in the Figure 5.The experimental results revealed that thermal conductivity is also increases with increase in the Nano particles concentration as can be seen in the Figure 6. It is evident that the effect of nanoparticle concentration on the thermal conductivity of nanofluids is not much pronounced when compared to the effect of temperature of nanofluids. It can also be observed from the figure that more enhancement in the thermal conductivity is observed at higher temperatures than at lower temperatures of nanofluids.

8 156 M.T. Naik and G.R. Janardhana For example for a nanofluid of 0.1% volume fraction the thermal conductivity is increased from 1.8% at 25 C to 15.97% at 65 C, where as the enhancement in the thermal conductivity of 1.2% volume fraction nanofluids is from10.9% to 43.37% at the same temperatures. Figure 3 Comparison of ASHRAE thermal conductivity values of propylene glycol and water mixture (60:40 by volume) and experimental data (see online version for colours) Figure 4 Comparison of present experimental thermal conductivity of PG/water mixture base fluid with other experimental data (see online version for colours)

9 Temperature dependent thermal conductivity enhancement 157 Figure 5 Variation of thermal conductivity of PG/water-CuO nanofluids with temperature for different volume concentration (see online version for colours) Figure 6 Thermal conductivity ratio of PG/water mixture-cuo nanofluids with volume fraction for different temperatures (see online version for colours) The effect of particle volume concentration in the nanofluids also plays a role on the thermal conductivity of nanofluids. At temperature of 25 C, the enhancement in the thermal conductivity for 0.1 and 1.2 percentage volume fraction nanofluid is 1.8 %and 10.92% respectively. The Figure 6 gives the relationship between volume fraction and thermal conductivity ratios (ratio of nanofluid thermal conductivity to base fluid thermal

10 158 M.T. Naik and G.R. Janardhana conductivity) of CuO nanofluids at different temperatures. In the present paper the classical thermal conductivity models developed by Maxwell, Wasp and Bruggeman which were reviewed by Murshed et al. (2008) for predicting thermal conductivity of Al 2 O 3 nanofluids is used, because suitable correlations are not available in the literature to predict thermal conductivity of CuO nanofluids.. The classical models under predicted the thermal conductivity of Glycol based CuO nanofluids in the present investigation as CuO nanofluids exhibited enhanced thermal conductivity as can be observed from the Figure 7. The probable parameters which promote thermal conductivity enhancement of CuO nanofluids include stochastic and Brownian motion of nanoparticles in the fluid. Temperature of the nanofluid increases the particle random movements. Thermal conductivity is a surface phenomenon and the increased surface area of the ultra fine CuO nanoparticles is another factor which is responsible for enhanced thermal conductivity of CuO nanofluids. Figure 7 Enhancement of thermal conductivity ratio with different volume concentration of CuO nanofluids and comparison with models (see online version for colours) 6 Conclusions The thermal conductivity property of PG/water base fluid was measured and found to be in good agreement with the data of ASHRAE and other experimental data reported in the literature. The CuO nanofluids are prepared and the effect of temperature on the thermal conductivity of PG/water-CuO nanofluids is presented in the present experimental investigation. For a volume concentration, a 10.9% to 43% enhancement in the thermal conductivity of nanofluids was observed in the temperature range 25ºC to 65ºC, for the nanofluid of 1.2% volume concentration. Both temperature and nanoparticle concentration parameters are attributed to the enhanced thermal conductivity of CuO nanofluids. The present experimental data is compared with thermal conductivity models.

11 Temperature dependent thermal conductivity enhancement 159 All the models mentioned in this paper which are valid for Al 2 O 3 nanofluids are used as there are no suitable models or correlations available in the literature for CuO nanofluids to predict its thermal conductivity. The present experimental work of CuO nanofluids exhibited higher thermal conductivities when compared with thermal conductivity models. A more comprehensive theory needs to be developed to predict the effective thermal conductivity of the PG/water-CuO nanofluids. References Abuja, A.S. (1975) Augmentation of Heat Transport in Laminar Flow of Polystyrene Suspension: Experiments and Results, J. Appl. Phys., Vol. 46, No. 8, pp ASHRAE Hand Book (2009) Fundamentals. Choi. S.U.S. (1995) Developments and applications of non-newtonian flows, ASME, FED-Vol. 231/MD-Vol. 66, Washington. Das. S.K., Nandy, P., Thiesen, P. and Roetzel, W. (2003) Temperature dependence of thermal conductivity enhancement for nanofluids, ASME Journal of Heat Transfer, Vol. 125, pp Eastman, J.A., Choi, U.S., Li, S., Yu, W. and Thompson, L.J. (2001) Anomalously increased effective thermal conductivities of ethylene glycol-based nanofluids containing copper nanoparticles, Appl. Phys. Lett., Vol. 78, No. 6, pp Hamilton, R.L. and Crosser, O.K. (1962) Thermal conductivity of heterogeneous two component systems, I & EC Fundamentals, Vol. 1, No. 3, pp Hui, P.M., Zhang, X., Markworth, A.J. and Stroud, D. (1999) Thermal conductivity of graded composites; numerical simulations and an effective medium approximation, Journal of Material Science, Vol. 34, pp Lee, S., Choi, U.S., Li, S. and Eastman, J.A. (1999) Measuring thermal conductivity of fluids containing oxide nanoparticles, ASME J. Heat Transfer, Vol. 121, pp Liu, K.V., Choi, U.S. and Kasza, K.E. (1988) Measurement of pressure drop and heat transfer in turbulent pipe flows of particulate slurries, Argonne National Laboratory Report, ANL Mamut, E. (2006) Characterization of heat and mass transfer properties of nanofluids, Rom. Jour. Physics, Vol. 51, Nos. 1 2, pp Maxwell, J.C. (1881) A Treatise on Electricity and Magnetism, 2nd ed., p.435, Clarendon Press, Oxford, U.K. Maxwell, J.C. (1891) A treatise on electricity and magnetism, Unbridged 3rd ed., Clarendon Press, Oxford, UK. Murshed, S.M.S, Leong, K.C. and Yang, C. (2008) Thermophysical and electro kinetic properties off nanofluids a critical review. Siam Sunder, L and Sharma, K.V. (2008) Thermal conductivity enhancement of nano particles in distilled water, International Journal of Nanoparticles, Inderscience Enterprises Ltd., No. 1. Sun, T. and Teja, A.S. (2004) Density, viscosity and thermal conductivity of aqueous solutions of propylene glycol, dipropylene glycol, and tripropylene glycol between 290 K and 460 K, 1311 J. Chem. Eng. Data, Vol. 49, pp Wang, B.X., Zhou, L.P. and Peng, X.F. (2003) A fractal model for predicting the effective thermal conductivity of liquid with suspension of nanoparticles, International Journal of Heat and Mass Transfer, Vol. 46, pp Wasp, F.J. (1977) Solid-liquid slurry pipeline transportation, Trans. Tech., Berlin. Xuan, Y. and Li, Q. (2000) Heat transfer enhancement of nanofluids, Int. J. Heat Fluid Flow, Vol. 21, pp