Investigation of thermal conductivity and viscosity of

international journal of refrigeration 70 (2016) 93–102

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Investigation of thermal conductivity and viscosity of Al2O3/PAG nanolubricant for application in automotive air conditioning system M.Z. Sharif a, W.H. Azmi a,b,*, A.A.M. Redhwan a,c, R. Mamat a,b a

Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia Automotive Engineering Centre, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia c Faculty of Manufacturing Engineering Technology, TATI University College, 24000 Kemaman, Terengganu, Malaysia b

A R T I C L E

I N F O

A B S T R A C T

Article history:

In this paper, thermal conductivity and viscosity of the Al2O3/polyalkylene glycol (PAG) 46

Received 25 March 2016

nanolubricants for 0.05 to 1.0% volume concentrations at temperatures of 303.15 to 353.15 K

Received in revised form 11 June

have been investigated. Al2O3 nanoparticles were dispersed in the PAG lubricant by a two

2016

step preparation. The measurement of thermal conductivity and viscosity was performed

Accepted 20 June 2016

using KD2 Pro Thermal Properties Analyzer and LVDV-III Rheometer, respectively. The results

Available online 21 June 2016

showed that the thermal conductivity of the nanolubricants increased by concentration, but decreased by temperature. Besides, the viscosity of the nanolubricants sharply in-

Keywords:

creased for concentrations higher than 0.3%. However, this parameter diminished by

Nanolubricants

temperature. The highest thermal conductivity and viscosity ratio were observed to be 1.04

Thermal conductivity

and 7.58 times greater than the PAG lubricant for 1.0% and 0.4% concentrations, respec-

Viscosity

tively. As a conclusion, it was recommended to use the Al2O3/PAG nanolubricants with

Air conditioning system

concentration of less than 0.3% for application in automotive air conditioning system. © 2016 Elsevier Ltd and IIR. All rights reserved.

Étude de la conductivité thermique et de la viscosité de nanolubrifiant Al2O3/PAG appliqué au système de conditionnement d’air d’automobile Mots clés : Nanolubrifiants ; Conductivité thermique ; Viscosité ; Système de conditionnement d’air

* Corresponding author. Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia. Tel.: +6 09 4246338; Fax: +6 09 4242202. E-mail address: [email protected] (W.H. Azmi). http://dx.doi.org/10.1016/j.ijrefrig.2016.06.025 0140-7007/© 2016 Elsevier Ltd and IIR. All rights reserved.

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Nomenclature

English symbols AD average deviation COP coefficient of performance cSt centistokes FESEMfield emission scanning electron microscopy h nanolayer thickness k thermal conductivity [W(m·K)−1] kr thermal conductivity ratio [kNL/kL] m mass n number of layer PAG polyalkylene glycol POE polyolester r original radius of nanoparticle SD standard deviation T temperature [°C] TEM transmission electron microscopy Greek symbols β ratio nanolayer thickness to the original radius [h/r] ϕ volume concentration [%] ϕ volume concentration in fraction μ dynamic viscosity [mPa·s] viscosity ratio [μNL/μL] μr ρ density [kg·m−3] Subscripts bf based fluid eff effective eq equation exp experiment L lubricant NL nanolubricant p nanoparticle r ratio

1.

Introduction

Masuda et al. (1993) measured the thermal conductivity of TiO2– water and Al2O3–water nanofluids and discovered thermal conductivity was increased by 11% and 32%, respectively. This research served as a foundation for further thermal conductivity studies. Choi (1995) from the U.S. Argonne National Laboratory introduced the term nanofluids (the mixture of solid nanoparticles with a base fluid) as a promising heat transfer fluid. This kind of heat transfer fluid has higher thermal conductivity in contrast to based conventional heat transfer fluids. Nanofluids also possess superb heat transfer properties such as high thermal conductivity, good stability, and homogeneity along with the minimal clogging in flow passages due to very small size and tremendous specific surface area of the nanoparticles (Chandrasekar et al., 2010). Refrigerants are extensively used in commercial, industrial and automotive refrigeration and air conditioning systems.

The idea of nanorefrigerants has been proposed based on the idea of nanofluids, which were prepared by mixing the nanoparticles into the conventional refrigerant. So far, at least three main advantages of using nanoparticles in refrigerant are obtained (Bi et al., 2011): (1) Nanoparticle as additives can increase the solubility between the refrigerant and the lubricant; (2) Thermal conductivity and heat transfer characteristics of the refrigerant can be increased; and (3) Nanoparticle dispersion into lubricant might reduce the friction coefficient and wear rate. Nanorefrigerants have the prospective to boost heat transfer rate thus more compact of heat exchanger in air conditioning and refrigeration systems are achievable. Studies on nanorefrigerants (Mahbubul et al., 2013a, 2013b; Park and Jung, 2007; Peng et al., 2009; Sun and Yang, 2013) have shown that adding nanoparticles to refrigerants can improve the heat transfer of the base refrigerant. The effect of carbon nanotubes (CNT)/R134a on nucleate boiling heat transfer has been studied by Park and Jung (2007). They found that large enhancement up to 36.6% was observed. Peng et al. (2009) explored the heat transfer features of refrigerant-based nanofluid flow boiling inside a horizontal smooth tube and found the maximum heat transfer coefficient increased to 29.7%. Then, they projected a heat transfer correlation and found that the difference between the predicted and experimental data is 20%. Sun and Yang (2013) did the research on nanorefrigerant material type and vapour quality in horizontal pipe for pure copper (Cu), copper oxide (CuO), pure aluminium (Al) and aluminium oxide (Al2O3). According to their research, the heat transfer coefficient of CuR141b was about 1.26 times higher than pure R141b and the highest among nanoparticles studied. Mahbubul et al. (2013a, 2013b) investigated heat transfer of Al2O3-R141b and Al2O3R134a nanorefrigerant in horizontal smooth circular tube. They concluded that with the increment of nanorefrigerant volume concentration, the heat transfer characteristics increased significantly. Apart from nanorefrigerants, the studies on mixture of nanolubricants and nanorefrigerants (Bartelt et al., 2008; Bobbo et al., 2010; Henderson et al., 2010; Kedzierski, 2009, 2012; Kedzierski and Gong, 2009; Peng et al., 2010a, 2010b; Wang et al., 2003) have also shown better performance compared to their based fluid. Wang et al. (2003) studied the refrigeration system using HFC134a and mineral lubricant appended with TiO2 nanoparticle as working fluids. They found that it performs better by returning more lubricant back to the compressor compared by using HFC134a and POE oil. Bartelt et al. (2008) examined the heat transfer effect on horizontal flow boiling condition of R-134a/POE/CuO. They concluded that a maximum enhancement of 101% was obtained for 2% mass fraction. Kedzierski and Gong (2009) studied the effect of CuO-R134a pool boiling heat transfer and found that the nanoparticles improved the heat transfer by 50 to 275% compared with the heat transfer of pure R134a/polyolester (99.5/0.5). The effect of CuO nanoparticles dispersed in R134a-lubricant pool boiling heat transfer has been investigated by Kedzierski (2009). He found that 2.0% volume concentration of CuO nanoparticle dispersed in R134a/nanolubricant mixtures had smaller boiling heat flux than the mixtures with 1% volume concentration. Henderson et al. (2010) studied the effect of nanoparticles on the heat transfer of R-134a and R-134a/POE mixtures. They


concluded that the average heat transfer enhancement was up to 76%. The influence of nanoparticles dispersion in POE oils on lubricity and R134a solubility has been studied by Bobbo et al. (2010). Solubility and tribology of TiO2/SW32 oil mixture showed the best performance among mixtures that have been studied. Peng et al. (2010b) studied nucleate pool boiling heat transfer characteristics of refrigerant/oil dispersed with diamond nanoparticles. They found that the nucleate pool boiling heat transfer coefficient increased up to 63.4%. The effect of carbon nanotubes (CNTs) on nucleate pool boiling heat transfer features of refrigerant oil mixture have also been studied by Peng et al. (2010a). They found that nucleate pool boiling heat transfer coefficient increased up to 61%. Kedzierski (2012) done the research on the influence of Al2O3 nanoparticles and R134a/ polyolester mixtures on the pool boiling performance on a rectangular finned surface. He found that the boiling performance enhanced up to 113% on a rectangular finned surface. Considering the potential of nanolubricants for improving the efficiency of air conditioning and refrigeration systems, thermal conductivity and viscosity measurements of potential nanolubricants gain advantages not only on fundamental research, but also on the design consideration. But to the best of the authors’ knowledge, there are only few literatures (Jiang et al., 2009a, 2009b; Kedzierski, 2013; Mahbubul et al., 2013c) that are available for the experimental works on thermal conductivity of nanorefrigerant or nanolubricant. Jiang et al. (2009a) studied thermal conductivity of CNT/R113 and found that the thermal conductivity was increased between 50 and 104%. Jiang et al. (2009b) also investigated the thermal conductivity of R113 with Cu, Al, Ni, CuO and Al2O3 with controlled volume concentrations of 0.1 to 1.2%. They found that the thermal conductivity of nanorefrigerant increased tremendously with the increment of volume fraction. They also concluded that thermal conductivities of nanorefrigerants with various types of nanoparticles are quite similar to one another if the nanoparticle volume fractions are similar. In heat transfer applications, apart from thermal conductivity, viscosity also plays a vital parameter. Pumping power and pressure drop are directly associated to viscosity of any fluid, especially in laminar flow (Mahbubul et al., 2012). Thermal conductivity and viscosity of the Al2O3/R141b nanorefrigerants for 0.5 to 2% volume concentrations at temperatures of 278.15 to 293.15 K were studied by Mahbubul et al. (2013c). They found that the thermal conductivity was up to 1.63 times greater than the base R141b. Meanwhile, Kedzierski (2013) investigated the viscosity and density of Al 2 O 3 nanolubricants. He came up with the viscosity model, which was able to predict the kinematic viscosity of the nanolubricant within ±15% of the measurement was developed. Most of the researchers are using classical models for thermal conductivity estimation such as Hamilton and Crosser (1962), Maxwell (1904), Timofeeva et al. (2007) and Yu and Choi (2003). Meanwhile, the model for viscosity are based on the models of Brinkman (1952), Pak and Cho (1998) and Wang et al. (1999). Both the thermal conductivity and viscosity models are widely used for nanofluids properties estimated at low volume concentration. As different base fluids have different thermophysical properties, the model implemented may not suit for lubricants purposes. Therefore if experimental data of the thermo-physical properties of the nanolubricants are obtained,

95

it would be used for better understanding on the enhancement of heat transfer, coefficient of performance (COP), energy saving, lubricity and others. The application of nanolubricants in the refrigeration and air condition systems done by Lee et al. (2009), Sabareesh et al. (2012) and Xing et al. (2014) are very much relevant to this study and should be used as references accordingly. Sabareesh et al. (2012) used R12 refrigerant as working fluid in experimenting the effect of dispersing low concentration of TiO2 nanoparticles in the mineral oil based lubricant, on its viscosity and lubrication characteristics, as well as on the overall performance of a vapour compression refrigeration system. Lee et al. (2009) mixed fullerene nanoparticles of 0.1% volume concentration in mineral oil and evaluate the friction coefficient by a diskon-disk tribotester. They found that, the friction coefficient of the nano-oil decreased by 90% in comparison with raw oil. While Xing et al. (2014) found that the friction coefficients of the Fullerene C60 nano-oil significantly decreased with increasing the concentration of nanoparticles in the mineral oil. Hence, the objective of the present work is to study the thermal conductivity and viscosity of Al2O3 nanoparticles suspended in polyalkylene glycol (PAG) 46 synthetic oil for 0.05 to 1.0% volume concentrations and working temperature of 303.15 to 353.15 K. Further, the optimum volume concentration of nanolubricants needs to be identified thoroughly by considering the thermal conductivity and viscosity of nanolubricants for application in automotive air conditioning system. Simultaneously, the regression equation for each properties were developed using the measured data.

2.

Methodology

The experimental procedures are thoroughly discussed in this section. This segment is divided into the characterization of the Al2O3 nanoparticle and the PAG lubricant. Subsequently, steps for preparation and stability observation of nanolubricants. Finally, steps to measure the thermal conductivity and viscosity of the nanolubricants are further discussed in detail.

2.1.

Materials and preparation of the nanolubricants

Al2O3 nanoparticles are used and procured with 99.8% purity and 13 nm in size. The characterization of Al2O3 nanoparticle is obtained using field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) imaging technique. Fig. 1(a) shows the image of FESEM in ×300,000 magnification. The Al2O3 nanoparticles shape is observed spherically. From the FESEM image, it has been observed that the nanoparticles are spherical in shape and the sizes are approximately 13 nm. TEM analysis was further undertaken to determine the condition of nanolubricant agglomeration, dispersion and also to confirm the particle size under suspended form. Fig. 1(b) shows the TEM image of Al2O3 nanoparticle suspended in PAG lubricant in ×88,000 magnification. From the TEM images, the nanoparticle is dispersed well in the lubricant. However, small agglomeration and minimum clustering of nanoparticle is observed in the solution as displayed in Fig. 1(b). The number-based size of the

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Fig. 1 – FESEM and TEM images of Al2O3 nanoparticle.

nanoparticles from TEM image is used to determine the average size of the nanoparticle. As a result, it is confirmed that Al2O3 nanoparticle size is approximately 13 nm and nearly spherical in shape. The properties of Al2O3 are shown in Table 1 (Aldrich, 2013; Zakaria et al., 2015). The present nanolubricant was intended to be tested in automotive air conditioning system that uses compressor and being lubricated by polyalkylene gycol (PAG). PAG lubricants have better tribology performance over mineral oils when used together with HFCs according to Matlock et al. (1999). At high pressures and temperatures, PAG has low solubility in the gaseous refrigerant and provide excellent lubricity. PAG have been used mainly in automotive air-conditioning systems due to the compatibility characteristic with most of elastomers

Table 1 – Properties of nanoparticles used in this experiment (Aldrich, 2013; Zakaria et al., 2015). Property Molecular mass, g·mol−1 Average particle diameter, nm Density, kg·m−3 Thermal conductivity, W(m·K)−1 Specific heat, J(kg·K)−1

Al2O3 101.96 13 4000 36 773

(Matlock et al., 1999). Table 2 shows the properties of the PAG 46 lubricant at atmospheric pressure (Brown, 1993; Dow, 2013). Two step method suggested by Yu and Xie (2012) is used in preparation of nanolubricants. Eastman et al. (1997), Lee et al. (1999) and Wang et al. (1999) used the same method in their preparation of Al2O3 nanofluids. Eq. (1) used to calculate the volume concentration of the nanolubricants.

φ=

mp ρp × 100 m p ρ p + mL ρL

(1)

where, ϕ is the volume concentration in percent, mp and mL are the masses of the nanoparticle and lubricant, respectively; and ρp and ρL are the density of the nanoparticle and density of the lubricant, respectively. The initial mixing process

Table 2 – Properties of PAG 46 lubricant (Brown, 1993; Dow, 2013). Property

PAG 46 −3

Density, g·cm @ 293.15 K Flash point, K Kinematic viscosity, cSt @ 313.15 K Pour point, K

0.9954 447.15 41.4–50.6 222.15


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Fig. 2 – Nanolubricant samples after a month of preparation.

is done by using a magnetic stirrer. The required mass of the Al2O3 nanoparticles to be dispersed in lubricant was precisely measured utilizing a high accuracy electronic balance. The mixture then is subjected to ultrasonic homogenization for an hour to ensure good dispersions of nanolubricants. Dispersion stability is observed visually after a month of preparation and found that no sedimentations were occurred in the samples as shown in Fig. 2. It should be noted that no surfactant has been used in this preparation.

Table 3 – Thermal conductivity model for nanofluids. Model

Thermal conductivity

Maxwell (1904)

Hamilton and Crosser (1962) Timofeeva et al. (2007)

2.2.

Thermal conductivity measurements

Thermal conductivity is measured using KD2 Pro thermal property analyzer as shown in Fig. 3. Azmi et al. (2016a), Lee et al. (2011), Mahbubul et al. (2013c) and Zakaria et al. (2015) are some of the researchers who used KD2 Pro in their thermal conductivity measurement. This apparatus uses the transient line heat source to determine the thermal properties of liquids and solids. The apparatus meets the standards of both ASTM D5334 and IEEE 442 – 1981. A single needle sensor (KS-1) in the range of 0.002 to 2.00 W(m·K)−1 is used. A water bath of WNB7L1 model is utilised to maintain a constant temperature of the sample with accuracy of 0.1 K (Zakaria et al., 2015). The thermal conductivity of 0.05 to 1.0% volume concentrations of Al2O3/PAG nanolubricants were measured for temperature range of 303.15 to 353.15 K. The sensor was validated by measuring the thermal conductivity of the verification liquid (glycerin) given by the supplier. The measured value of glycerin at 298.15 K is 0.286 W(m·K)−1, which is in agreement with the calibrated data

Yu and Choi (2003)

kr =

keff ⎡ kp + 2kbf + 2 (kp − kbf ) ϕ ⎤ =⎢ ⎥ kbf ⎣ kp + 2kbf − (kp − kbf ) ϕ ⎦

kr =

keff ⎡ kp + (n − 1) kbf − (n − 1) (kbf − kp ) ϕ ⎤ =⎢ ⎥ kbf ⎣ kp + (n − 1) kbf + (kbf − kp ) ϕ ⎦

kr =

keff = (1 + 3ϕ ) kbf

kr =

keff ⎡ kp + 2kbf + 2 (kp − kbf ) (1 + β )3 ϕ ⎤ =⎢ ⎥ kbf ⎣ kp + 2kbf − (kp − kbf ) (1 + β )3 ϕ ⎦

of 0.285 W(m·K)−1 and within ±0.35% accuracy. The validation process was done each time before the thermal conductivity measurement was taken. In order to ensure the consistency of data measurement, minimum five data were taken for every concentration at a specific temperature. The thermal conductivity models (Hamilton and Crosser, 1962; Maxwell, 1904; Timofeeva et al., 2007; Yu and Choi, 2003) are shown in Table 3 and used to verify the results of nanolubricants thermal conductivity.

2.3.

Viscosity measurements

Fig. 4 shows LVDV-III (low viscosity digital viscometer) Ultra Programmable Rheometer. The viscometer is able to measure the viscosity of liquid sample within the range of 1.0 to

Fig. 3 – KD2 Pro thermal properties analyzer.

Fig. 4 – LVDV III Ultra Programmable Rheometer.

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Table 4 – Viscosity model for nanofluids. Model

Viscosity

Brinkman (1952)

Wang et al. (1999)

Pak and Cho (1998)

μr =

μeff 1 = μbf (1 − ϕ )2.5

μr =

μeff = 123ϕ 2 + 7.3ϕ + 1 μbf

μr =

μeff = 1 + 39.11ϕ + 533.9ϕ 2 μbf

6,000,000 mPa·s with accuracy of ±1.0% and temperature accuracy is 0.1 K ranging from 253.15 to 373.15 K by utilizing the UL Adapter. The viscosity of 0.05 to 0.40% volume concentrations of Al2O3/PAG were measured for a temperature range of 303.15 to 353.15 K. A spindle was used to measure the viscosity of suspensions. The viscometer drives a spindle immersed in nanolubricants. By means of rotation of the spindle, a viscous drag of the fluid in opposition to the spindle is created, which is measured by the deflection of the calibrated spring. Each measurement was conducted three times to get more reliable data. The mean value of the three data was considered for analysis. The dynamic viscosity models (Brinkman, 1952; Pak and Cho, 1998; Wang et al., 1999) are listed in Table 4 and are used to compare the measured values of viscosity for the different volume concentrations. For both thermo-physical measurements, the confidence level is 99.9% with 0.1% uncertainties.

3.

Results and discussion

3.1.

Thermal conductivity of the nanolubricants

Fig. 5 shows the thermal conductivity of the Al 2 O 3 /PAG nanolubricants at 303.15 K for 0.05 to 1.0% volume concentrations. The experimental results of the present study were

Fig. 5 – Variation of thermal conductivity ratio as the function of volume concentration at 303.15 K.

compared with the estimated values obtained from previously published models in literature. The figure shows that the thermal conductivity of the Al 2 O 3 /PAG nanolubricant increases with volume concentration. The experimental values for this study were found to be slightly higher than the three models of Hamilton and Crosser (1962), Maxwell (1904), and Timofeeva et al. (2007). However, the model by Yu and Choi (2003) seem to agree with the experimental value to some extent. The mean and maximum deviation of the experimental values compared to Yu and Choi (2003) is 0.05% and 0.12%, respectively. Mahbubul et al. (2013c) have compared the experiment value of thermal conductivity of Al 2 O 3 -R141b nanorefrigerant with Maxwell (1904). The results showed that their experimental value also is much higher compared to Maxwell (1904) by 34% deviation. On the other hand, Peng et al. (2009) used the Hamilton and Crosser model (1962) to calculate the thermal conductivity of CuO/R113 nanorefrigerants. By comparing results with others researchers, the thermal conductivity result in this study is found to be in a good agreement with most models from literature. Fig. 6 shows the thermal conductivity of the Al2O3/PAG nanolubricant as a function of temperature. It clearly shows that thermal conductivity increases as volume concentrations increase. The highest thermal conductivity achievable is 0.153 W(m·K)−1 at 1.0% volume concentration and temperature of 303.15 K. In addition, the enhancement ratio of nanolubricant is 1.04 when compared to pure PAG under the same volume concentration and temperature. The measured thermal conductivity for all volume concentrations decreased with the increasing in temperature. The pattern is agreed well with the pure PAG behaviour as plotted using data from Booser (1994), which is presented by the solid straight line in Fig. 6. This behaviour can be explained when liquid is heated, the molecules of the liquid move apart, hence increasing the mean path. Consequently the probability of collision of molecules will be reduced. As a result, thermal conductivity decreases as temperature increases. It can be concluded that the thermal conductivity of the nanolubricant increases by volume concentration, but in contrast, decreases by temperature. In addition, the thermal conductivity enhancement of Al2O3/PAG

Fig. 6 – Thermal conductivity as a function of temperature for different volume concentrations.


Fig. 7 – Comparison of nanolubricant thermal conductivity value between present data and proposed Eq. (2).

nanolubricant is observed to be insignificant with less than 5% for the volume concentration up to 1.0%. Consequently, Eq. (2) is developed to estimate the thermal conductivity of nanolubricants for different volume concentrations and a wide range of temperature. The correlation has an average deviation of 0.34% and standard deviation of 0.26%. The equation is in good agreement within ±1.5% deviation compared to the experimental data as shown in Fig. 7 and applicable for 0 ≤ φ ≤ 1.0% and 303.15 ≤ T ≤ 353.15 K .

kr =

3.2.

φ ⎞4⎛ kNL T − 273.15 ⎞ −0.05 = 0.15 ⎛ 1 + 1+ ⎝ ⎠ ⎝ ⎠ kL 100 80

(2)

Viscosity of the nanolubricants

Viscosity of the Al2O3/PAG nanolubricants for 0.05 to 0.4% volume concentrations and temperature of 313.15 K have been plotted in Fig. 8. The figure shows that the viscosity of the

Fig. 8 – Variation of dynamic viscosity ratio as the function of volume concentration at 313.15 K.

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Fig. 9 – Viscosity against shear strain rate for volume concentration at 303.15 K.

nanolubricant increases exponentially with the increase of volume concentration. The model of Brinkman (1952), Pak and Cho (1998) and Wang et al. (1999) were utilized to compare with the present values for different volume concentrations and temperature of 313.15 K. It can be seen that all models were largely under prediction of the viscosity of nanolubricants. The classical models show that the effective viscosity depends on the volume concentration and viscosity of the base fluid. While the experimental results shows that the temperature, the particle diameter and the type of material also contributes to the enhancement of the effective viscosity of a nanofluids (Azmi et al., 2016b). Kole and Dey (2010) whom studied viscosity of Al2O3 in car engine coolant compared their viscosity result with Batchelor (1977), Brinkman (1952), Chen et al. (2007), Einstein (1956) and Krieger and Dougherty (1959). They found that all of the models failed to predict their measured viscosity of the nanofluids. Similar trend results also had been found by other researches (Mahbubul et al., 2013c; Namburu et al., 2007; Peng et al., 2009). For further investigation towards the behaviour of the nanolubricant, the viscosity of the pure PAG lubricant and nanolubricant were been measured as a function of shear strain rate for 303.15 K as depicted in Fig. 9. It clearly shows that, the viscosity of the pure PAG lubricant is independent of the shear strain rate, indicative of Newtonian behaviour. Adding the Al2O3 nanoparticles up to certain extend did not affect the behaviour of the lubricant as shown by 0.1 and 0.2% volume concentration. However, the nanolubricant tends to show non-Newtonian behaviour at high volume concentrations. At 0.3% volume concentration, the graph shows downward trend reflecting the shear thinning behaviour. Therefore, the nanolubricant behaves non-newtonian fluids for volume concentration above 0.3%. The same phenomenon on nanolubricant behaviour was also been observed by Kole and Dey (2011). Fig. 10 shows the temperature dependence of Al 2 O 3 nanolubricants viscosity for different volume concentrations. It clearly showed that the viscosity of nanolubricants decrease exponentially at elevated temperature. The highest

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Fig. 10 – Comparison of experimental value of viscosity at various temperatures.

viscosity ratio in this study were found to be 7.58 for nanolubricants with 0.4% volume concentration compared to the pure PAG at 313.15 K. The differences became closer align with the increment of temperature. The pattern again is agreed well when compared to pure PAG behaviour as plotted in Fig. 10 using the data from Booser (1994). High nanolubricants temperature is believed to intensify the Brownian motion as suggested by Namburu et al. (2007). This is the reason why the viscosity of nanolubricants is reduced as the temperature increases. Based on the result in Fig. 10, it is recommended to use the Al2O3/PAG nanolubricant with volume concentration of less than 0.3% for application in automotive air conditioning system. The effective viscosity of nanolubricants is exponentially increased for volume concentration higher than 0.3%. This can be due to the significant agglomeration that occurs in nanolubricants at high volume concentrations. The physical agglomeration of the suspended Al2O3 nanoparticle in lubricants was confirmed by TEM image in Fig. 1(b). Furthermore, the Al2O3/PAG nanolubricants exhibit non-Newtonion behaviour for volume concentration above 0.3% and confirmed by the result in Fig. 9. Higher viscosity of nanolubricants with more than 100 mPa.s will penalty with extra load, work under performance and lesser life cycle to the compressor. Simultaneously, the pumping power and pressure drop of the overall system are directly associated to the viscosity of nanolubricants (Mahbubul et al., 2012). There is no theoretical correlation available in literature for Al2O3 nanoparticle dispersed in PAG lubricant for designated volume concentration and temperature. However, there is similar work done by Kedzierski (2013) to investigate the viscosity of Al2O3 nanoparticle dispersed in POE lubricant for high concentration. Kedzierski (2013) has generalized the model for viscosity estimation of various types of lubricant dispersed by Al2O3 nanoparticle. Fig. 11 depicted the parity graph of Kedzierski model against the present viscosity data. The model is able to predict the viscosity of present measurements with the deviation between the experimental value and the model within ±15% as suggested by the author. Even though the equation could predict the viscosity of the Al2O3/PAG nanolubricant, but

Fig. 11 – Comparison of nanolubricant viscosity value between present data and Kedzierski (2013) model.

Fig. 12 – Comparison of nanolubricant viscosity value between present data and proposed Eq. (3).

the deviation is consider high. Hence, Eq. (3) has been proposed to estimate the viscosity of nanolubricants for different volume concentrations and a wide range of temperature. The correlation has an average deviation of 3.88% and standard deviation of 3.33%. The equation is in good agreement within ±10% compared to the present data as shown in Fig. 12 and it is applicable for 0 ≤ φ ≤ 0.3% and 303.15 ≤ T ≤ 353.15 K .

μr =

4.

T − 273.15 ⎞⎟ 0.3 μ NL ⎛⎜ φ ⎞⎟ 368 ⎛⎜ = ⎜1 + ⎟ ⎜ 0 .1 + ⎟ ⎠ μL ⎝ 100 ⎠ ⎝ 80

(3)

Conclusions

Thermal conductivity and viscosity of Al2O3/PAG nanolubricants have been studied. Like other nanofluids, the experimental investigation found that the thermal conductivity of the nanolubricants increases with volume concentration. But contrary to other nanofluids, the thermal conductivity of


nanolubricants decreases as temperature increases. Similar to thermal conductivity, volume concentration and temperature have significant effects over the viscosity of nanolubricants. The results showed that by increasing the volume concentration, the viscosity of nanolubricants increases. However, it will decrease by increment of temperature. From the observation, for the same volume concentration, the viscosity increment rate was found to be higher compared to the increment rate of thermal conductivity. The highest thermal conductivity ratio was found to be 1.04. On the other hand, the highest viscosity was observed to be 7.58 compared to the based lubricant. Hence it is important to find the ideal volume concentration of Al2O3/PAG nanolubricants in order to get optimum performance of the automotive vapour compression system. Therefore, it is recommended to use the Al2O3/PAG nanolubricants with volume concentration of less than 0.3% for application in automotive air conditioning systems. Further investigations on the performance of automotive air conditioning system using Al2O3/PAG nanolubricants are required to extend the present work.

Acknowledgements The authors are grateful to the Universiti Malaysia Pahang (UMP) and Automotive Engineering Centre (AEC) for financial support given under RDU151411 (RAGS/1/2015/TK0/UMP/03/2).

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