PaperM_8258.pdf

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Developing a new correlation to estimate the thermal conductivity of MWCNT-CuO/water hybrid nanofluid via an experimental investigation

Masoud Zadkhast¹^•Davood Toghraie²^•Arash Karimipour¹

Received: 22 October 2016 / Accepted: 17 February 2017 / Published online: 2 March 2017 Akade´miai Kiado´, Budapest, Hungary 2017

Abstract

The enhancement of thermal conductivities of water in the presence of copper oxide and multiwalled carbon nanotubes is investigated for the first time. Hybrid nanofluid is a homogenous mixture of multiwalled carbon nanotubes-CuO particles suspended in water as the base fluid. The thermal conductivity of mixture is measured by KD2 Pro instrument. All thermal conductivity measurements are repeated three times in the range of 25–50

C. A

hot water bath is used to stabilize the temperature at 25, 30, 35, 40, 45 and 50

C during the measurements. The results

show that the thermal conductivity of the nanofluid increases at more solid concentration. Furthermore, the thermal conductivity of the nanofluid increases with the temperature; however, this increase is by far more notice- able in higher solid concentrations compared with the lower ones. Moreover, it is tried to propose a new correlation for predicting the thermal conductivity of the present nanofluid at different temperatures and volume fractions.

The highest enhancement percentage was observed as 30.38% for the state of

T=

50

C andu=

0.6%. How- ever, the enhancement percentages were achieved as 25.57–30.38 for the state of

u=

0.6% at

T=

25–50

C,

respectively.

Keywords

Hybrid nanofluid Thermal conductivity measurement New correlation

Introduction

In the industrial sectors, heat must be transferred either to insert energy into the system or to remove the energy produced in the system. Due to the rapid increase in energy demand worldwide, the intensifying heat transfer process has become an important task. Cooling and heating are the demanding challenges in some high heat flux systems. The fluid medium has the ability to transfer a large quantity of heat across small temperature difference, which improves the performance of heat transfer devices. Nanofluid is a novel heat transfer fluid prepared by dispersing nanometer- sized solid particles in traditional heat transfer fluids such as water or ethylene glycol to increase thermal conductivity and therefore heat transfer performance. Several methods of enhancing the heat transfer are used in heating and cooling systems, but using the nanofluids in industrial applications has a better heat transfer rate [1–8].

In another experimental study, the thermal conductivity of MgO/EG nanofluid with different sizes of particles (20, 40, 50 and 60 nm) was measured by Esfe et al. [9] and Yabuki et al. [10]. They applied a two-step sintering method to a copper layer formed from a copper ink con- taining 20-nm copper nanoparticles and covered by a thin carbon layer and proposed the use of gelatin as a stabilizer and antioxidation reagent for copper nanoparticles, so that the fine particles were prepared by a chemical reduction of copper (II) oxide (CuO) and a copper salt. CuO is a solid copper source containing oxygen as the only counter-anion.

It represents the ideal metal source candidate for copper fine particles used in conductive inks and pastes [11,

12].

Thermal conductivity of nanofluid using KD2 Pro was measured by Chandrasekar et al. [13] who proposed a new model to predict the thermal conductivity for Al

₂

O

₃

/water nanofluid. Chein and Chuang [14] conducted an

& Davood Toghraie

[email protected]

1 Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

2 Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran DOI 10.1007/s10973-017-6213-8

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experimental study on the heat transfer and hydraulic performance of CuO/water mixture without using a dis- persion agent inside a silicon wafer microchannel heat sink.

They studied 0.2 and 0.4% nanoparticles volume fractions and observed that nanofluids absorbed more dissipated energy and offered lower wall temperature than the base fluid for the low fluid flow rates (20 mL min

^-1

). For the higher flow cases, the agglomeration and deposition clearly penalized the heat improvement, although the agglomeration could be prevented by using a higher bulk temperature, while only a slight increase in the pressure drop was observed. More works can be addressed in this way, such as Soltanimehr and Afrand, Zarringhalam et al., Karimipour et al., Afrand et al., Esfe et al., Akbar et al., Zeeshan et al. and Sheikholeslami et al. [15–34].

A great number of researches have been conducted on different nanofluids from different aspects [35–41]. These particles included different types of oxides such as copper oxide [42–44], aluminum oxide [45–47], titanium oxide [48,

49]. Choi et al. [50] reported 150% increase in thermal

conductivity of poly(a-olefin) by adding 1% volume fraction of MWCNT. Similarly, Yang et al. [51] reported 200%

increase in thermal conductivity by adding 0.35% multi- walled carbon nanotube. Esfe et al. [52] investigated aquatic nanofluid of carbon nanotube and examined the thermophysical properties and pressure drop in two-tube transformers. Amiri et al. [53] produced a stable nanofluid by the synthesis of carbon nanotubes decorated by silver particles and examined its thermophysical properties. The heat transfer of MWCNT/oil nanofluid inside horizontal

1000 900 800 700 600 500 400 300 200 100 0

Lin (counts)

10 20 30 40 50 60 70 80 90

2-theta - Scale (a)

(b)

Fig. 1 aXRD pattern of CuO nanoparticles.bTEM image of CuO nanoparticles

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flattened tubes was performed by Ashtiani et al. [54].

Baghbanzadeh et al. [55,

56] examined the thermal con-

ductivity and viscosity of hybrid MWCNT/SiO2 nanofluid.

Also, Chen and Xie [57] examined the effect of PH on zeta potential of one- and two-wall nanofluids and measured their thermal conductivity. In the present study, the nanofluid thermal conductivity composed of MWCNT-CuO/

water is examined experimentally. To the author’s knowledge, there is no comprehensive and thorough investigation to predict the thermal conductivity of the supposed nanofluid.

Experimental

Thermal conductivity measurement

In the present study, the thermal conductivity of hybrid nanofluid of multiwalled carbon nanotubes (MWCNT)-copper oxide (CuO) in water at different solid concentrations (0.05%

up to 0.6%) and temperatures (25

C up to 50C) is evaluated.

Thermal conductivity of nanofluid at different solid volume fractions and temperatures is measured using a KD2 Pro instrument manufactured by Decagon Devices, USA. The KD2 Pro measures thermal conductivity based on the transient hot wire technique. In this method, a KS-1 sensor is used for measuring the thermal conductivity. It is 60 mm long and is of 1.27 mm diameter and is made of stainless steel. KD2 has the maximum error of

±5%. A hot water bath is used in order to

stabilize the temperature; moreover, a thermometer which has an accuracy of 0.1

C is used for measuring the temperature.

All thermal conductivity measurements are repeated three times. An accurate hot water is used to stabilize the temperature at 25, 30, 35, 40, 45 and 50

C during the mea-

surements. Validation for the results accuracy was done before the analysis of the nanofluid, which meant the sensor needle was calibrated by sensing the thermal conductivity of glycerin, ethylene glycol and pure water. The results were in good agreement with those in the literature with maximum deviation of 5%. The figures of relative thermal conductivity as well as the diagrams of thermal conductivity enhancement percentage are presented.

Thermal conductivity ratio

¼knf=kbf ð1Þ

Thermal conductivity enhancement percentage

ð Þ%

¼ðknfkbfÞ=kbf

100

ð2Þ

Prepare the samples

The two-step method is employed to prepare the samples, which means dry multiwalled carbon nanotubes (MWCNT) and copper oxide (CuO) are mixed with an equal volume.

The achieved mixture is dispersed in water at different solid volume fractions as 0.05, 0.1, 0.15, 0.2, 0.4 and 0.6%. To attain a suitable dispersion, after magnetic stirring for 2 h, each sample is exposed to an ultrasonic processor with the power of 400 W and frequency of 24 kHz for 6 h. All samples have a good stability, and no sedimentation is observed in the long time before the experiments. The structural properties and morphology of MWCNT and copper oxide nanoparticles are measured using X-ray diffraction (XRD) and transmission electron microscope (TEM) as shown in Figs.

1

and

2,

respectively. The average size of nanoparticles is achieved by applying XRD image (bruker-D8 Germany) and Debye–

Scherrer equation d

=

0.89k/(bcosh), where d shows the particle diameter size,

k

represents the wave length of X-ray (0.1541),

b

implies the full width at half maximum, and finally,

h

illustrates the Bragg’s angle.

Results and discussion

The two-step method is employed to prepare the samples.

In this way, multiwalled carbon nanotubes and copper oxide nanoparticles are mixed with deionized water. This

150

100

50

0

Lin (counts)

20 40 60 80

2θ scale (a)

(b)

Fig. 2 a XRD pattern of multiwalled carbon nanotubes. b TEM image of multiwalled carbon nanotubes

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combination is dispersed in water with solid volume frac- tions of 0.05, 0.1, 0.15, 0.2, 0.4 and 0.6%. The properties of MWCNTs and CuO nanoparticles are presented in Tables

1

and

2, respectively.

Figure

3

depicts the thermal conductivity of the hybrid nanofluid against temperature at different solid volume fractions. It can be observed that the thermal conductivity of the hybrid nanofluid considerably enhances with rising temperature and solid volume fraction.

Table 3 Thermal conductivity enhancement percentage for different amounts of temperature at various volume fractions

u T/C

25 30 35 40 45 50

0.05 7.3 7.8 8.22 8.5 8.9 9.61

0.1 10.65 11.54 11.77 12.06 12.96 13.17

0.15 13.11 13.98 14.51 14.92 15.31 16.43

0.2 15.57 16.74 17.42 18.25 18.6 19.38

0.4 20.65 21.78 23.06 23.8 24.84 25.42

0.6 25.57 26.5 27.25 28.73 29.68 30.38

0.640 0.68 0.72 0.76 0.8 0.84 0.88

0.1 0.2 0.3 0.4 0.5 0.6

Solid volume fraction/%

Thermal conductivity/Wm–1K–1

T = 25°C T = 30°C T = 35°C T = 40°C T = 45°C T = 50°C

Fig. 5 Hybrid nanofluid thermal conductivity versus solid volume fraction at different temperatures

10 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

0.1 0.2 0.3 0.4 0.5 0.6

T = 25°C T = 30°C T = 35°C T = 40°C T = 45°C T = 50°C

Fig. 4 Thermal conductivity ratio of nanofluid with respect to solid volume fractions at different temperatures

20 25 30 35 40 45 50 55

Temperature/°C 0.88

0.84

0.8

0.76

0.72

0.68

0.64 Thermal conductivity/Wm–1K–1

ϕ = 0.05 ϕ = 0.1 ϕ = 0.15 ϕ = 0.2 ϕ = 0.4 ϕ = 0.6

Fig. 3 Thermal conductivity of nanofluid versus temperature at different concentrations

Table 2 Properties of copper oxide

Parameter Value

Purity \99.9/%

Specific heat 550.5/J kg^-1K^-1

Color Black

Particle size 30–50/nm

Specific surface area 20/m²g^-1

Thermal conductivity 32.9/Wm^-1K^-1

True density *6320/kg m^-3

Table 1 Properties of MWCNT

Parameter Value

Purity \98/%

Content of -COOH 2.56/mass%

Color Black

Outer diameter 5–15/nm

Inner diameter 3–5/nm

Length *50/lm

Thermal conductivity 1500–3000/Wm^-1K^-1

True density *2100/kg m^-3

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Experimental (T = 25°C) Correlation (T = 25°C)

0 0.1 0.2 0.3 0.4 0.5 0.6

1 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

0 0.1 0.2 0.3 0.4 0.5 0.6

1 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

0 0.1 0.2 0.3 0.4 0.5 0.6

1 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

0 0.1 0.2 0.3 0.4 0.5 0.6

1 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

0 0.1 0.2 0.3 0.4 0.5 0.6

1 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

0 0.1 0.2 0.3 0.4 0.5 0.6

1 1.04 1.08 1.12 1.16 1.2 1.24 1.28 1.32 1.36 1.4 1.44

Fig. 6 Comparisons of thermal conductivity ratio between experimental data and correlation outputs for various temperatures versus volume fraction

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To show the results better, the nanofluid thermal con- ductivity ratio versus solid volume fraction for various temperatures is presented in Fig.

4. This figure also implies

the positive effects of

u

and

T

on thermal conductivity;

however, the effects of temperature are more sensible at higher values of volume faction.

Figures

3

and

4

also show that there is a parallel trend for each solid volume fraction and temperature. The main cause of improving of thermal conductivity due to the temperature rising may be described by the augmentation of interactions between the nanoparticles and Brownian motion. Moreover, at higher solid volume fractions, the number of suspended nanoparticles is higher. It may lead to enhancement of the ratio of surface to volume and collisions between particles. In fact, in the presence of large amounts of particles, the effect of temperature on motion of the particles is more tangible.

In order to investigate more accuracy, thermal conduc- tivity enhancement percentage for different amounts of temperature at various volume fractions is presented in Table

3.

As said, thermal conductivity can be increased by more temperature and solid nanoparticles Brownian motions.

Apart from higher collisions between the particles, more nanoparticles can create clusters of particles in the fluid, which corresponds to larger thermal conductivity. The highest enhancement percentage is observed as 30.38% for the state of

T=

50

C and u=

0.6%. However, the enhancement percentages are achieved as 25.57–30.38 for the state of

u=

0.6% at

T=

25–50

C, respectively.

For

u=

0.05% to

u=

0.6%, the enhancement per- centage of the supposed hybrid nanofluid thermal con- ductivity changes from 7.3 to 25.57 at

T=

25

C, while

this trend occurs from 9.61 to 30.38 at

T=

50

C (see

Table

3).

Figure

5

illustrates the thermal conductivity versus solid volume fraction at different temperatures. This figure shows the results of Fig.

3

in another point of view, which can be useful to better sense the effects of both temperature and volume fraction.

Propose a correlation

The nanofluid thermal conductivity composed of MWCNT/

CuO nanoparticles suspended in water from 25 to 50

C for

various solid volume fractions of 0.05, 0.1, 0.15, 0.2, 0.4 and 0.6% was examined. Experimental results revealed that the thermal conductivity was enhanced by more solid volume fraction and temperature. It was also seen that the variations of thermal conductivity ratio with solid volume

fraction at higher temperatures were more than the state of lower temperatures.

As a result, an empirical correlation is developed to predict the thermal conductivity of present hybrid nano- fluid according to the new experimental achievements of this work. This correlation, expressed in Eq. (3), is a simple power-multiplicative function of temperature and solid volume fraction. It has a very high accuracy and is valid for the temperature range of 25–50

C and volume

concentrations range from 0.05 to 0.6%.

knf

kbf

¼

0:907 exp 0:36/

^0:3111þ

0:000956T

ð3Þ

where

k_nf

and

k_bf

are the thermal conductivity of nanofluid and base fluid, respectively. Moreover,

T

is the temperature of nanofluid in

C and u

represents the solid volume fraction in volumetric percentage. Comparisons of thermal conductivity ratio between present experimental achievements and correlation outputs at various temperatures versus volume fraction are illustrated in Fig.

6

to validate the accuracy of the developed correlation.

Appropriate agreements are observed in the diagrams in this figure.

Conclusions

Thermal conductivity of hybrid nanofluid composed of multiwalled carbon nanotubes-copper oxide (MWCNT- CuO) dispersed in water at different solid volume fractions (0.05, 0.1, 0.15, 0.2, 0.4 and 0.6%) and temperatures (25, 30, 35, 40, 45 and 50

C) was experimentally investigated

by using the KD2 Pro instrument for the first time. All thermal conductivity measurements were repeated three times. A hot water bath with 0.1

C accuracy was used to

stabilize the temperature during the measurements.

Results showed that more temperature and volume fraction corresponded to more thermal conductivity.

However, the effect of temperature at higher solid volume

fraction was more significant. The highest enhancement

percentage was observed as 30.38% for the state of

T=

50

C and u=

0.6%. However, the enhancement

percentages were achieved as 25.57 to 30.38 for the state of

u=

0.6% at

T =

25–50

C, respectively. More focus and

for

u=

0.05–0.6%, the enhancement percentage of the

supposed hybrid nanofluid thermal conductivity changed

from 7.3 to 25.57 at

T =

25

C, while this trend occurred

from 9.61 to 30.38 at

T=

50

C. The extension of this

paper for nanofluid according to our previous works

[58–84] affords engineers a good option for nanoscale and

microscale simulation.

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56. Baghbanzadeh M, Rashidi A, Rashtchian D, Lotfi R, Amrollahi A. Synthesis of spherical silica/multiwall carbon nanotubes hybrid nanostructures and investigation of thermal conductivity of related nanofluids. Thermochim Acta. 2012;549:87–94.

57. Chen L, Xie H. Surfactant-free nanofluids containing double-and single-walled carbon nanotubes functionalized by a wet- mechanochemical reaction. Thermochim Acta. 2010;497:67–71.

58. Afrand M, Toghraie D, Ruhani B. Effects of temperature and nanoparticles concentration on rheological behavior of Fe₃O₄– Ag/EG hybrid nanofluid: an experimental study. Exp Therm Fluid Sci. 2016;77:38–44.

59. Esfe MH, Yan WM, Afrand M, Sarraf M, Toghraie D, Dahari M.

Estimation of thermal conductivity of Al₂O₃/water (40%)—ethylene-glycol (60%) by artificial neural network and correlation using experimental data. Int Commun Heat Mass Transf. 2016;74:125–8.

60. Toghraie D, Chaharsoghi VA, Afrand M. Measurement of thermal conductivity of ZnO–TiO₂/EG hybrid nanofluid. J Therm Anal Calorim. 2016. Doi:10.1007/s10973-016-5436-4.

61. Semiromi DT, Azimian A. Molecular dynamics simulation of annular flow boiling with the modified Lennard-Jones potential function. Heat Mass Transf. 2012;48:141–52.

62. Toghraie D, Alempour SMB, Afrand M. Experimental determination of viscosity of Water based magnetite nanofluid for application in heating and cooling systems. J Magn Magn Mater.

2016;417:243–8.

63. Esfe MH , Saedodin S, Wongwises S, Toghraie D. An experimental study on the effect of diameter on thermal conductivity and dynamic viscosity of Fe/water nanofluids. Therm Anal.

doi:10.1007/s10973-014-4328-8.

64. Esfe MH, Afrand M, Gharehkhani S, Rostamiand H, Toghraie D, Dahari M. An experimental study on viscosity of alumina-engine oil: effects of temperature and nanoparticles concentration. Int Commun Heat Mass Transfer. 2016;76:202–8.

65. Hemmat Esfe M, Afrand M, Yan WM, Yarmand H, Toghraie D, Dahari M. Effects of temperature and concentration on rheological behavior of MWCNTs/SiO₂ (20–80)-SAE40 hybrid nano- lubricant. Int Commun Heat Mass Transfer. 2016;76:133–8.

66. Esfe MH, Hassani Ahangar MR, Rejvani M, Toghraie D, Haj- mohammad MH. Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO₂ using experimental data. Int Commun Heat Mass Transfer. 2016;75:192–6.

67. Afrand M, Toghraie D, Sina N. Experimental study on thermal conductivity of water-based Fe₃O₄nanofluid: development of a new correlation and modeled by artificial neural network. Int Commun Heat Mass Transf. 2016;75:262–9.

68. Afrand M, Sina N, Teimouri H, Mazaheri A, Safaei MR, Hemmat Esfe M, Kamali J, Toghraie D. Effect of magnetic field on free convection in inclined cylindrical annulus containing molten potassium. Int J Appl Mech. 2015;7(04):1550052.

69. Noorian H, Toghraie D, Azimian AR. Molecular dynamics simulation of Poiseuille flow in a rough nano channel with checker surface roughnesses geometry. Heat Mass Transf. 2014;50(1):105–13.

70. Karimipour A, Alipour H, Akbari OA, Semiromi DT, Esfe MH.

Studying the effect of indentation on flow parameters and slow heat transfer of water-silver nano-fluid with varying volume fraction in a rectangular two-dimensional micro channel. Indian J Sci Technol. 2015;8:1–15.

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71. Akbari OA, Toghraie D, Karimipour A. Impact of ribs on flow parameters and laminar heat transfer of Water-Aluminum oxide nanofluid with different nanoparticle volume fractions in a three- dimensional rectangular microchannel. Adv Mech Eng.

2015;7(11):1–11.

72. Akbari OA, Karimipour A, Toghraie Semiromi D, Safaei MR, Alipour H, Goodarzi Ma, Dahari M. Investigation of rib’s height effect on heat transfer and flow parameters of laminar water–

Al₂O₃ nanofluid in a two dimensional rib-microchannel. Appl Math Comp. 2016;290:135–53.

73. Akbari OA, Toghraie D, Karimipour A. Numerical simulation of heat transfer and turbulent flow of Water nanofluids copper oxide in rectangular microchannel with semi attached rib. Adv Mech Eng. 2016;8(4):1–25.

74. Alipour H, Karimipour A, Safaei MR, Semiromi DT, Akbari OA.

Influence of T-semi attached rib on turbulent flow and heat transfer parameters of a silver–water nanofluid with different volume fractions in a three-dimensional trapezoidal microchannel. Phys E Low-Dimens Syst Nanostruct. 2017;88:60–76.

75. Nazari S, Toghraie D. Numerical simulation of heat transfer and fluid flow of water–CuO nanofluid in a sinusoidal channel with a porous medium. Phys E Low-Dimens Syst Nanostruct.

2017;87:134–40.

76. Sajadifar SA, Karimipour A, Toghraie D. Fluid flow and heat transfer of non-Newtonian nanofluid in a microtube considering slip velocity and temperature jump boundary conditions. Eur J Mech-B/Fluids. 2017;61:25–32.

77. Aghanajafi A, Toghraie D, Mehmandoust B. Numerical simulation of laminar forced convection of water–CuO nanofluid inside a triangular duct. Phys E Low-Dimens Syst Nanostruct.

2017;85:103–8.

78. Afrand M, Toghraie D, Karimipour A, Wongwises S. A numerical study of natural convection in a vertical annulus filled with gallium in the presence of magnetic field. J Magn Magn Mater.

2017;430:22–8.

79. Toghraie D, Mokhtari M, Afrand M. Molecular dynamic simulation of copper and platinum nanoparticles Poiseuille flow in a nanochannels. Phys E Low-Dimens Syst Nanostruct. 2016;84:

152–61.

80. Semiromi DT, Azimian AR. Nanoscale Poiseuille flow and effects of modified Lennard-Jones potential function. Heat Mass Transf. 2010;46:791–801.

81. Semironi DT, Azimian AR. Molecular dynamics simulation of liquid–vapor phase equilibrium by using the modified Lennard- Jones potential function. Heat Mass Transf. 2010;46:287–94.

82. Semiromi DT, Azimian AR. Molecular dynamics simulation of nonodroplets with the modified Lennard-Jones potential function.

Heat Mass Transf. 2011;47:579–88.

83. Faridzadeh MR, Semiromi DT, Niroomand A. Analysis of laminar mixed convection in an inclined square lid-driven cavity with a nanofluid by using an artificial neural network. Heat Transf Res.

2014;45:1–20.

84. Afrand M, Sina N, Teimouri H, Mazaheri A, Safaei MR, Esfe HM, Kamali J, Toghraie D. Effect of magnetic field on free convection in inclined cylindrical annulus containing molten potassium. Int J Appl Mech. 2015;7:1550052.