water based SiO2 nanofluids

ICHMT-03458; No of Pages 10 International Communications in Heat and Mass Transfer xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

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

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a r t i c l e

Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia Automotive Engineering Centre, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia d Tarbiat Modares University, Tehran, Iran b c

i n f o

a b s t r a c t

Nanofluids are a new class of engineered heat transfer fluids which exhibit superior thermophysical properties and have potential applications in numerous important fields. In this study, nanofluids have been prepared by dispersing SiO2 nanoparticles in different base fluids such as 20:80% and 30:70% by volume of BioGlycol (BG)/water (W) mixtures. Thermal conductivity and viscosity experiments have been conducted in temperatures between 30 °C and 80 °C and in volume concentrations between 0.5% and 2.0%. Results show 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. The maximum thermal conductivity enhancement among all the nanofluids was observed for 20:80% BG/W nanofluid about 7.2% in the volume concentration of 2.0% at a temperature of 70 °C. Correspondingly among all the nanofluids maximum viscosity enhancement was observed for 30:70% BG/W nanofluid about 1.38-times in the volume concentration of 2.0% at a temperature of 70 °C. The classical models and semi-empirical correlations failed to predict the thermal conductivity and viscosity of nanofluids with effect of volume concentration and temperatures. Therefore, nonlinear correlations have been proposed with 3% maximum deviation for the estimation of thermal conductivity and viscosity of nanofluids. © 2016 Published by Elsevier Ltd.

Available online xxxx Keywords: Nanofluids BioGlycol Silicon oxide Thermal conductivity Viscosity

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1. Introduction

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Nanofluids, colloidal dispersions of nanoparticles in liquid have great potential as coolants owing to their higher thermal conductivity. The motivation for nanofluids can be traced back to Maxwell's [1] prediction of improving thermal conductivity of liquids using solid particles. Certainly the heat-transfer processes have an effective role in most of the areas of industrial engineering, represented in the power generation, air conditioning, automotive, solar collector and chemical processors [2–8]. Over the past decades, there had been a dramatic increase in using water (W), ethylene glycol (EG) and propylene glycol (PG) based nanofluids due to their vast applications in the transfer of thermal energy [9–14]. Masuda et al. [15] have initiated studies on the effect of Al2O3, SiO2 and TiO2 nanoparticles dispersed in water on the thermal conductivity and viscosity of the nanofluid.

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M.Kh. Abdolbaqi a, Nor Azwadi Che Sidik b,⁎, Mohd Fadzil Abdul Rahim a, Rizalman Mamat a,c, W.H. Azmi a,c, Mohammad Noor Afiq Witri Muhammad Yazid b, G. Najafi d

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Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2 nanofluids☆

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☆ Communicated by W.J. Minkowycz. ⁎ Corresponding author. E-mail address: [email protected] (N.A.C. Sidik).

Tavman et al. [16] reported measurement of the effective thermal conductivity and viscosity for SiO2–water nanofluids of (0.4–1.85) volume concentration with 12 nm particle size at 20–50 °C. Measured results showed that the viscosity increased dramatically with an increase in concentration. In addition, the effective thermal conductivity of nanofluids increases as the concentration of the particles increases and also this increase is independent of the temperature. According to Choi [17], in the recent two decades, nanofluids have attracted wide attention as exhibited by the enormous increment in the publications on this subject. It is noteworthy that the majority of these studies adopted water-based nanofluids that were often tested within the surrounding temperatures, followed by many other studies until recently. Few investigative studies have been conducted on the role of ethylene glycol based SiO2 nanofluids in heat transfer enhancement [18,19]. Peñas et al. [18] stated experimental results of the thermal conductivity of two nanofluids of SiO2 in water and ethylene glycol at various concentrations up to around 5% in mass fraction. They compared their results with simple theoretical models that predicted the thermal conductivity of solid suspensions. They mentioned that in

http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001 0735-1933/© 2016 Published by Elsevier Ltd.

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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M.K. Abdolbaqi et al. / International Communications in Heat and Mass Transfer xxx (2016) xxx–xxx

T1:1

Nomenclature

T1:2 T1:3 T1:4 Q4 T1:5 T1:6 T1:7 T1:8 T1:9 T1:10 T1:11 T1:12 T1:13 T1:14 T1:15 T1:16 T1:17 T1:18 T1:19 T1:20 T1:21 T1:22

ATC Cμ BG EG PG D

T1:23 T1:24 T1:25 T1:26 T1:27 T1:28 T1:29 T1:30 T1:31

Greek symbols ϕ Volume concentration, % φ Volume fraction, φ = (ϕ/100) φa Volume fraction of aggregates φi Volume fraction of agglomerates, φi = (ra/r)D − 3 ρ Density, kg/m3 α Conductivity ratio, α = (σp/σbf) ω Weight concentration in percent

T1:32 T1:33 T1:34 T1:35 T1:36 T1:37

Subscripts bf Base fluid eff Effective nf Nanofluid p Particle

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2. Experimental setup

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2.1. Nanofluid preparation

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Silicon oxide SiO2 water based nanofluid contains SiO2 nanoparticles with 99% purity procured from US Research Nanomaterials, Inc. and is used in the experiments after appropriate dilution and mixing with BioGlycol/Water. The SiO2 nanoparticles have thermal conductivity of 1.4 W/m K, density of 2220 kg/m3 and average particle size is 22 nm [36]. It was supplied with an initial concentration of 25% by weight at a pH of 11 [3]. The nanofluid supplied in weight concentration ω is converted to volume concentration ϕ with Eq. (1). The volume of distilled water ΔV to be added for attaining a desired concentration ϕ2 is evaluated with Eq. (2) with the values of V1 and ϕ1 known a priory [3]

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some cases observed enhancements are several times larger than the predicted ones. Namburu et al. [19] studied the effect of 29, 50, and 100 nm average particle diameter to the effective viscosity of 60:40 ratio by weight ethylene glycol and water mixture based SiO2 nanofluid. The concentration and temperature varied from 0 to 10% volume concentration and −35 to 50 °C, respectively. The results illustrate that the viscosity of nanofluid decreases with the increase of the particle parameter. They also observed that the nanofluid exhibited as a non-Newtonian fluid at low temperature. In spite of its versatility, the use of PG as the base liquid has been rarely investigated [20–22]. Recently, Vajjha et al. [23] studied the viscosities of five nanoparticles (Al2O3, CuO, SiO2, TiO2, and ZnO) dispersed in a base fluid of 60:40 PG/W. The results illustrated that these nanofluids exhibit a non-Newtonian behavior within a lower temperature range of 243 K–273 K and a Newtonian behavior within the higher temperature range of 273 K–363 K. In a different study, Suganthi and Rajan [24] investigated experimentally the influence of ZnO dispersed in 20:80% of PG:W with volume concentrations of lower than 2% and temperature of 15 to 50 °C. The results showed that the thermal conductivity revealed better enhancements at lower temperatures. However, a number of studies have found that the dispersion and stability are the essential characteristics in the enhancement of the thermo-physical properties of nanofluids especially for thermal conductivity [25–28]. Uniform dispersion and stable suspension of nanoparticles in the liquids are the main key to successful applications of nanofluids. The final

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TEC HS Kbf kBG kr μbf μnf μr m r ra ka n

Temperature compensation Viscosity enhancement BioGlycol Ethylene glycol Propylene glycol Fractal index, which has an average value of 1.8 for nanofluids Thermo-electrical conductivity ratio Hashin–Shtrikman model Thermal conductivity of base fluid, W/m K Thermal conductivity of BioGlycol, W/m K Thermal conductivity ratio = knf/kbf Viscosity of base fluid, (mPa·s) Viscosity of nanofluid fluid, (mPa·s) Viscosity ratio = μnf/μbf Mass, g Radius of primary nanoparticles, nm Radius of aggregates nanoparticles, nm Thermal conductivity of agglomerates Empirical shape factor

properties of nanofluids were determined by the quality of the dispersed state of the suspension [4,29–31]. Many researchers have reported the necessity of proper dispersion of nanofluids and various dispersion techniques [32]. They also measured the thermal conductivity as a function of ultra-sonication (physical technique) time and showed that long hours of ultra-sonic dispersion were required to improve particle dispersion [33]. Recently Abdolbaqi et al. [34] studied the thermal conductivity of pure BioGlycol based Al2O3 nanofluid of 0.1, 0.3, 0.5, 0.7 and 1% volume concentration at a wide range of temperature (30 °C to 80 °C). The results showed that the thermal conductivity of the nanofluids was a nonlinear function of temperature while was concentration independent. However, BioGlycol (BG) showed more advantages compared to water, for instance a much lower freezing point and a much higher boiling point (−46 °C to 177 °C). Moreover, one of the BG attributes is that it has a lower thermal conductivity than water to about onethird. Additionally BioGlycol solution is produced domestically, renewable sourced fluid, non-toxic, and at low temperatures that provided 30% lower viscosity compared to propylene glycol which is petroleum-derived [35]. It also has greater thermal stability while possessing similar or better thermo-physical properties compared to propylene and ethylene glycols. It offers better performance than propylene glycol while giving its users an environmentally safer product than ethylene glycol [35]. Literature reviews have indicated that there is no academic report that has been published so far using BioGlycol/water based SiO2 nanofluids. Therefore, this study aims to investigate experimentally the thermal conductivity, viscosity and stability of 20:80% and 30:70% BioGlycol:water mixture based SiO2 nanofluid as well as to develop thermal conductivity and viscosity models based on the present study data of 0.5 to 2.0% volume concentration and the temperature range of 30 to 80 °C. The thermal conductivity and viscosity data obtained in the present work are compared with sixteen models and semiempirical correlations available in the literature.

ϕ¼


ωρw ω  ω ρ 1− ρ þ 100 p 100 w

ΔV ¼ ðV2 −V1 Þ ¼ V1

  ϕ1 −1 ϕ2

105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 Q5 136 137

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ð1Þ 152 153

ð2Þ 155

The characterization of SiO2 nanoparticles is obtained by the field emission scanning electron microscopy (FESEM) technique [37]. The image of FESEM at a magnification of ×35,000 illustrated that the SiO2 nanoparticles' average size is 22 nm and the shape is observed to be spherical as shown in Fig. 1. The experiments were conducted using 0.5, 1.0, 1.5 and 2.0% volume concentrations of SiO2 nanofluids with two mixture ratios of BG/W in 20:80% and 30:70% by volume. The

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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3.1. Thermal conductivity models

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A considerable number of models and correlations have been proposed in view of explaining the thermal conductivity behavior of suspensions containing small solid particles. Some of those theories are analyzed and compared with current experimental findings. Maxwell [1] was the first to propose the model to determine the effective electrical or thermal conductivity of suspensions containing solid particles. This model may be applied to statistically homogeneous and low-volume fraction liquid–solid suspensions with randomly dispersed, uniformly sized and non-interacting spherical particles:

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In order to prepare stable nanofluids and reduce the size of agglomerates, sonication was applied using an ultra-sonicator. Nanofluids were prepared in volumes of 100 mL for each volume concentration and exposed to the sonicator for 2 h. After that, the nanofluids were very stable throughout the measuring process. Evaluation of the stability of the studied nanofluids has been examined at room temperature with a UV–vis spectrophotometer. Many researchers have used a UV–vis spectrophotometer to evaluate the stability of the nanofluids [43–46]. Stability measurement of nanofluids with a UV–vis spectrophotometer was first proposed by Jiang et al. [47] as an extension of the sediment time method. The most important issues that need to be taken into account are peak scanning and standard preparation of nanofluid as reported by [46,47]. Therefore, this research came out with concentrations of two samples of BG/W (20:80% and 30:70%) based SiO2 nanofluid of 2.0 vol.% to evaluate the stability of nanofluids.

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2.3. Thermal conductivity measurement

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The thermal conductivity of the samples was measured using the KD2 Pro thermal property analyzer of Decagon Devices, Inc., USA. The data were collected for a temperature range of 30 to 80 °C after 2 h of the sonication process. Various investigators used the KD2 Pro thermal property analyzer in their measurements of thermal conductivity [14,48–52]. This instrument applied the transient hot-wire method. The present measurement method allowed the thermal conductivity measurement of nanofluids with minimum free convection effects. 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 ±1.3%.

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2.4. Viscosity measurement

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The viscosity of the nanofluids was measured using a Brookfield LVDV-III Ultra Rheometer. Several investigators used a Brookfield Rheometer in their measurement of viscosity [19,53,54]. The viscosity was measured in temperatures between 30 °C and 80 °C and the values were recorded at steady state conditions and 30 min was allowed to stabilize the temperature.

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procured SiO2 nanofluid was prepared to a new concentration by dilution techniques. The technique was applied successfully by the previous researchers in their heat transfer nanofluid evaluation [38–42].

where kp, is the thermal conductivity of the nanoparticles, and φ is the volume fraction of nanoparticles in the mixture. It is noted that the Maxwell equation is not a function of temperature. Since then several models were introduced in order to take an account of Brownian motion of nanoparticles [55,56], liquid layering around them [57], ballistic heat transport in nanoparticles and the particle's geometry [58–61]. However, it is largely accepted that the thermal conductivity enhancement can be best described by nanoparticle structuring [50] and the manner of particle packing in the matrix [62]. Thus, Hamilton and Crosser [63] modified Maxwell's model and showed the effect of particle shape and particle volume fraction on thermal conductivity of suspensions. On the other hand, the size effects of nanoparticles are not included in both models. Considering that nanoparticles in nanofluids are mostly in the form of aggregates. Chen et al. [64] used to modify the conventional form of the Hamilton–Crosser model. It introduced the concept of the effective volume fraction of aggregates φa and replaced the term kp with ka, which is the thermal conductivity of agglomerates as shown by Eq. (4):

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Fig. 1. FESEM image of dry SiO2 nanoparticle at ×35,000 magnification.

  knf ka þ 2kbf −2φa kbf −ka   ¼ kbf ka þ 2kbf þ φa kbf −ka

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ð3Þ

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  knf kp þ 2kbf −2φ kbf −kp   ¼ kbf kp þ 2kbf þ φ kbf −kp Q1

3

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ð4Þ

where φa = (ra/r)3 −D and ka is to be determined from the Bruggeman 230 model [65] in Eq. (5): (  1 ) ka 1 kp kp kp 2 ¼ ð3φi −1Þ þ ð3ð1−φi Þ−1Þ þ ð3φi −1Þ þ ð3ð1−φ1 Þ−1Þ2 þ 8 kbf 4 kbf kbf kbf

ð5Þ where φi is the solid volume fraction of agglomerates given by φi = (ra/r)D − 3. ra and r are the radius of aggregates and primary nanoparticles, respectively. The term D is the fractal index, which has an average value of 1.8 for nanofluids assuming diffusion limited aggregation. Moreover, some authors [66,67] consider mean-field boundary theory as the best suited model for estimating thermal conductivity enhancement. There are two main models in the mean field theory. One is the simple series and parallel model which is based on the configuration of nanoparticles relative to the direction of heat flux in a nanofluid [68]. According to the theory, the effective thermal conductivity is calculated assuming series and parallel configurations of nanoparticles in base fluid are as follows:

kseries ¼

ð1−φÞ φ þ kbf kp

ð6Þ

232 233 234 235 236 237 238 239 240 241 242

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1 kparallel

¼ ð1−φÞkbf þ φkp

ð7Þ

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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M.K. Abdolbaqi et al. / International Communications in Heat and Mass Transfer xxx (2016) xxx–xxx

More complex Hashin–Shtrikman model (HS) [69] was widely used to estimate upper and lower limits of effective thermal conductivity of nanofluids according to the formula in Eq. (8): kbf 1 þ

!   3φ kp −kbf   ≤ knf ≤ kp  3kbf þ ð1−φÞ kp −kbf

1−

 ! 3ð1−φÞ kp −kbf   3kp þ φ kp −kbf ð8Þ

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The early studies for the determination of viscosity of suspended sphere particles in liquids have been undertaken by Einstein [71]. The Einstein model Eq. (9) is the mostly referred equation to predict the viscosity of nanofluids and this model is applicable for drawback in that it predicts only very low nanoparticle concentrations (φ ≤ 0.02%), considering the hydrodynamics around an isolated sphere. Ever since this work was published, many researchers developed several equations to extend Einstein's theory to higher particle volume fractions. μ eff ¼ ½1 þ 2:5φ μ bf

ð9Þ

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Brinkman [72] considered the higher-order coefficients neglected by Einstein in an effort to validate his equation to higher particle volume fraction less than 4.0%.

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Batchelor [73] improved the correlation of Einstein by considered the effect of Brownian motion of particles for an isotropic suspension of rigid and spherical particles. μ eff

 ¼ 1 þ 2:5φ þ 6:5φ2

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Nguyen et al. [74]. developed an equation that is applicable to predict the viscosity of water based Al2O3 nanofluid of 0–12.0% volume concentration.

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4. Results and discussion

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The stability of the different nanofluids was tested with an Ultra Violet–visible spectrophotometer (UV–vis spectra). Each type of nanofluid sample was scanned in 5 min with 1.0 nm interval to measure the suspension concentration with increasing sediment time at room temperature. Fig. 2 represents the UV–vis spectra of SiO2 nanoparticles

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4.1. Stability of nanofluid

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In order to investigate the effectiveness of BG/W as a base fluid a comparison of the standard data of BioGlycol by Dynalene [35] with propylene glycol and ethylene glycol by ASHRAE [76] at the same mixture ratio of fluid/water (20:80% and 30:70%) with temperature range of (30–80) °C, BG/W provide 3.1% higher thermal conductivity than PG/W [76] while EG/W [76] demonstrate 0.7% higher thermal conductivity than BG/W as illustrate in Fig. 3. At the stage of instrument calibration, the measured thermal conductivity data obtained for nanofluids were compared with available literature data of BioGlycol by Dynalene [35].

ð12Þ

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Thomas and Muthukumar [75] derived the effective viscosity of a dilute suspension of hard spheres from fully hydrodynamic interactions involving three spheres. μ eff

 ¼ 1 þ 2:5φ þ 4:8292φ2 þ 6:4028φ3 þ … μ bf

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 μ nf

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with their respective concentrations for two base ratio of BG/W. The absorbance of nanofluids demonstrates characteristic absorption bands of SiO2 nanoparticles between 200 and 400 nm wavelengths. The peak absorbance of SiO2 nanoparticles occurs at the peak wavelength of 294 to 299 nm. Besides, the peak absorbance of nanofluids diminishes from higher concentration to lower concentration for both base ratios. It can be concluded that lower concentration of nanofluids has more potential for agglomeration and faster sedimentation time. In addition, the pattern of absorbance shows evidence that there are more suspended nanoparticles at higher volume concentrations.

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Fig. 2. UV–vis spectrophotometer evaluation of SiO2 nanofluid.

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Physically, the upper HS bound corresponds to a nanocluster matrix with spherical inclusions of fluid regions while the lower HS bound assumes well dispersed nanoparticles in the base fluid [70]. HS lower bound, which is the left hand side of the inequality, is identical to Maxwell's equation.

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Fig. 3. Comparison of thermal conductivity among BioGlycol, propylene glycol and ethylene glycol.

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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309 Q10 In the measured temperature range, the obtained thermal conductivity 310 of all fluids was found ± 0.7% deviation with reference values. The 311 312 313 314 315

enhancement of thermal conductivity increased with an increase of nanoparticle volume concentration and temperature, as well as the enhancement of BG/W in 20:80% mixture ratio was higher than 30:70%. Furthermore Eq. (14) used to estimate the enhancement of thermal conductivity based on the measured thermal conductivity data.   Enhancement ¼ knf −kbf 100=kbf

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Fig. 5. Thermal conductivity ratio of BG/W SiO2 nanofluid.

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Effect of temperature on thermal conductivity can be explained in Fig. 4(a,b) which presents thermal conductivity against measured temperature. The thermal conductivity of base liquid and nanofluids is increasing with temperature. Hence, it can be deduced that the thermal conductivity of nanofluids is directly proportional with temperature and concentrations of nanoparticles. As seen from Fig. 5(a,b), the thermal conductivity enhancement increases with an increase of temperature and nanoparticle concentration for SiO2 nanofluids. The maximum thermal conductivity enhancement of 20:80% BG/W based 2.0% SiO2 nanofluid is 7.2% at a temperature of 70 °C as illustrated in Fig. 5(a). Whereas the maximum thermal conductivity enhancement

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Fig. 4. Thermal conductivity of BG/W SiO2 nanofluid with temperature variation.

of using 30:70% is 6.9% at the same conditions as shown in Fig. 5(b). 328 This could be attributed to the layering of the base fluid molecules on 329 the surface of the nanoparticles [24]. 330 4.2.1. Proposed model of thermal conductivity The analysis of variance is a requisite to check the significance of the model [77]. Moreover, this analysis gives clearly how the parameters affect the response and the level of significance of the factors [78]. Hence, the analysis of variance was implemented to produce nonlinear correlation as a function of volume concentration and temperature. Thus, it gives an accurate description of the thermal conductivity behavior for BG/W nanofluid. The current study of thermal conductivity measurement for SiO2 nanoparticles dispersed in 20:80% and 30:70% of BG/W based fluid. The present experimental data have been compared among several established theoretical and semi-empirical equations. The thermal conductivity correlations are presented in Table 1. The equations were developed based on an experimental measurement data of nanofluids in different base fluids [48,77–79]. Fig. 6(a) and (b) shows comparison between the equations in Table 1 and presents thermal conductivity data for SiO2 nanofluids in BG/W at a temperature of 30 °C. However, the equations failed to agree with the present BioGlycol data. Therefore,

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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t1:3

References

Correlations

t1:4 Q2

Maïga et al. [80]

K nf K bf

¼ 4:97φ þ 2:72φ þ 1

t1:5

Buongiorno [81]

K nf K bf

¼ 1 þ 2:92φ−11:99φ2

t1:6

Mintsa et al. [52]

K nf K bf

t1:7

Jeffrey [82]

knf kbf

¼ 1 þ 1:72φ



  k =k −1 k =k −1 2 k =k −1 2 ¼ 1 þ 3 kpp =kbf þ2 φ þ 3 kpp =kbf þ2 þ 34 kpp =kbf þ2 φ2

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2

bf

bf

bf

new thermal conductivity of BioGlycol based nanofluid correlation was developed as a function of volume concentration and temperature. The equation was conceived in the form of Eq. (15).

kr ¼

   0:008 knf ϕ 0:03 T ¼ 1:199   100 80 kbf

ð15Þ

obtained with an average deviation of 0.5%, standard deviation of 0.3% and maximum deviation of 2%. In particular, good correlation between experimental results and theoretical prediction according to aggregation models was observed at fairly high particle concentration. Moreover, the Maxwell and series models appear to be the most ineffective estimation tool for both nanofluids. In fact, it is hard to control the nanoparticle structure and configuration with the existing knowledge and technology. This restricts the series–parallel model from becoming a viable technique. Fig. 7 shows the result validation for the BioGlycol thermal conductivity model in Eq. (15). The figure provides the similarities between predicted and experimental results of thermal conductivity of SiO2 nanofluid in BG/W for a wide range of volume concentrations of 0.5 to 2.0% and temperature of 30 to 80 °C. The result shows that the model is able to estimate the thermal conductivity of BioGlycol nanofluids for different concentrations and temperatures within ±2% deviation. The present observations show the capability of the aggregation mechanism to accurately predict the thermal conductivity of well-dispersed nanofluids even at fairly high volume concentrations.

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4.3. Viscosity enhancement

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In order to study the effectiveness of BG/W as a base fluid a comparison at same mixture ratio fluid/water of (20:80%, 30:70%) and with temperature range of (30–80) °C, BG/W [35] demonstrate 57.3% and 57.5% lower viscosity than PG/W [76] respectively and 54.3% and 50.8% lower viscosity than EG/W [76] respectively as shown in Fig. 8. Interestingly, viscosity instrument was calibrated by introducing the known viscosity of fluids such as 20:80% and 30:70% BG/W. The experimental values of all fluids were found ±1.1% deviation in comparison with the values obtained from Dynalene [35] at a measured temperature range of (30–80) °C. Likewise, the viscosity results of 20:80% and 30:70% BG/W nanofluid respectively reveal that viscosity of nanofluids decreases with increase of temperatures, while increases with increase of particle volume concentrations compared to base fluid as shown in Fig. 9(a) and (b). Additionally, viscosity enhancement of 20:80% BG/W based 2.0% volume concentration is 16.02% and 28.9% in temperatures of 30 °C and 70 °C. As well as the viscosity of 30:70% BG/W nanofluid compared to base fluid respectively as illustrated in Fig. 10(a) and (b) the viscosity enhancement for 2.0% volume concentration is 17.3% and 37.8% in temperatures of 30 °C and 60 °C compared to base fluid respectively. It is noteworthy that at the same volume concentration (2.0%) of all nanofluids in low temperature (30 °C), the viscosity enhancement is lower compared to high temperature (80 °C).

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Table 1 Thermal conductivity models for nanofluids proposed by researchers.

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The thermal conductivity data of SiO2 nanofluid at different concentrations and temperatures is subjected to regression. Eq. (15) was

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Fig. 6. Comparison between experimental data of thermal conductivity and semiempirical correlations.

Fig. 7. Result validation of non-linear thermal conductivity proposed model in Eq. (15).

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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Fig. 8. Comparison of viscosity among BioGlycol, propylene glycol and ethylene glycol.

Fig. 10. Viscosity ratio of BG/W SiO2 nanofluid.

4.3.1. Predict new correlation of viscosity Similarly, of proposed thermal conductivity correlation the analysis of variance was implemented to produce nonlinear correlation as a function of volume concentration and temperature. Accordingly, it obtains an accurate description of the viscosity behavior for BG/W nanofluid. In the present study, the equations failed to agree with the present BioGlycol data as shown in Fig. 11(a) and (b). Therefore, new viscosity of BioGlycol based nanofluid correlation was developed as a function of volume concentration and temperature. An exponential form was used to derive the viscosity values of nanofluids as a function of temperature and volume concentration. Viscosity correlation Eq. (16) was developed based on 60 data points by assuming that nanofluid viscosity increases and decreases exponentially with particle concentrations and temperature respectively μ nf μ bf

   T ¼ 0:906 exp 10:975φ þ 0:169 80

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ð16Þ 412

Fig. 9. Viscosity of BG:W SiO2 nanofluid with temperature variation.

A maximum deviation of about 3%, standard deviation about 1.2% and average deviation about 1.4% were observed between the experi- 413 mental and proposed correlation values for all the nanofluids examined 414 as shown in Fig. 12 415

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4.4. Effect of property enhancement ratio

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The condition for maximum heat transfer was analyzed with the aid of viscosity and thermal conductivity. The property enhancement ratio (PER) of viscosity to thermal conductivity is given by Eq. (17) According to the analysis of Garg et al. [79] when (PER) is greater than 5.0, the nanofluid does not aid heat transfer enhancement.

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 ðμ r −1Þ K bf μ nf −μ bf   ¼ PER ¼ ðK r −1Þ μ bf K nf −K bf

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ð17Þ 423

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The effect of property enhancement ratio was observed to be strongly dependent on the changes in the values of viscosity and thermal conductivity. Therefore, the property enhancement ratio illustrated in Fig. 13(a) and (b) predicted the experimental condition of heat enhancement satisfactorily for BG/W 20:80% and 30:70% based SiO2 nanofluid. The PER is 5.3 for SiO2 20:80% BG/W at 0.5% concentration with 50 °C and the remaining points are in agreement with the condition for maximum experimental heat transfer coefficient. While the PER for SiO2 30:70% BG/W shows decreases in enhancement ratio for different temperatures and volume concentrations. This due to greater values of viscosity for BG/W 30:70% based SiO2 nanofluids as compared

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Fig. 11. Comparison between experimental data of viscosity and semi-empirical correlations.

Fig. 12. Result validation of non-linear viscosity proposed model in Eq. (16).

Fig. 13. Effect of properties on heat transfer enhancement.

Please cite this article as: M.K. Abdolbaqi, et al., Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2..., Int. Commun. Heat Mass Transf. (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.001

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5. Conclusions

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Experimental analysis was conducted for the estimation of thermal conductivity and viscosity of SiO2 nanofluid with the influence of particle concentrations, temperatures and base fluids. In order to study the properties with effect of base fluids, two base fluids such as 20:80% and 30:70% BG/W were considered. Based on the obtained results, key findings of this investigation can be summarized as follows:

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• New non-linear models have been developed for the thermal conductivity and viscosity of nanofluids as a function of temperature and volume concentration with 5% maximum deviation. 450 • Nanofluid prepared in high thermal conductivity base fluid exhibits 451 more enhancement compared to low thermal conductivity base fluid. 452 • The highest thermal conductivity enhancement of 7.2% was observed 453 at 2% volume concentration for BG/W in 20:80% mixture ratio and 454 temperature of 70 °C. The maximum thermal conductivity enhance455 ment up to 6.9% was found for 30:70% at the same conditions. 456 • The viscosity enhancement of 20:80% BG/W based 2.0% volume 457 concentration is 20.5% and 33.8% in temperatures of 30 °C and 80 °C. 458 As well as the viscosity enhancement of 30:70% BG/W for 2.0% volume 459 concentration is 29.8% and 53.4% in temperatures of 30 °C and 60 °C 460 compared to base fluid respectively. 461 • However, BG/W 20:80% based SiO2 nanofluids for all concentrations 462 show lower values of PER as compared to the values of BG/W 463 Q16 30:70% based SiO2 nanofluids. 464 447

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Acknowledgements

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The financial support by Universiti Malaysia Pahang (UMP) under RDU1403110 and also Automotive Excellence Center (AEC) under RDU1403153 is gratefully acknowledged.

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References

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to the values of BG/W 20:80% based SiO2 nanofluids as well as due to the lower values of thermal conductivity for BG/W 30:70% based SiO2 nanofluids as compared to the values of BG/W 20:80% based SiO2. However, BG/W 20:80% based SiO2 nanofluids for all concentrations show lower values of PER comparing with the values of BG/W 30:70% based SiO2 nanofluids.

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