Experimental Thermal and Fluid Science 76 (2016) 221–227

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Experimental Thermal and Fluid Science

journal homepage: www.elsevier .com/locate /et fs

An experimental study on rheological behavior of non-Newtonian hybridnano-coolant for application in cooling and heating systems

http://dx.doi.org/10.1016/j.expthermflusci.2016.03.0150894-1777/� 2016 Elsevier Inc. All rights reserved.

⇑ Corresponding author.E-mail addresses: masoud.afrand@pmc.iaun.ac.ir, masoud_afrand@yahoo.com

(M. Afrand).

Hamed Eshgarf, Masoud Afrand ⇑Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

a r t i c l e i n f o

Article history:Received 4 December 2015Received in revised form 12 March 2016Accepted 14 March 2016Available online 21 March 2016

Keywords:Non-Newtonian behaviorHybrid nano-coolantSiO2 nanoparticleCOOH functionalized MWCNTsEG–water

a b s t r a c t

In this paper, the rheological behavior of COOH functionalized MWCNTs–SiO2/EG–water hybridnano-coolant for application in cooling systems at temperatures ranging from 27.5 �C to 50 �C has beenexamined. Stable and homogeneous suspensions, with solid volume fractions ranging from 0.0625% to 2%were prepared by dispersing dry MWCNTs and SiO2 nanoparticles (50:50 vol.%) in a specified amount of abinary mixture of EG–water (50:50 vol.%). Viscosity measurements were performed at the shear raterange of 0.612 s�1 to 122.3 s�1 for each nano-coolant sample. Results showed that the base fluid exhibitsNewtonian behavior and the nano-coolant samples exhibit a pseudoplastic rheological behavior with apower law index of less than unity (n < 1). The results also revealed that the apparent viscosity generallyincreases with an increase in the solid volume fraction and decreases with increasing temperature.

� 2016 Elsevier Inc. All rights reserved.

1. Introduction

A mixture of water and ethylene glycol (EG), called antifreezecoolant, is used for application in cooling systems, heat exchangers,solar collectors, automobile radiators and so on [1–5]. However,this mixture has a low thermal conductivity for application in ther-mal systems. In this regard, many researchers have attempted toenhance its thermal conductivity by dispersing the nanoparticlesor carbon nanotubes (CNTs) [6–11]. These suspensions, callednanofluids, have been widely investigated in the recent decade[12–19].

In recent years, growing attention has been paid to use newnanofluids combined of various nanoparticles, called hybridnanofluids, to improve the heat transfer rate [20–25]. For example,Hemmat Esfe et al. [26] dispersed Cu and TiO2 nanoparticles in amixture of water/EG (60:40) to enhance the thermal conductivityof the coolant. They examined thermal conductivity of this hybridnanofluid for various solid concentrations at different tempera-tures. They also proposed two new correlations for predicting thethermal conductivity of the hybrid nanofluids as function of solidconcentration and temperature.

It is clear that adding nano-sized materials to the fluids alterstheir other thermo-physical properties. In this regard, rheological

behavior is an effective parameter in pumping power to circulatethe nano-coolants. Therefore, examination of the viscosity ofnanofluids is necessary to calculate the needed pumping power.Several investigations have been performed on the rheologicalbehavior of numerous nanofluids. A summary of such studies forthe viscosity of nanofluids based on Newtonian behavior is pre-sented in Table 1. These studies revealed that the viscosity ofnanofluids enhances by increasing the particle concentration, anddecreases with an increase in temperature. However, a few studieshave been reported on non-Newtonian nanofluids. Among thesestudies, the study of the rheological behavior of TiO2–ethyleneglycol nanofluids was performed by Cabaleiro et al. [43]. Theydetermined the viscosity of the nanofluid at nanoparticle massconcentrations up to 25% for temperature ranging from 283.15 Kto 323.15 K. They also repeated the experiments at various shearrates and showed that the nanofluid exhibited a non-Newtonianbehavior according to the Ostwald–de Waele model. The rheolog-ical behavior of mixtures of polycarbonate containing between0.5 and 15 wt% carbon nanotubes was investigated by Potschkeet al. [44]. They employed an oscillatory rheometry for theirexperiments and reported that the viscosity curves above 2 wt%nanotubes show a greater reduction in frequency than samplescontaining lower nanotube loadings. Their results also showed thatsamples containing more than 2 wt% nanotubes show a non-Newtonian behavior at lower frequencies. Phuoc et al. [45] studiedthe viscosity of nanofluids containing multi-walled carbon nan-otubes (MWCNTs). They used MWCNTs to enhance or reduce thefluid base viscosity. Their results revealed a reduction up to 20%

Table 1A summary of existing studies for the viscosity of nanofluids based on consideredparameters.

Particles Basefluid

Temperaturerange (�C)

Volume fractionrange (%)

Referencenumber

TiO2 Water 25 5–12 [27]CuO Water 5–50 5–15 [28]SiO2 EG:

Water(�35)–50 0–10 [29]

CuO EG:Water

(�35)–50 0–6.12 [30]

TiO2 Water 15–35 0.2–2 [31]Al2O3 EG:

Water(�35)–50 1–10 [32]

Al2O3 andZr

Water 20–80 3,6 [33]

Ag Water 50–90 0.3–0.9 [34]Fe3O4 EG:

Water0–50 0–1 [35]

Al2O3 andTiO2

EG:Water

15–40 1–8 [36]

MgO Water 24–60 <1 [37]ZnO EG 25–50 0.25–5 [38]MWCNT Water 25–55 0.05–1 [39]DWCNT Water 27–67 0.01–0.4 [40]ZnO Turbine

oil0–60 0.1–4 [41]

SWCNT EG 30–60 0–0.1 [42]

Table 2Characteristics of MWCNTs and SiO2 nanoparticles.

Characteristic Value

MWCNTs SiO2

222 H. Eshgarf, M. Afrand / Experimental Thermal and Fluid Science 76 (2016) 221–227

in the viscosity-reduction case. They also observed a non-Newtonian behavior in the viscosity-enhancement case.

The effects of temperature and shearing time on viscosity ofAl2O3/water and CNT/water nanofluids were experimentally inves-tigated by Aladag et al. [46]. The experiments showed that CNT/water nanofluids exhibited a Newtonian behavior at high shearrate, while Al2O3/water nanofluid behaves as a non-Newtonianfluid within the range of low temperatures. Tamjid and Guenther[47] studied the rheological behavior of Ag/EG at the solid volumefraction range of 0.11–4.38%. Their measurements showed thatnanofluid samples generally exhibited a yield pseudoplastic behav-ior. They also evaluated the shear stress–shear rate dependency byusing Bingham plastic, Herschel–Bulkley and Casson models.Moghaddam et al. [48] measured the rheological properties ofgrapheme/glycerol nanofluids at different mass fractions(0.0025–0.02) and temperatures (20–60 �C). Their results showedthat the viscosity of the nanofluids increases by increasing themass fraction, and decreases with increasing temperature. Theyalso observed a very strong shear thinning behavior of thegrapheme/glycerol nanofluids. Nevertheless, as mentioned above,few studies were focused on the rheological behavior of hybridnano-coolants. On the other hand, Newtonian or non-Newtonianbehavior of nanofluids plays an important role in thermal and fluidflow applications. Hence, there is a key need to the examination ofthe rheological behavior of hybrid nano-coolants. In this paper, forthe first time, the rheological behavior of COOH functionalizedMWCNTs–SiO2/EG–water hybrid nano-coolants is evaluated. Inthis regard, the nano-coolant samples were prepared at varioussolid volume fractions and were experimented under differenttemperatures.

Purity >97% >99%Color Black White

Size Outer diameter: 5–15 (nm) 20–30 (nm)Inner diameter: 3–5 (nm)Length: 50 (lm)

Thermal conductivity 1500 (W/m K) 1.3 (W/m K)Bulk density: 0.27 (g/cm3) <0.10 (g/cm3)True density �2.1 (g/cm3) 2.4 (g/cm3)Specific surface area (SSA) 233 (m2/g) 180–600 (m2/g)Content of –COOH 2.56 (wt%) –

2. Experimentation

2.1. Samples preparation

There are two techniques to prepare stable and homogeneoussuspensions containing carbon nanotubes. The first is the use of asurfactant, and the second is the functionalization of the carbonnanotubes. Adding a surfactant may have undesirable effects on

the thermal properties of the samples. While, functionalizingMWCNTs using carboxyl (COOH) makes the carbon nanotubeshydrophilic; thus, the stability of the suspensions is improved[39]. Therefore, using COOH-functionalized MWCNTs seems moresuitable.

Stable and homogeneous suspensions, with solid volume frac-tions ranging from 0.0625% to 2% were prepared by dispersingdry MWCNTs and SiO2 nanoparticles (50:50 vol.%) in a specifiedamount of the binary mixture of EG–water (50:50 vol.%). The char-acteristics of COOH-functionalized MWCNTs, SiO2 nanoparticles,water and ethylene glycol are presented in Tables 2 and 3. In orderto obtain a characterization of the nano-sized materials, the struc-tural properties of the dry MWCNTs and SiO2 nanoparticles weremeasured by using X-ray diffraction as shown in Fig. 1.

The quantity of MWCNTs and SiO2 nanoparticles required fordifferent solid volume fractions can be determined using the fol-lowing equation,

u ¼wq

� �MWCNTs

þ wq

� �SiO2

wq

� �MWCNTs

þ wq

� �SiO2

þ wq

� �Water

þ wq

� �EG

264

375� 100 ð1Þ

where u is the percentage of solid volume fraction, q is the densityin kg/m3, and w is the mass in kg.

In the present work, to make stable samples of nano-coolant,after magnetic stirring for 2 h, the suspensions were exposed toan ultrasonic processor (Hielscher Company, Germany) with thepower of 400 W and frequency of 24 kHz for 5–6 h. This processwas applied to break down the agglomeration between the parti-cles, which leads to achieving a uniform dispersion and a stablesuspension. The photographs of MWCNTs, SiO2 nanoparticles, EGand a nano-coolant sample are displayed in Fig. 2.

2.2. Viscosity measurement

The viscosity of the nano-coolant with solid volume fractions of0.0625%, 0.25%, 0.5%, 0.75%, 1%, 1.5% and 2.0% were measured inthe temperature range of 25–50 �C. The Brookfield DV-I PRIME dig-ital Viscometer, with a temperature bath equipped, was employedto measure the viscosities of the nano-coolant samples in the shearrate range of 0.612 s�1 to 122.3 s�1. The accuracy and repeatabilityof Brookfield Viscometer are ±1% and 0.2% of full scale range (FSR),respectively. Before the measurement of dynamic viscosity ofnanofluids, the Viscometer was tested with ethylene glycol andwater at room temperature. To evaluate the rheological behavior(Newtonian or non-Newtonian) of the nano-coolants, all experi-ments were repeated at different shear rates for each solid volumefraction and temperature.

Table 3Characteristics of water and ethylene glycol.

Characteristic Value

Water Ethylene glycol

Chemical formula H2O C2H6O2

Molar mass 18.02 g/mol 62.07 g/molAppearance Almost colorless,

transparentClear, colorless liquid

Odor Odorless OdorlessDensity 998.21 kg/m3 1113.20 kg/m3

Melting point 0.00 �C �12.9 �CBoiling point 100 �C 197.3 �CThermal

conductivity0.6 W/m K (@20 �C) 0.244 W/m K

(@20 �C)Viscosity 1 cP (@20 �C) 16.1 cP (@20 �C)

H. Eshgarf, M. Afrand / Experimental Thermal and Fluid Science 76 (2016) 221–227 223

3. Results and discussion

Before performing the rheological measurements of nanofluids,in order to ensure the accuracy of the Viscometer, a comparison ofthe viscosity of the mixture of water and EG was made betweenthe results obtained by the Viscometer and those presented inASHRAE [49]. As shown in Fig. 3, the experimental values of viscos-ity are in good agreement with the ASHRAE data, and there is a lit-tle difference (average 4.3%) at all temperatures considered.

Fig. 4 shows the viscosity and shear stress versus the shear rateat 27.5 �C for the base fluid and the nano-coolants with two lowconcentrations of nano-coolants (0.0625% and 0.25%). The mea-surements show a little decrease in the viscosity of base fluid withan increase in shear rate. This behavior is due to shear heating con-siderations, which occur in high shear rates. Moreover, the viscos-ity of the base fluid is independent of the shear rate, which meansthat the base fluid exhibits Newtonian behavior. However, it can beobserved that by adding the nono-sized materials to the base fluid,the viscosity increases and is dependent on shear rate, whichmeans that the behavior of nano-coolant is Non-Newtonian. Thisfigure also clearly indicates that the nano-coolants used in thisstudy possess shear-thinning behavior. Therefore, COOH function-alized MWCNTs–SiO2/EG–water hybrid nano-coolants exhibit apseudoplastic rheological behavior and follow the power law (orOstwald de Waele) model given in Eq. (2) with a power law indexof less than unity (n < 1).

Fig. 1. XRD patterns for MWCN

s ¼ m _cn ð2Þ

where s is the shear stress, _c is the shear rate, m is consistencyindex and n is the power law index. Moreover, the apparent viscos-ity for the power law fluid is thus given by:

l ¼ m _cn�1 ð3Þ

in which, l is the apparent viscosity.The apparent viscosity and shear stress for various solid volume

fractions at different temperatures are plotted against the shearrate in Fig. 5. It can be observed that the Newtonian behavior ofbase fluid is mostly changed to non-Newtonian for the nano-coolant because of the complex interactions between the base fluidand hybrid nano-sized materials. Moreover, by increasing solidvolume fraction, the non-Newtonian behavior becomes important.It can also be seen that for an increase in the solid volume fractionfrom 0% to 2%, the apparent viscosity of the nano-coolantsincreases incredibly (approximately 20,000%). Moreover, thisfigure shows the significant decrease in the apparent viscositywhen the shear rate is increased. As an example, at the solidvolume fraction of 1%, for an increase in shear rate from0.612 s�1 to 4.898 s�1, the apparent viscosity at the temperaturesof 30, 40 and 50 �C decrease approximately 59%, 63%, and 72%,respectively. These changes are very important for engineeringapplications such as pumping power and convective heat transfer.

Fig. 5 also shows that the nano-coolant samples exhibit a pseu-doplastic rheological behavior and follow the power law modelwith a power law index of less than unity (n < 1). Therefore, theinvestigation of the consistency index (m) and the power law index(n) seems necessary.

As shown in Fig. 5, the consistency and the power law index aredependent on temperature and solid volume fraction. Theseparameters could be obtained by curve-fitting on shear stress–shear rate graphs using Eq. (2). For example, the curve-fittingresults for nano-coolant with solid volume fraction of 0.75% aredemonstrated in Fig. 6. This figure shows a good agreementbetween experimental data and results obtained by curve-fitting.Moreover, the pseudoplastic rheological behavior is clearlyobserved in this figure.

Based on curve-fitting results, the power law index ofnano-coolant, as a function of the solid volume fraction at differenttemperatures, is shown in Fig. 7. It can be observed that the powerlaw index decreases with an increase in solid volume fraction and

Ts and SiO2 nanoparticles.

Fig. 2. Photographs of MWCNTs, SiO2 nanoparticles, EG and nano-coolant.

Temperature (oC)

Visc

osity

(mPa

.s)

30 35 40 45 500

1

2

3

4 ASHRAE data [49]

Experimental data

Fig. 3. Comparison between experimental and ASHRAE [49] data for EG–water(50:50 vol.%).

Shear rate (1/s)

Shea

rstre

ss(P

a)

Visc

osity

(mPa

.s)

0 20 40 60 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

5

10

15

20

25

30

35ϕ= 0ϕ= 0.0625%ϕ= 0.25%

Viscosity

0 20 40 60 800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

5

10

15

20

25

30

35ϕ= 0ϕ= 0.0625%ϕ= 0.25%

Shear stress

Fig. 4. Viscosity and shear stress versus shear rate at 27.5 �C for the base fluid andthe nano-coolants.

224 H. Eshgarf, M. Afrand / Experimental Thermal and Fluid Science 76 (2016) 221–227

temperature and is less than unity (n < 1) for all nano-coolant sam-ples. When this parameter is closer to 1, it means that the rheolog-ical behavior of nano-coolants is closer to a Newtonian behavior.As can be seen in Fig. 7, at lower solid volume fractions (e.g.0.0625%), the power law index is closer to 1.

Fig. 8 presents the consistency index of nano-coolant as a func-tion of the solid volume fraction at different temperatures. As men-tioned in Eq. (3), the consistency index directly affects the apparentviscosity. Results show that the consistency index of nano-coolantand consequently apparent viscosity is influenced by both the solidvolume fraction and temperature. It can be seen that the consis-tency index of nanofluids generally increases with an increase inthe solid volume fraction. This is in agreement with the resultsfor the viscosity of Newtonian nanofluids reported in previousinvestigations [26–41]. The reason may be related to the randommovement of particles in the base fluid. Moreover, when nanopar-ticles and nanotubes are added to the base fluid, these nano-materials scatter in the base fluid. Due to van der Waals forcesbetween the nanoparticles and the base fluid, symmetric and lar-ger nanoclusters are formed. These nanoclusters prevent the

movement of base fluid on each other, leading to an increase in vis-cosity. Fig. 8 also displays the decrease in the consistency indexand consequently apparent viscosity. This is due to the fact thatwith increasing temperature, intermolecular interactions betweenthe molecules become weak and therefore the apparent viscositydecreases. Generally, the observed trend for consistency index isin good agreement with the results of previous works [50,51].

As mentioned above (Figs. 7 and 8), the consistency index andpower law index are functions of temperature and solid volumefraction. Therefore, Eqs. (4) and (5) are proposed to predict the con-sistency index and power law index, respectively, using theMarquardt–Levenberg algorithm [52].

m¼0:01125

þ 38:19�0:3T7:655þ0:6953T

� �0:01138uþ0:5529u2�0:3613u3þ0:07u4� �

ð4Þ

n¼0:8543

þ �3:303þ1:418T15:8þ0:3914T

� ��0:7366uþ0:8519u2�0:4552u3þ0:08871u4� �

ð5Þ

Shear rate (1/s)

App

aren

tvisc

osity

(mPa

.s)

Shea

rstre

ss(P

a)

0 20 40 60 800

2

4

6

8

10

12

14

0

0.1

0.2

0.3

0.4

0.5

T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

ϕ= 0.0625%

Shear rate (1/s)

App

aren

tvisc

osity

(mPa

.s)

Shea

rstre

ss(P

a)

0 10 20 30 400

10

20

30

40

0

0.1

0.2

0.3

0.4

0.5

T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

ϕ = 0.25%

Shear rate (1/s)

App

aren

tvisc

osity

(mPa

.s)

Shea

rstre

ss(P

a)

0 5 10 15 20 250

25

50

75

100

125

150

175

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

ϕ = 0.5%

Shear rate (1/s)

App

aren

tvisc

osity

(mPa

.s)

Shea

rstre

ss(P

a)

0 1 2 3 4 50

100

200

300

400

500

600

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

ϕ =1%

Shear rate (1/s)

App

aren

tvisc

osity

(mPa

.s)

Shea

rstre

ss(P

a)

0 1 2 3 40

200

400

600

800

1000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

ϕ =1.5%

Shear rate (1/s)

App

aren

tvisc

osity

(mPa

.s)

Shea

rstre

ss(P

a)

0 1 2 3 40

200

400

600

800

1000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

ϕ = 2%

Fig. 5. Viscosity and shear stress versus shear rate for various nano-coolant samples at different temperatures.

H. Eshgarf, M. Afrand / Experimental Thermal and Fluid Science 76 (2016) 221–227 225

These correlations can be applied in the temperatures from27.5 �C to 50 �C, and solid volume fraction range of 0.0625% to2%. These ranges may be used for application in heating and cool-ing systems such as solar heaters and heat exchangers.

In order to evaluate the accuracy of the correlations, the com-parison of shear stress obtained by the correlations and Eq. (2) with

experimental data for various nanofluid samples is depicted inFig. 9.

It can be observed that most points are near the equality line oron it. This figure shows that there is a good agreement betweenexperimental data and the results obtained by the suggestedcorrelations.

Shear rate (1/s)

Shea

rstre

ss(P

a)

0 1 2 3 4 5 60

0.1

0.2

0.3

Curve-fittingExperimental data @ 40oCExperimental data @ 50oC

R2=0.9981m=0.147n=0.441

R2=0.9919m=0.112n=0.387

Fig. 6. Curve-fitting results for nano-coolant with solid volume fraction of 0.75%.

Solid volum fraction (%)

Pow

erla

win

dex

0 0.25 0.5 0.75 1 1.25 1.5 1.75 20.3

0.4

0.5

0.6

0.7

0.8

T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

Fig. 7. Power law index of nano-coolant versus solid volume fraction at differenttemperatures.

Solid volum fraction (%)0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

0

0.1

0.2

0.3

0.4

0.5

0.6 T=27.5oCT=30oCT=35oCT=40oCT=45oCT=50oC

Con

siste

ncy

inde

x(P

a.sn )

Fig. 8. Consistency index of nano-coolant versus solid volume fraction at differenttemperatures.

Experimental data

Cor

rela

tion

resu

lts

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.1

0.2

0.3

0.4

0.5

0.6

0.7Equality lineϕ=0.25%ϕ=0.75%ϕ=1%ϕ=2%

Fig. 9. Comparison of shear stress obtained by correlations with experimental datafor various nanofluid samples.

226 H. Eshgarf, M. Afrand / Experimental Thermal and Fluid Science 76 (2016) 221–227

4. Conclusion

In the present study, the rheological behavior of COOH function-alized MWCNTs–SiO2/EG–water hybrid nano-coolant for applica-tion in cooling systems at temperature ranging from 25 �C to50 �C for various suspensions at the solid volume fraction of0.0625%, 0.25%, 0.5%, 0.75%, 1%, 1.5% and 2.0% has been examined.Viscosity measurements at different shear rates showed that thebase fluid exhibits Newtonian behavior. However, by adding thenono-sized materials to the base fluid, the viscosity increases andthe behavior of nano-coolant becomes Non-Newtonian. The resultsalso revealed that for an increase in the solid volume fraction from 0to 2%, the apparent viscosity of the nano-coolants increases incred-ibly (approximately 20,000%). Moreover, measurements showed asignificant decrease in the apparent viscosity when the shear ratewas increased. Results also clearly indicate that the nano-coolantsused in this study possess shear-thinning behavior. The consistencyand the power law index, related to shear-thinning behavior, wereobtained by curve-fitting on shear stress–shear rate graphs. Curve-fitting results showed that the power law index of nano-coolantdecreases an increase in solid volume fraction and temperature,and it is less than unity (n < 1) for all nano-coolant samples.Curve-fitting results also revealed that the consistency index ofnano-coolant and consequently apparent viscosity generallyincreases with an increase in the solid volume fraction, anddecreases with increasing temperature.

Acknowledgement

The authors would like to thank the Najafabad Branch, IslamicAzad University, Najafabad, Iran for the support.

References

[1] W. Duangthongsuk, S. Wongwises, Comparison of the effects of measured andcomputed thermophysical properties of nanofluids on heat transferperformance, Exp. Therm. Fluid Sci. 34 (2010) 616–624.

[2] O. Mahian, A. Kianifar, S.A. Kalogirou, I. Pop, S. Wongwises, A review of theapplications of nanofluids in solar energy, Int. J. Heat Mass Transfer 57 (2013)582–594.

H. Eshgarf, M. Afrand / Experimental Thermal and Fluid Science 76 (2016) 221–227 227

[3] O. Mahian, A. Kianifar, C. Kleinstreuer, A.-N. Moh’d A, I. Pop, A.Z. Sahin, S.Wongwises, A review of entropy generation in nanofluid flow, Int. J. Heat MassTransfer 65 (2013) 514–532.

[4] M.R. Raveshi, A. Keshavarz, M.S. Mojarrad, S. Amiri, Experimental investigationof pool boiling heat transfer enhancement of alumina–water–ethylene glycolnanofluids, Exp. Therm. Fluid Sci. 44 (2013) 805–814.

[5] M. Nazari, M. Karami, M. Ashouri, Comparing the thermal performance ofwater, ethylene glycol, alumina and CNT nanofluids in CPU cooling:experimental study, Exp. Therm. Fluid Sci. 57 (2014) 371–377.

[6] O. Mahian, A. Kianifar, S. Wongwises, Dispersion of ZnO nanoparticles in amixture of ethylene glycol–water, exploration of temperature-dependentdensity, and sensitivity analysis, J. Cluster Sci. 24 (2013) 1103–1114.

[7] L.S. Sundar, M.H. Farooky, S.N. Sarada, M.K. Singh, Experimental thermalconductivity of ethylene glycol and water mixture based low volumeconcentration of Al2O3 and CuO nanofluids, Int. Commun. Heat MassTransfer 41 (2013) 41–46.

[8] M.S. Mojarrad, A. Keshavarz, M. Ziabasharhagh, M.M. Raznahan, Experimentalinvestigation on heat transfer enhancement of alumina/water and alumina/water–ethylene glycol nanofluids in thermally developing laminar flow, Exp.Therm. Fluid Sci. 53 (2014) 111–118.

[9] M. Hemmat Esfe, S. Saedodin, M. Akbari, A. Karimipour, M. Afrand, S.Wongwises, M.R. Safaei, M. Dahari, Experimental investigation anddevelopment of new correlations for thermal conductivity of CuO/EG–waternanofluid, Int. Commun. Heat Mass Transfer 65 (2015) 47–51.

[10] M.H. Esfe, M. Afrand, A. Karimipour, W.-M. Yan, N. Sina, An experimental studyon thermal conductivity of MgO nanoparticles suspended in a binary mixtureof water and ethylene glycol, Int. Commun. Heat Mass Transfer 67 (2015) 173–175.

[11] M. Soltanimehr, M. Afrand, Thermal conductivity enhancement of COOH-functionalized MWCNTs/ethylene glycol-water nanofluid for application inheating and cooling systems, Appl. Therm. Eng. (2016), http://dx.doi.org/10.1016/j.applthermaleng.2016.03.089.

[12] A. Celen, A. Çebi, M. Aktas, O. Mahian, A.S. Dalkilic, S. Wongwises, A review ofnanorefrigerants: flow characteristics and applications, Int. J. Refrig. 44 (2014)125–140.

[13] M.H. Esfe, A. Naderi, M. Akbari, M. Afrand, A. Karimipour, Evaluation ofthermal conductivity of COOH-functionalized MWCNTs/water viatemperature and solid volume fraction by using experimental data and ANNmethods, J. Therm. Anal. Calorim. 121 (2015) 1273–1278.

[14] M.H. Esfe, A.A.A. Arani, W.-M. Yan, H. Ehteram, A. Aghaie, M. Afrand, Naturalconvection in a trapezoidal enclosure filled with carbon nanotube–EG–waternanofluid, Int. J. Heat Mass Transfer 92 (2016) 76–82.

[15] M.H. Esfe, S. Saedodin, N. Sina, M. Afrand, S. Rostami, Designing an artificialneural network to predict thermal conductivity and dynamic viscosity offerromagnetic nanofluid, Int. Commun. Heat Mass Transfer 68 (2015) 50–57.

[16] M.H. Esfe, M. Afrand, W.-M. Yan, M. Akbari, Applicability of artificial neuralnetwork and nonlinear regression to predict thermal conductivity modeling ofAl2O3–water nanofluids using experimental data, Int. Commun. Heat MassTransfer 66 (2015) 246–249.

[17] M.H. Esfe, M. Afrand, S. Wongwises, A. Naderi, A. Asadi, S. Rostami, M. Akbari,Applications of feedforward multilayer perceptron artificial neural networksand empirical correlation for prediction of thermal conductivity of Mg (OH)2–EG using experimental data, Int. Commun. Heat Mass Transfer 67 (2015) 46–50.

[18] M.H. Esfe, K. Motahari, E. Sanatizadeh, M. Afrand, H. Rostamian, M. Hassani,Estimation of thermal conductivity of CNTs-water in low temperature byartificial neural network and correlation, Int. Commun. Heat Mass Transfer(2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2015.12.012.

[19] M. Hemmat Esfe, W.-M. Yan, M. Afrand, M. Sarraf, D. Toghraie, M. Dahari,Estimation of thermal conductivity of Al2O3/water (40%)-ethylene glycol (60%)by artificial neural network and correlation using experimental data, Int.Commun. Heat Mass Transfer (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.02.002.

[20] S. Suresh, K. Venkitaraj, P. Selvakumar, M. Chandrasekar, Synthesis of Al2O3–Cu/water hybrid nanofluids using two step method and its thermo physicalproperties, Colloids Surf. A: Physicochem. Eng. Aspects 388 (2011) 41–48.

[21] S. Suresh, K. Venkitaraj, P. Selvakumar, M. Chandrasekar, Effect of Al2O3–Cu/water hybrid nanofluid in heat transfer, Exp. Therm. Fluid Sci. 38 (2012) 54–60.

[22] L. Chen, M. Cheng, D. Yang, L. Yang, Enhanced thermal conductivity ofnanofluid by synergistic effect of multi-walled carbon nanotubes and Fe2O3

nanoparticles, Appl. Mech. Mater. 548–549 (2014) 118–123.[23] M.J. Nine, B. Munkhbayar, M.S. Rahman, H. Chung, H. Jeong, Highly productive

synthesis process of well dispersed Cu2O and Cu/Cu2O nanoparticles and itsthermal characterization, Mater. Chem. Phys. 141 (2013) 636–642.

[24] D. Madhesh, R. Parameshwaran, S. Kalaiselvam, Experimental investigation onconvective heat transfer and rheological characteristics of Cu–TiO2 hybridnanofluids, Exp. Therm. Fluid Sci. 52 (2014) 104–115.

[25] M. Hemmat Esfe, S. Saedodin, W.-M. Yan, M. Afrand, N. Sina, Study on thermalconductivity of water-based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles, J. Therm. Anal. Calorim. 124 (2016) 455–460.

[26] M. Hemmat Esfe, S. Wongwises, A. Naderi, A. Asadi, M.R. Safaei, H. Rostamian,M. Dahari, A. Karimipour, Thermal conductivity of Cu/TiO2–water/EG hybridnanofluid: experimental data and modeling using artificial neural network andcorrelation, Int. Commun. Heat Mass Transfer 66 (2015) 100–104.

[27] W.J. Tseng, K.C. Lin, Rheology and colloidal structure of aqueous TiO2

nanoparticle suspensions, Mater. Sci. Eng., A 355 (2003) 186–192.[28] D.P. Kulkarni, D.K. Das, G.A. Chukwu, Temperature dependent rheological

property of copper oxide nanoparticles suspension (nanofluid), J. Nanosci.Nanotechnol. 6 (2006) 1150–1154.

[29] P.K. Namburu, D.P. Kulkarni, A. Dandekar, D.K. Das, Experimental investigationof viscosity and specific heat of silicon dioxide nanofluids, Micro & Nano Lett.,IET 2 (2007) 67–71.

[30] P.K. Namburu, D.P. Kulkarni, D. Misra, D.K. Das, Viscosity of copper oxidenanoparticles dispersed in ethylene glycol and water mixture, Exp. Therm.Fluid Sci. 32 (2007) 397–402.

[31] W. Duangthongsuk, S. Wongwises, Measurement of temperature-dependentthermal conductivity and viscosity of TiO2-water nanofluids, Exp. Therm. FluidSci. 33 (2009) 706–714.

[32] B.C. Sahoo, R.S. Vajjha, R. Ganguli, G.A. Chukwu, D.K. Das, Determination ofrheological behavior of aluminum oxide nanofluid and development of newviscosity correlations, Petrol. Sci. Technol. 27 (2009) 1757–1770.

[33] U. Rea, T. McKrell, L. Hu, J. Buongiorno, Laminar convective heat transfer andviscous pressure loss of alumina–water and zirconia–water nanofluids, Int. J.Heat Mass Transfer 52 (2009) 2042–2048.

[34] L. Godson, B. Raja, D. Mohan Lal, S. Wongwises, Experimental investigation onthe thermal conductivity and viscosity of silver-deionized water nanofluid,Exp. Heat Transfer 23 (2010) 317–332.

[35] L.S. Sundar, E.V. Ramana, M.K. Singh, A.C.M. De Sousa, Viscosity of low volumeconcentrations of magnetic Fe3O4 nanoparticles dispersed in ethylene glycoland water mixture, Chem. Phys. Lett. 554 (2012) 236–242.

[36] T. Yiamsawas, O. Mahian, A.S. Dalkilic, S. Kaewnai, S. Wongwises, Experimentalstudies on the viscosity of TiO2 and Al2O3 nanoparticles suspended in amixture of ethylene glycol and water for high temperature applications, Appl.Energy 111 (2013) 40–45.

[37] M. Hemmat Esfe, S. Saedodin, M. Mahmoodi, Experimental studies on theconvective heat transfer performance and thermophysical properties of MgO–water nanofluid under turbulent flow, Exp. Therm. Fluid Sci. 52 (2014) 68–78.

[38] M. Hemmat Esfe, S. Saedodin, An experimental investigation and newcorrelation of viscosity of ZnO–EG nanofluid at various temperatures anddifferent solid volume fractions, Exp. Therm. Fluid Sci. 55 (2014) 1–5.

[39] M. Hemmat Esfe, S. Saedodin, O. Mahian, S. Wongwises, Thermophysicalproperties, heat transfer and pressure drop of COOH-functionalized multiwalled carbon nanotubes/water nanofluids, Int. Commun. Heat Mass Transfer58 (2014) 176–183.

[40] M. Hemmat Esfe, S. Saedodin, O. Mahian, S. Wongwises Heat transfercharacteristics and pressure drop of COOH-functionalized DWCNTs/waternanofluid in turbulent flow at low concentrations, Int. Commun. Heat MassTransfer 73 (2014) 186–194.

[41] S. Zeinali Heris, M.A. Razbani, P. Estellé, O. Mahian, Rheological behavior ofzinc-oxide nanolubricants, J. Dispersion Sci. Technol. 36 (2015) 1073–1079.

[42] M. Baratpour, A. Karimipour, M. Afrand, S. Wongwises, Effects of temperatureand concentration on the viscosity of nanofluids made of single-wall carbonnanotubes in ethylene glycol, Int. Commun. Heat Mass Transfer (2016), http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.02.008.

[43] D. Cabaleiro, M.J. Pastoriza-Gallego, C. Gracia-Fernández, M.M. Piñeiro, L. Lugo,Rheological and volumetric properties of TiO2-ethylene glycol nanofluids,Nanoscale Res. Lett. 8 (2013) 286 (13 pages).

[44] P. Potschke, T.D. Fornes, D.R. Paul, Rheological behavior of multi walled carbonnanotube/polycarbonate composites, Polymer 43 (2002) 3247–3255.

[45] T.X. Phuoc, M. Massoudi, R.H. Chen, Viscosity and thermal conductivity ofnanofluids containing multi-walled carbon nanotubes stabilized by chitosan,Int. J. Therm. Sci. 50 (2011) 12–18.

[46] B. Aladag, S. Halelfadl, N. Doner, T. Mare, S. Duret, P. Estelle, Experimentalinvestigations of the viscosity of nanofluids at low temperatures, Appl. Energy97 (2012) 876–880.

[47] E. Tamjid, B.H. Guenther, Rheology and colloidal structure of silvernanoparticles dispersed in diethylene glycol, Powder Technol. 197 (2010)49–53.

[48] M.B. Moghaddam, E.K. Goharshadi, M.H. Entezari, P. Nancarrow, Preparation,characterization and rheological properties of grapheme–glycerol nanofluids,Chem. Eng. J. 231 (2013) 365–372.

[49] A.F. Handbook, American Society of Heating, Refrigerating and Air-conditioning Engineers, Inc., Atlanta, GA, USA, 2009.

[50] M. Hojjat, S.G. Etemad, R. Bagheri, J. Thibault, Rheological characteristics ofnon-Newtonian nanofluids: experimental investigation, Int. Commun. HeatMass Transfer 38 (2011) 144–148.

[51] F. Yasar, H. Togrul, N. Arslan, Flow properties of cellulose and carboxymethylcellulose from orange peel, J. Food Eng. 81 (2007) 187–199.

[52] D.W. Marquardt, An algorithm for least squares estimation of parameters, J.Soc. Ind. Appl. Math. 11 (1963) 431–441.