Thermal conductivity and viscosity of stabilized ethylene glycol and

International Communications in Heat and Mass Transfer 56 (2014) 86–95

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International Communications in Heat and Mass Transfer journal homepage: www.elsevier.com/locate/ichmt

Thermal conductivity and viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids for heat transfer applications: An experimental study☆ L. Syam Sundar a,⁎, E. Venkata Ramana c, Manoj K. Singh a,b,⁎, Antonio C.M. Sousa a a b c

TEMA, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal Aveiro Institute of Nanotechnology, University of Aveiro, 3810-193 Aveiro, Portugal I3N, Department of Physics, University of Aveiro, 3810-193 Aveiro, Portugal

a r t i c l e

i n f o

Available online 14 June 2014 Keywords: Nanofluid Thermal conductivity Viscosity Ethylene glycol–water mixture Enhancement

a b s t r a c t In this work nanofluids have been prepared by dispersing Al2O3 nanoparticles in different base fluids such as 20:80%, 40:60% and 60:40% by weight of ethylene glycol (EG) and water (W) mixtures. Thermal conductivity and viscosity experiments have been conducted in temperatures between 20 °C and 60 °C and in volume concentrations between 0.3% and 1.5%. Results indicate that thermal conductivity of nanofluids increases with increase of volume concentrations and temperatures. Similarly, viscosity of nanofluid increases with increase of volume concentrations but decreases with increase of temperatures. Among all the nanofluids maximum thermal conductivity enhancement was observed for 20:80% EG/W nanofluid about 32.26% in the volume concentration of 1.5% at a temperature of 60 °C. In a similar way among all the nanofluids maximum viscosity enhancement was observed for 60:40% EG/W nanofluid about 2.58-times in the volume concentration of 1.5% at a temperature of 0 °C. The classical Hamilton–Crosser and Einstein models failed to predict the thermal conductivity and viscosity of nanofluids with influence of temperatures. Hence correlations have been proposed for the estimation of thermal conductivity and viscosity of nanofluids. The potential heat transfer benefits of nanofluids in laminar and turbulent flow conditions have been explained for conditions of fixed mass flow rate and geometry. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Single phase heat transfer fluids such as water, engine oil, transformer oil, and ethylene glycol are commonly used in many industrial sectors including power generation, heating and cooling processes, transportation, chemical processes, etc. However these fluids often cannot meet the performance requirements of mechanical devices such as heat exchangers and condensers because of their low thermal conductivity. The properties of these fluids may be enhanced by suspending small size metallic, non-metallic or polymeric particles to form colloidal solutions. Choi [1] created dispersions of solid nanosized particles in fluids and identified them as nanofluids. The properties such as thermal conductivity, viscosity, density and specific heat are very important for the estimation of convective heat transfer coefficient. Typically properties such as thermal conductivity and viscosity are estimated from the experimental study, whereas density and specific heat are estimated from solid–fluid homogeneous equations. Many researchers have obtained that thermal conductivity of nanofluids increases with increase of particle concentrations and temperatures by experiments (Eastman ☆ Communicated by W.J. Minkowycz. ⁎ Corresponding authors. E-mail addresses: [email protected] (L. Syam Sundar), [email protected] (M.K. Singh).

http://dx.doi.org/10.1016/j.icheatmasstransfer.2014.06.009 0735-1933/© 2014 Elsevier Ltd. All rights reserved.

et al. [2]; Mintsa et al. [3]; Masuda et al. [4]; Saleh et al. [5]; Putnam et al. [6]; Das et al. [7]; Murshed et al. [8]). Pastoriza-Gallego [9] measured both thermal conductivity and viscosity of Al2O3/EG nanofluid in temperatures of 283.15 K to 323.15 K at 25% mass concentration and they found 19% thermal conductivity increment and 2-times viscosity increment compared to base fluid. Chandrasekar et al. [10] measured thermal conductivity and viscosity of Al2O3/water nanofluids and observed 9.7% thermal conductivity enhancement at 3 vo.% and 2.36-times viscosity enhancement at 5 vo.% compared to base fluid. Wang et al. [11] dispersed Al2O3 nanoparticles (28 nm) in different base fluids and obtained 16% and 12% thermal conductivity enhancements for 5.5 vo.% and 3 vo.% respectively. Xie et al. [12] prepared Al2O3 nanoparticles (60.4 nm) dispersed in water and prepared stable solution by adjusting pH and obtained thermal conductivity enhancement of 21% for 5 vo.% and 14% at 3.2 vo.%. Li and Peterson [13] observed 1.52-times thermal conductivity enhancement with 6 vo.% of CuO/water nanofluid, whereas 1.3-times thermal conductivity enhancement with 10 vo.% of Al2O3/water nanofluid at a temperature of 34 °C. Kole and Dey [14] of engine oil/Al2O3 nanofluid demonstrated that there is a transition from Newtonian characteristics for the base fluid to non-Newtonian behavior with increasing content of Al2O3 in the engine coolant. They also mentioned that viscosity increases with an increase in concentration and decreases with an increase in

L. Syam Sundar et al. / International Communications in Heat and Mass Transfer 56 (2014) 86–95

temperature. Pang et al. [15] found 10.74% and 14.29% thermal conductivity enhancements for 0.5 vo.% of Al2O3/methanol and SiO2/methanol nanofluids, respectively. Zhang et al. [16] considered Au/toluene, Al2O3/ water, TiO2/water, CuO/water and CNT/water nanofluids in temperatures ranging from 5 °C to 50 °C and observed that thermal conductivity increases with increase of particle concentrations. Timofeeva et al. [17] have studied both thermal conductivity and viscosity of Al2O3/water and Al2O3/ethylene glycol nanofluids with influence of particle diameters (11 nm, 20 nm and 40 nm). Leong et al. [18] proposed a new thermal conductivity model for nanofluids by considering the effects of the interfacial layer at the solid/liquid interface and found that their model predicts better results compared to Hamilton–Crosser [19] and Tinga [20] models. Most of the researchers have used water, ethylene glycol and engine oil as base fluids for the preparation of nanofluids. A great demand of energy is needed for heating industrial and residential buildings in the cold regions of the world such as Alaska, Canada, Northern Europe and Russia. Due to long winter climate conditions ethylene glycol or propylene glycol mixed with water in different weight percentages is generally used to lower the aqueous freezing point of heat transfer medium (McQuiston [21]). Such heat transfer fluids are used in baseboard heaters in homes, heat exchangers, and automobiles and in industrial plants in cold regions and can also withstand very low temperatures. Under low temperatures ethylene glycol mixtures have better heat transfer characteristics than propylene glycol mixtures (ASHRAE [22]). Namburu et al. [23] first time prepared CuO nanofluid by considering 60% of EG and 40% of water as a base fluid — measured viscosity in temperatures from −35 °C to 50 °C and in volume concentration up to 6% and proposed viscosity correlation. Vajjha and Das [24] also prepared nanofluids by considering 60% of EG and 40% of water as base fluid by dispersing Al2O3, CuO and ZnO and estimated thermal conductivity up to 10% volume concentration in temperatures from 298 K to 363 K. Yiamsawas et al. [25] investigated viscosity of TiO2 and Al2O3 nanoparticles suspended in 20% of EG and 80% of water in volume concentrations between 0% and 4% and temperature range of 15–60 °C and observed that theoretical models are not suitable to predict the viscosity of nanofluids. Beck et al. [26] measured thermal conductivity of seven nanofluids containing Al2O3 nanoparticles with particle size from 8 nm to 282 nm dispersed in water, ethylene glycol and 50% of water + 50% of ethylene glycol mixture and observed that thermal conductivity enhancement for these nanofluids decreases as the particle size decreases below 50 nm. Dehkordi et al. [27] measured thermal conductivity and viscosity of Al2O3 nanoparticles, at low volume concentrations of 0.01–1.0% dispersed in the mixture of ethylene glycol and water (mass ratio, 60:40) using sodium dodecylbenzene sulfonate (SDBS) as a surfactant and observed better dispersion. Naik and Sundar [28] prepared CuO nanofluid by considering 70% of PG and 30% of water mixtures as a based fluid and obtained 10.9% and 43.37% of thermal conductivity enhancements at 1.2 vo.% at 25 °C and 65 °C, respectively. Most of the researchers have considered single weight concentration of EG/W (60:40% and 50:50%) and PG/W (70:30%) mixtures as a base fluid for the preparation of nanofluids and property estimation. The thermal conductivity and viscosity of various weight concentrations of EG and water mixture based nanofluids are not available in the literature. In this regard, the present study focuses on the preparation and estimation of properties for Al2O3 nanoparticles dispersed in various weight concentrations of EG and water mixtures. Thorough understandings of the properties are very important for successful application in cold regions of the world. Three weight concentrations of base fluids such as 20:80%, 40:60% and 60:40% EG/W were used for the preparation of nanofluids and properties such as thermal conductivity and viscosity were estimated experimentally as a function of volume concentrations and temperatures. Density and specific heat of the nanofluids were calculated based on the solid–fluid homogeneous equations. New thermal conductivity and viscosity correlations have been proposed. Theoretical analysis was performed for the figure

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of merit presenting the effectiveness of EG/W mixture nanofluids in the laminar and turbulent flow regimes by using thermal properties based on the Prasher et al. [29] model and Mouromtseff number (Simons [30]). 2. Preparation of nanofluids The nanoparticles of Al2O3 and ethylene glycol (Sigma-Aldrich Chemicals, USA), double distilled water (Milli-Q) were used for the preparation of nanofluids. XRD was performed by Siemens, D-500, 45 kV, and 40 mA X-ray diffractometer by Cu–Kα radiation in the range of 20°–80° at a rate of 2.5°/min and surface morphology was performed by Hitachi SU-70 SEM. The physical properties of nanoparticles and base fluids were presented in Table 1. The nanofluids were prepared by considering 20:80%, 40:60% and 60:40% EG/W as a base fluid in the volume concentrations of 0.3%, 0.6%, 0.8%, 1.0% and 1.5%. Each nanofluid sample contains 40 ml and the required amounts of nanoparticles were estimated based on the volume concentration (ϕ) and density of nanoparticles (3970 kg/m3). No surfactant was used in the nanofluid preparation. Sonication of the nanofluid samples was performed to obtain better dispersion of nanoparticles and breaking down of the particle sedimentation. Ultrasonic cleaner (bath) manufactured by Branson Ultrasonic Cleaner, USA (model: 5510) with an amplitude of 40 kHz and 490 W was used in the present study. The optimization of ultrasonic time is required for higher stability of nanofluids, and zeta potential tests for the estimation of stability of nanofluid are crucial. Zeta potential for 1.5% volume concentration of 20:80%, 40:60% and 60:40% EG/W nanofluids with different sonication times (30, 60, 90, 180 min) was measured by Malvern ZSNanoS analyzer (Malvern Instrument Inc., London, UK) to obtain the optimize sonication time. The density (ρnf = (1 − ϕ)ρbf + ϕ ρp) and specific heat (Cpnf = (1 − ϕ)Cpbf + ϕ Cpp) of 20:80%, 40:60% and 60:40% EG/W nanofluids at a temperature of 20 °C were represented in Table 2. 3. Thermal conductivity of nanofluids 3.1. Models and correlations Numerous experimental and theoretical studies have been conducted to predict the effective thermal conductivity of solid particles suspended in base fluids. The Maxwell [31] model for thermal conductivity of nanofluid (knf) is derived based on the thermal conductivities of solid (kp) and base fluid (kbf) and volume concentration (ϕ) is given for micro or millimeter sized particles. 3 2 kp þ 2kbf þ 2ϕ kp −kbf knf 5 ¼4 kbf kp þ 2kbf −ϕ ðkp −kbf

ð1Þ

This model is good for spherical shaped particles with low particle volume concentrations. The Hamilton–Crosser model [19]: 3 2 kp þ ðn−1Þkbf −ðn−1Þϕ kbf −kp knf 5 ¼4 kbf kp þ ðn−1Þkbf þ ϕ ðkbf −kpÞ

ð2Þ

where (n) is the empirical shape factor given by λ3, and (λ) is the particle sphericity (λ = 3), defined as surface area of a sphere (with the same Table 1 Physical property of Al2O3 nanoparticles and base fluids. S. no.

Nanoparticles/base fluid

ρ (kg/m3)

k (W/m K)

μ (mPa s)

Cp (J/kg K)

1 2 3 4

Al2O3 20:80% EG/W 40:60% EG/W 60:40% EG/W

3890 1029.72 1059.68 1086.27

30.0 0.492 0.404 0.334

– 1.65 2.96 5.38

880 3815 3468 3084

88


Table 2 Thermophysical properties of nanofluids at a temperature of 20 °C. Base fluid

20:80%

Property

Volume concentrations (%)

k (W/m K) μ (mPa s) ρ (kg/m3) Cp (J/kg K) k (W/m K) μ (mPa s) ρ (kg/m3) Cp (J/kg K) k (W/m K) μ (mPa s) ρ (kg/m3) Cp (J/kg K)

40:60%

60:40%

0

0.3

0.6

0.8

1.0

1.5

0.492 1.55 1029 3815 0.404 2.94 1059 3468 0.334 5.75 1086 3084

0.542 1.582 1038 3806 0.434 3.50 1068 3460 0.347 7.59 1095 3077

0.552 1.622 1046 3797 0.441 4.05 1076 3453 0.353 9.42 1103 3071

0.562 1.695 1052 3791 0.452 5.02 1082 3447 0.361 11.9 1108 3066

0.569 1.767 1058 3786 0.459 6.56 1088 3442 0.369 14.38 1114 3062

0.578 2.176 1073 3771 0.463 8.89 1102 3429 0.371 18.9 1128 3051

volume as the given particle) to the surface area of the particle. Bruggemen [32] proposed an implicit model for effective thermal conductivity of solid–liquid mixtures, taking into account the interactions among the randomly distributed particles. " ϕ

# " # kp −keff kbf −keff þ ð1−ϕÞ ¼0 kp þ 2keff kbf þ 2knf

ð3Þ

This model can be applied to spherical particles with no limitations on the particle volume concentrations. Murshed et al. [33] employed the Bruggemen model [32] to predict thermal conductivity of nanofluids using the following formulation: i k pffiffiffiffi 1h knf ¼ ð3ϕ−1Þkp þ ð2−3ϕÞkbf þ w Δ 4 4 " !2 !# k k 2 2 2 p p Δ ¼ ð3ϕ−1Þ þ ð2−3ϕÞ þ 2 2 þ 9ϕ−9ϕ : kbf kbf

ð4Þ

mean-free path (l bf ) was used in their paper for water for the entire tested temperature range. Vajjha and Das [24] proposed the following thermal conductivity correlation for 60:40% EG/W based Al2O3, ZnO and CuO nanofluids in the temperature range from 298 K to 363 K. 3 k þ 2kbf −2ϕ kbf −kp 5 þ 5 104 β ϕ ρ C p k ¼ kbf 4 bf kp þ 2kbf þ ϕ kbf −kp 2

kT f ðT; ϕ; etc:Þ ðρ dÞp

T −2 −3 f ðT; ϕÞ ¼ 2:8217 10 ϕ þ 3:917 10 To −2 −3 þ −3:0669 10 ϕ−3:91123 10

−1:07304

; 1≤φ≤10% for Al2 O3 nanofluid

−1:07304

; 1≤φ≤7% for ZnO nanofluid

β ¼ 8:4407ð100ϕÞ An alternative formula for calculating thermal conductivity of nanofluids was introduced by Yu and Choi [34], which is expressed in the follow equation: 2 3 kp þ 2kbf þ 2 ϕ kp −kbf ð1 þ βÞ3 knf 5 ¼4 kbf kp þ 2kbf − ϕ kp −kbf ð1 þ βÞ3

!0:7476

−0:9446

β ¼ 9:881ð100ϕÞ

; 1≤φ≤6% for CuO nanofluid

3.2. Experimental study

dbf dp

!0:3690 ϕ

0:7460

ð6Þ

where (dbf) is the base fluid molecular diameter, Prandtl number μ C ρ KT Pr ¼ bfk pbf , Reynolds number Re ¼ 3πμbf 2 l and (lbf) is the meanbf

β ¼ 8:4407ð100ϕÞ

ð5Þ

where (β) is the ratio of the nano-layer thickness to the original particle radius and generally β = 0.1 is used to calculate the thermal conductivity of the nanofluid. Chon et al. [35] proposed an equation for the estimation of thermal conductivity of Al2O3/water nanofluids based on experimental data by using Buckingham–Pi theorem with a linear regression scheme and they conduced that the Brownian motion of nanoparticles present in the base fluid is the most important factor in enhancing thermal conductivity of nanofluids. The correlation is given as: knf 1:2321 0:9955 kp ¼ 1 þ 64:7Re Pr kbf kbf

ð7Þ

bf bf

free path for the base fluid. A constant value of 0.17 nm for the

KD2 Pro Thermal Properties Analyzer (Decagon Devices, Inc., USA) was used for measuring the thermal conductivity of nanofluids. The KD2 Pro measurement is working on the basis of the transient hot wire method. It consists of microcontroller and needle sensor. The sensor needle used was KS-1 which is made of stainless steel having a length of 60 mm and a diameter of 1.3 mm. The sensor needle measures the thermal conductivity with an accuracy of ± 2.5% in the range of 0.2–2 W/m K and also meets the standards of both ASTM D5334 [36] and IEEE 442 [37]. The thermal conductivity was measured in temperatures between 20 °C and 60 °C. In order to obtain precise measurements and to get the stable temperature the needle was installed in the package of the water jacket, which was connected to the water bath. Water bath (Julabo temperature controller water bath, Germany) with ± 0.1 °C accuracy was used for stabilizing the temperature of samples with circulating water through the water jacket. In this investigation only thermal conductivities of KD2 Pro with correlation coefficient equal to 1 are kept and considered. The experiment was performed five times for each sample and condition, and a data point reported in this study thus represents an average of five measurements with an estimated error of ±2%.


89

of nanofluids is derived based on shear stress (τ), viscosity (μ) and

shear strain rate γ˙ .

4. Viscosity of nanofluids 4.1. Models and correlations

5. Results and discussion The Einstein model [38] is the mostly referred equation to predict the viscosity of nanofluids and the drawback is that it predicts only very low nanoparticle concentrations (φ ≤ 0.02 %). μ nf ¼ ½1 þ 2:5 ϕ μ bf

ð8Þ

Brinkman [39] has extended the Einstein's formula valid up to volume concentration less than 4.0%. μ nf μ bf

¼

1 ð1−ϕÞ2:5

ð9Þ

Batchelor [40] considered the effect due to the Brownian motion of particles for an isotropic suspension of rigid and spherical particles, and proposed: i μ nf h 2 ¼ 1 þ 2:5 ϕ þ 6:5 ϕ μ bf

ð10Þ

where (bf), (nf), (ϕ) and (μ) are base fluid, nanofluid, particle concentration and viscosity, respectively. The abovementioned theoretical models failed to predict the viscosity of nanofluid with effect of temperature. Researchers have proposed viscosity correlations for nanofluids as a function of volume concentrations and temperatures. Namburu et al. [41] prepared Al2O3 nanofluid by considering 60% of EG and 40% of water and measured viscosity in the temperature of −35 °C to 50 °C and in the particle concentrations from 1.0% to 10% and proposed correlation. −B T log μ bf ¼ A e

5.1. XRD and zeta potential Fig. 1a shows the XRD pattern of Al2O3 nanoparticles. The XRD result shows a high purity of γ-phase alumina nanoparticles, when compared to the JCPDS standard. The average crystallite size was calculated with Sherrer's equation [27] and it was obtained as 36 nm. Fig. 1b depicts that the shape of the nanoparticles is in spherical shape. Fig. 2a shows the particle size distribution obtained from DLS method. Diluted 60:40% EG/W nanofluid was used for size measurement and the obtained value is in the order of 30 nm, which is almost equal to the particle size estimated from Sherrer's equation [27]. The stability of nanofluids was expressed in terms of zeta potential. Sonication time is also important for proper dispersion of nanoparticles in base fluids. Fig. 2b shows the zeta potential of alumina nanofluids with respect to sonication time. By changing sonication time the stability of nanosuspension could be improved from poor to an almost good state. With less sonication time (30 min), the stability of the particles was poor, by increasing the sonication time the absolute value was increased above ±30 mV, which is considered an almost good stability of suspension [42]. It can be noticed that up to some time the ultrasonic vibration had broken down the particle sedimentation and later dispersed them uniformly in base liquids, which causes an increase in the stability of

ð11Þ

9 3 2 2 A ¼ −0:29956 ϕ þ 6:7388 ϕ −55:444 ϕ þ 236:11 R ¼ 0:9978 > > = >Al2 O3 nanofluid −6:4745 ϕ3 þ 140:03 ϕ2 −1478:5 ϕ þ 20341 2 ; B¼ R ¼ 0:9994 > 106

Yiamsawas et al. [25] developed a correlation that is presented to predict the viscosity of TiO2 and Al2O3 nanoparticles suspended in the mixture of EG/water (20:80) for practical applications. B C D

μ bf ¼ Aϕ T μ f

ð12Þ

A ¼ 0:837931; B ¼ 0:188264; C ¼ 0:089069; D ¼ 1:100945ðTiO2 nanofluidÞ A ¼ 0:891842; B ¼ 0:739192; C ¼ 0:099205; D ¼ 0:9844ðAl2 O3 nanofluidÞ

4.2. Experimental study AR-1000 rheometer (TA Instruments, UK) was used for measuring the viscosity of nanofluids. Plate-cone (40 mm and cone of 4°) geometry was used for measurements and temperature of the plate (PT-100 thermoresistance inside the Peltier) was controlled by Julabo temperature controller bath, Germany with an accuracy of ±0.1 °C. Some trial methods with a quantity of 26 ml nanofluid sample were finalized. Sample should not contain any air bubbles inside and the samples were loaded with pipette. The viscosity was measured in temperatures between 0 °C and 60 °C and the values were recorded at steady state conditions and 30 min was allowed to stabilize the temperature. The

governing equation τ ¼ μ γ˙ for verifying the Newtonian behavior

Fig. 1. (a) XRD pattern. (b) SEM results of Al2O3 nanoparticles.

90


Fig. 2. (a) Particle size distribution. (b) Nanofluid stability with respect to ultrasonic times. (c) Zeta potential for 20:80%. (d) Zeta potential for 40:60%. (e) Zeta potential for 60:40% nanofluid.

nanofluids. More sonication time retards repulsive forces between nanoparticles and reduces the stability of nanofluids. A two hour time was identified as an optimized sonication time for the preparation of nanofluids. The zeta potential was measured for 1.5% volume concentration of Al2O3 nanoparticles dispersed in 20:80%, 40:60% and 60:40% EG/W mixtures and the obtained values are 50.8 mV, 39.8 mV and 31.2 mV, respectively (Fig. 2c–e).

concentrations and temperatures. At 0.3 vol.%, thermal conductivity enhancement is 7% and at 1.5 vol.%, and thermal conductivity enhancement is 14.60% at a temperature of 20 °C compared to base fluid. Similarly, at 0.3 vol.%, thermal conductivity enhancement is 16% and at 1.5 vol.%, and thermal conductivity enhancement is 30.51% at a temperature of 60 °C compared to base fluid. The experimental thermal conductivity of 60:40% EG/W based nanofluid was shown in Fig. 6.

5.2. Thermal conductivity of nanofluids Thermal conductivity instrument was calibrated by introducing the known thermal conductivity of fluids such as 20:80%, 40:60% and 60:40% EG/W. The measured values were presented in Fig. 3 in comparison with the values obtained from ASHRAE [22] handbook. In the measured temperature range, the obtained thermal conductivity of all fluids was found ± 2.5% deviation with handbook values. The nanofluids with different volume concentrations were introduced into the instrument and the obtained data of 20:80% EG/W nanofluid was represented in Fig. 4 along with the data of base fluid. It is observed that, thermal conductivity of nanofluids increases with increase of volume concentrations and temperatures. At 0.3 vol.%, thermal conductivity enhancement is 11% and at 1.5 vol.%, and thermal conductivity enhancement is 17.47% at a temperature of 20 °C compared to base fluid. Similarly, at 0.3 vol.%, thermal conductivity enhancement is 18.6% and at 1.5 vol.%, thermal conductivity enhancement is 32.26% at a temperature of 60 °C compared to base fluid. The experimental thermal conductivity of 40:60% EG/W nanofluid was shown in Fig. 5. It is observed that thermal conductivity of nanofluid increases with increase of volume

Fig. 3. Comparison of experimental thermal conductivity of base fluids with ASHRAE [22] data.


91

Fig. 4. Thermal conductivity of 20:80% EG/W based nanofluid at different particle concentrations.


A similar trend in thermal conductivity enhancement with increase of volume concentrations and temperatures was obtained. At 0.3 vol.%, thermal conductivity enhancement is 3.89% and at 1.5 vol.%, and thermal conductivity enhancement is 11.07% at a temperature of 20 °C compared to base fluid. Similarly, at 0.3 vol.%, thermal conductivity enhancement is 11% and at 1.5 vol.%, and thermal conductivity enhancement is 27.42% at a temperature of 60 °C compared to base fluid. The reason of thermal conductivity enhancement is with the Brownian motion and micro-convection of particles in the base fluids. The thermal conductivity enhancement not only depends on the particle concentration and temperature but it also depends on the effect of base fluid. The 20:80% EG/W based nanofluid is more thermal conductivity enhancement compared to 40:60% and 60:40% based nanofluids. The ethylene glycol is poor thermal conductivity fluid compared to water. The percentage of ethylene glycol adding to water will suppress the thermal conductivity of water. More percentage of ethylene glycol addition will give less thermal conductivity. This can be observed from Table 1. Under the same volume concentration of 1.5% and temperatures between 20 °C and 60 °C, thermal conductivity ratio for 20:80% EG/W based nanofluid is 1.85-times, for 40:60% EG/W based nanofluid is 2.08-times and for 60:40% EG/W based nanofluid is 2.49-times respectively. So, the enhancement is not constant, it depends on the base fluids.

The experimental thermal conductivity ratio of 60:40% EG/W nanofluid is shown in Fig. 7 in comparison with the data of Vajjha and Das [24] for the same nanofluid. It is observed that, thermal conductivity ratio increases with increase of volume concentrations and temperatures. A similar trend has been observed by Vajjha and Das [24]. In the present study, very low volume concentrations was used, whereas Vajjha and Das [24] used high volume concentrations. Under the same particle loadings in all base fluids, the classical thermal conductivity models such as Maxwell model [31] and H–C model [19] and predicting the same thermal conductivity values and those are failed to predict thermal conductivity with effect of temperatures.


5.3. Viscosity of nanofluids The instrument was calibrated with 20:80%, 40:60% and 60:40% EG/W and the obtained data is indicated in Fig. 8 along with the data of ASHRAE [22] handbook. A maximum of ±3% deviation was observed between measured and theoretical values in the tested temperature range. Nanofluids have been verified, whether they have Newtonian or non-Newtonian like behavior. It is also observed that, the shear strain rate of all nanofluids increases linearly with increase of shear stress. This indicates that nanofluids are exhibiting Newtonian behavior in the

Fig. 7. Thermal conductivity ratio of 60:40% EG/W nanofluid is compared with Vajjha and Das [24].

92


Fig. 8. Comparison of experimental viscosity of base fluids with ASHRAE [22] data.

Fig. 10. Viscosity of 40:60% EG/W based nanofluid at different particle concentrations.

tested volume concentrations and temperatures. A similar nature of Newtonian behavior has been observed by Namburu et al. [23] by considering 60:40% EG/W based CuO nanofluid and Kole and Dey [14] with engine oil/Al2O3 nanofluid. Experimental viscosity data of 20:80% EG/W nanofluid was shown in Fig. 9 and it is observed that viscosity of nanofluids increases with increase of particle volume concentrations, but decreases with increase of temperatures compared to base fluid. The viscosity enhancement for 1.5% volume concentration is 1.37-times and 1.29-times in temperatures of 0 °C and 60 °C compared to base fluid respectively. The experimental viscosity of 40:60% EG/W nanofluid was shown in Fig. 10 along with base fluid. The viscosity enhancement for 1.5% volume concentration is 2.27-times and 2.05-times in temperatures of 0 °C and 60 °C compared to base fluid respectively. The experimental viscosity of 60:40% EG/W nanofluids was shown in Fig. 11 along with base fluid. The viscosity enhancement for 1.5% volume concentration is 2.58-times and 2.35-times in temperatures of 0 °C and 60 °C compared to base fluid respectively. At the same volume concentration (1.5%) of all nanofluids in low temperature (0 °C), the viscosity enhancement is higher compared to high temperature (60 °C). The dispersion of nanoparticles in base fluids causes the development of resistance between the fluid layers and helps to enhance the viscosity. This nature was observed in all the base fluids. Viscosity of nanofluids with the effect of base fluids in the present analysis indicates that 60:40% EG/W based nanofluids provided more viscosity than the

40:60% and 20:80% EG/W based nanofluids in the measured particle loadings and temperatures. This is also noticed that as per ASHRAE [22] handbook, 60:40% EG/W is more viscous than other 40:60% and 20:80% EG/W fluids. So, high viscous base fluid provides higher viscosity ratios than low viscous base fluids. Anoop et al. [43] have observed the same nature by dispersing Al2O3 nanoparticles in EG and water. Their results showed that EG based nanofluids are more viscous than the water based nanofluids. The experimental viscosity ratio for 20:80%, 40:60% and 60:40% EG/W nanofluid is presented in Fig. 12 along with the data of Dehkordi et al. [27] and Yiamsawas et al. [25] as a function of volume concentration. Among all the base fluids, viscosity enhancement is more for 60:40% EG/W nanofluid compared to other 20:80% and 40:60% nanofluids. The viscosity enhancement is more in the present data compared to the published data. Dehkordi et al. [27] used SDBS surfactant for the preparation of stable nanofluids in 60:40% EG/W nanofluid. Where as Yiamsawas et al. [25] used 20:80% EG/W nanofluid with higher particle volume concentrations. At 1.0% volume concentration of 20:80% EG/W nanofluid, the present data almost coincides with the data of Yiamsawas et al. [25]. Various base fluids, particle sizes and preparation methods are one of the reasons for obtaining the different viscosity ratios. The classical theoretical models such as Einstein [38], Brinkman [39] and Batchelor [40] are predicting the same viscosity for all the nanofluids and they failed to predict the viscosity of nanofluids with effect of temperatures.

Fig. 9. Viscosity of 20:80% EG/W based nanofluid at different particle concentrations.

Fig. 11. Viscosity of 60:40% EG/W based nanofluid at various particle concentrations.


Fig. 12. Viscosity ratio 20:80%, 40:60% and 60:40% EG/W nanofluids is compared with Dehkordi et al. [27] and Yiamsawas et al. [25].

Thermal conductivity correlations were developed based on 135 experimental data points by assuming that nanofluid thermal conductivity increases linearly with particle concentrations. ð13Þ

A ¼ 1:1236 and B ¼ 8:0175 ⇒20 : 80% EG=W nanofluid A ¼ 1:0806 and B ¼ 10:164 ⇒40 : 60% EG=W nanofluid A ¼ 1:0618 and B ¼ 10:448 ⇒60 : 40% EG=W nanofluid

ð14Þ

A ¼ 0:9396 and B ¼ 24:16 ⇒20 : 80% EG=W nanofluid A ¼ 0:9299 and B ¼ 67:43 ⇒40 : 60% EG=W nanofluid A ¼ 1:1216 and B ¼ 77:56 ⇒60 : 40% EG=W nanofluid

Cμ Ck

b4:. From Eqs. (15) to (16),

for 20:80% EG/W nanofluid at 20 °C with 1.5 vol.%, the viscosity enhancement and thermal conductivity enhancement coefficients are Cμ = 26.92 and Ck = 11.65 then the ratio, Cμ /Ck = 2.31. In a similar way, from Eqs. (15) to (16), for 40:60% EG/W nanofluid at 20 °C with 1.5 vol.%, the viscosity enhancement and thermal conductivity enhancement coefficients are Cμ = 134.92 and Ck = 9.73 then the ratio, Cμ /Ck = 13.86. From Eqs. (15) to (16), for 60:40% EG/W nanofluid at 20 °C with 1.5 vol.%, the viscosity enhancement and thermal conductivity enhancement coefficients are Cμ = 152.46 and Ck = 7.38 then the ratio, Cμ /Ck = 20.65. In the value of

Viscosity correlations were developed based on 135 data points by assuming that nanofluid viscosity increases exponentially with particle concentrations. μ nf Bϕ ¼Ae μ bf

Fig. 13. Comparison of measured thermal conductivity with proposed correlation.

beneficial if the ratio of coefficients is

5.4. Proposed correlations

knf ¼AþBϕ kbf

93

Cμ Ck

b4, that nanofluid is the beneficial

heat transfer fluid. From the calculations, 20:80% EG/W nanofluid is beneficial in fully developed laminar flow conditions and the other 40:60% and 60:40% EG/W based nanofluids are not beneficial, because of high viscosity enhancements. The figure of merit for these two nanofluids in laminar flow conditions would be doubtful. Detailed experimental analysis is to be required before concluding the potential benefit of these fluids. 5.6. Heat transfer benefits of nanofluid in turbulent flow

The experimental data of thermal conductivity and viscosity of all nanofluids were shown in Figs. 13 and 14 along with the values estimated from Eqs. (13) to (14).

In turbulent flow conditions (Re N 2300) where potentially water based nanofluids would be used, the Mouromtseff number (Mo) can be used to compare two fluids. In experimental values of thermal

5.5. Heat transfer benefits of nanofluid in laminar flow In the point of view of industrial application, low viscosity fluid and lower pumping power are more advantageous. Based on the relative coefficients of thermal conductivity and viscosity enhancements to predict the potential of nanofluids in an actual application, Prasher et al. [29] have developed an expression for fully developed laminar flow condition (Re b 2300). knf ¼ 1 þ Ck ϕ kbf μ nf μ bf

¼ 1 þ Cμ ϕ

ð15Þ

ð16Þ

where Ck and Cμ are the thermal conductivity and viscosity enhancement coefficients. As per Prasher et al.'s [29] analysis, the nanofluid is

Fig. 14. Comparison of measured viscosity with proposed correlation.

94


Fig. 15. Figure of merit (FOM) of Al2O3 nanofluid prepared in different 20:80%, 40:60% and 60:40% EG/W base fluids.

conductivity and viscosity, theoretical values of density and specific heat were used to evaluate the nanofluid thermal effectiveness, by means of the Mouromtseff number (Simons [30]). It is a figure of merit (FOM) to evaluate and compare the heat transfer capability of an alternative thermal fluid. Higher ‘Mo’ numbers indicate higher heat transfer capability of fluid, for a given geometry at a specified velocity.

Monf ¼

0:67 0:33 ρ0:8 C pnf nf knf 0:47 μ nf

ð17Þ

30.51% and for 60:40% EG/W nanofluid is 27.42% at a temperature of 60 °C respectively compared to base fluid. Thermal conductivity enhancement of nanofluid not only depends on the particle concentration and temperature but it also depends on the base fluid thermal conductivity. Nanofluid prepared in high thermal conductivity base fluid exhibits more enhancement compared to low thermal conductivity base fluid. The viscosity enhancement for 1.5% particle concentration of 20:80% EG/W nanofluid is 1.37-times, for 40:60% EG/W nanofluid is 2.75-times and for 60:40% EG/W nanofluid is 2.58-times at a temperature of 0 °C respectively compared to base fluid. Nanofluid prepared in higher viscosity base fluid exhibits more enhancement compared to low viscosity base fluid. The effectiveness of nanofluid in laminar and turbulent flow conditions was studied based on the Prasher et al. [29] model and the Mouromtseff number [30]. The heat transfer benefits of 20:80% EG/W nanofluids for all particle concentrations were effective in both laminar and turbulent flow conditions compared to 40:60% EG/W and 60:40% EG/W nanofluids for all the particle concentrations. The reason behind is the domination of viscosity for the case of 40:60% and 60:40% EG/W nanofluids. Before concluding the applicability of these nanofluids in heat transfer equipments detailed experimental study is to be required. Acknowledgments The authors would like to thank the Portuguese Foundation of Cinecia e Technologia, through a grant funded by the Ministry of Science and Technology (PTDC/EME-MFE/105031/2008). One of the authors (L.S.S.) would like to thank FCT for his post-doctoral research grant (SFRH/BPD/79104/2011). References

Mobf ¼

FOM ¼

0:67 0:33 ρ0:8 C pbf bf kbf 0:47 μ bf

Monf Mobf

ð18Þ

ð19Þ

where (k), (ρ), (Cp) and (μ) are thermal conductivity, density, specific heat and viscosity, respectively. The nanofluids, FOM N 1 provide larger heat transfer benefits at the same velocity for a particular system. Fig. 15 represents the figure of merits for 20:80%, 40:60% and 60:40% EG/W nanofluids at different volume concentrations and at a temperature of 30 °C. It is observed that nanofluid prepared with 20:80% EG/W for all volume concentrations is more than one, nanofluid prepared with 40:60% and 60:40% EG/W for all volume concentrations is less than one. This indicates that the nanofluids prepared with 40:60% and 60:40% EG/W are more viscous than viscosity 20:80% EG/W. Hence those nanofluids were not suitable for use as heat transfer fluids in turbulent flow conditions. However, the Mouromtseff number does not incorporate any additional heat transfer mechanisms that have been observed in nanofluid heat transfer studies and therefore experiments need to be conducted before conclusions can be drawn of the fluid potential. 6. Conclusions Experimental analysis was conducted for the estimation of thermal conductivity and viscosity of Al2O3 nanofluid with influence of particle concentrations, temperatures and base fluids. In order to study the properties with effect of base fluids, three base fluids such as 20:80%, 40:60% and 60:40% EG/W were considered. At maximum particle concentration of 1.5%, the enhancement in thermal conductivity for 20:80% EG/W nanofluid is 32.26%, for 40:60% EG/W nanofluid is

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