graphene oxide nanofluid in a copper tube under air cross-flow: Applicable as a heat exchanger

Accepted Manuscript An experimental study on heat transfer and pressure drop of water/graphene oxide nanofluid in a copper tube under air cross-flow: Applicable as a heat exchanger Ramin Ranjbarzadeh, A.H. Meghdadi Isfahani, Masoud Afrand, Arash Karimipour, Mohammad Hojaji PII: DOI: Reference:

S1359-4311(17)31028-1 http://dx.doi.org/10.1016/j.applthermaleng.2017.06.110 ATE 10629

To appear in:

Applied Thermal Engineering

Received Date: Revised Date: Accepted Date:

14 February 2017 10 June 2017 21 June 2017

Please cite this article as: R. Ranjbarzadeh, A.H. Meghdadi Isfahani, M. Afrand, A. Karimipour, M. Hojaji, An experimental study on heat transfer and pressure drop of water/graphene oxide nanofluid in a copper tube under air cross-flow: Applicable as a heat exchanger, Applied Thermal Engineering (2017), doi: http://dx.doi.org/10.1016/ j.applthermaleng.2017.06.110

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An experimental study on heat transfer and pressure drop of water/graphene oxide nanofluid in a copper tube under air cross-flow: Applicable as a heat exchanger Ramin Ranjbarzadeh1, A.H. Meghdadi Isfahani2,3, Masoud Afrand2,3,*, Arash Karimipour2,3, Mohammad Hojaji2,3 1- Young Researchers and Elite club, Najafabad Branch, Islamic Azad University, Najafabad, Iran 2-Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran 3-Modern Manufacturing Technologies Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

* Corresponding author Emails: [email protected] & [email protected]

Abstract The effect of using water/graphene oxide nanofluid as a working fluid on heat transfer and pressure drop was studied experimentally. For this purpose, subsonic wind tunnel and a closed heat transfer cycle were used at the same time. The effect of different concentrations (0, 0.05, 0.1, 0.2% by volume) of water/graphene oxide nanofluid at different Reynolds numbers in a tube under air crossflow was evaluated in wind tunnel tests. The range of Reynolds number of the flow around the tube was 3800  Re o  21500 . The friction factor of the nanofluid flow inside the tube and the mean Nusselt number of the external air flow around the copper tube were calculated by measuring the variables. Results showed that by using water/graphene oxide nanofluid, the average Nusselt number enhanced by up to 51.4% compared to pure water. The use of nanofluid increased the friction factor by 21% in comparison with pure water. Because of changes in heat transfer and friction factor, heat transfer performance coefficient increased by up to 42.2%, indicating enhanced heat transfer compared to undesirable pressure drop in the test. According to the results, this nanofluid can be a good alternative in similar applications such as heat exchangers. Keywords: Experimental study; Air cross-flow heat transfer; Water/graphene oxide nanofluid; Pressure drop; Copper tube;

Nomenclature A

Greek symbols

Tube cross section area, m2

T

Temperature difference, K

Cp

Specific heat capacity, Jkg K

p

Pressure difference, Pa

D

Diameter of the tube, m



Volumetric concentration of nanoparticles, %

f

Friction factor



Dynamic viscosity, kgm-1s-1

Convective heat transfer coefficient of air, Wm-2K-1



Kinematics viscosity, m2s-1

k

Thermal conductivity, Wm-1K-1



L

Length of the tube, m

m

Mass flow rate, kgs-1

h Air

-1

-1

Nu Air Air flow average Nusselt number

Density, kgm-3

Subscripts b

Bulk

Pr

Prandtl number

bf

Base fluid

q

Heat transfer, W

in

Inlet

UR

Uncertainty, %

nf

Nanofluid

Rei

Internal flow Reynolds number

np

Nanoparticles

Reo

External flow Reynolds number

out

Outlet

T

Temperature, K

pt

Plain tube

u

Mean fluid velocity, m2s-1

w

Wall

wt

Weight percentage, %

Q

Volumetric flow Dubai, m3s-1

HTP

Heat transfer performance

1.

Introduction

Flow and heat transfer around tubes are important for use in thermal systems such as air-cooled heat exchangers [1], car engine cooling system [2], cooling towers [3], air conditioning systems [4] and other thermal applications [5-7]. Considering these applications and the wide range of application of air-cooled heat exchangers, which are the most widely-used type of heat transfer equipment in the industry, enhancement of heat transfer has become of interest for scientists in recent years. In this regard, several approaches have been proposed including the application of magnetic fields [8], use of mechanical baffles [9], use of extended surfaces [10], and addition of solid particles to fluid [11]. Extensive researches have been done to improve heat transfer around a tube under cross-flow of air. A few of such methods are reviewed in the following. Erek et al. [12] used numerical methods to

study the effect of fin geometry on heat transfer and pressure drop of external flow in finned tube heat exchanger. They reported that the use of fins with an elliptical cross-section improves heat transfer and pressure drop. The results also showed that increased large-to-small diameter ratio of the ellipse increases the heat transfer and reduces pressure drop in the heat exchanger. Nouri Boroujerdi and Lavasan [13] were the first to experimentally examine flow and heat transfer around a cam-shaped tube in cross-flow, aiming to improve heat transfer in external flows. Hot water flow had a constant temperature and flow rate within the tube. The effects of Reynolds number in the range of 1.5 *10 4  Re eq  2.7 *10 4 and the angle of attack of the flow with tube within the range of 0    180 were examined. Results showed that the maximum average heat transfer coefficient

occurs at 90 degrees angle of attack. The coefficient of thermal performance of cam tube was found to be greater than the circular tube with a similar surface except for 90 and 120 degrees angles of attack. Toolthaisong and Kasayapanand [14] empirically investigated the effect of the angle of attack in the air flow crossing the flat tubes with various cross sections on heat transfer. Inlet water temperature was 75oC. Their results showed that increasing the angle of attack from zero to 90 degrees increases the heat transfer and reduces the air hydrodynamic performance. The best hydrodynamic performance was achieved at an angle of zero. A method that has been used recently is the use of nanofluids in heating systems. In 1995, Choi [15] introduced nanofluids, creating a major revolution in fluid heat transfer applications. The new look was in fact introduced to fluid suspension with solid particles in the nanoscale. Nanofluids are produced by the uniform distribution of the nanoparticles in a base fluid. The use of nanofluids in this study aims at improving heat transfer in the cross-flow heat transfer system. In fact, the optimal thermo-physical properties of nanofluids, in addition to solving the energy problems, has made them an alternative fluid which advances heating systems, reduces the heat exchanger size, improves efficiency, reduces fuel consumption, and leads to cost savings [16, 17].

Due to their high thermal conductivity, nanofluids enhance the heat transfer rate and are regarded as a new generation of fluids that have great potential in industrial applications. Table 1 lists the results of empirical researches conducted on the high thermal potential of nanofluids.

Table 1. Maximum values of thermal conductivity enhancement for different nanofluids reported in previous works Ref. Number

Nanomaterial

Base fluid

Temperature o C

Concentration

Thermal conductivity enhancement %

[18]

SWCNT

Water

50

0.25 wt%

36.5

[19]

Al2O3

Water/EG

50

0.8 vol.%

17.89

[20]

Graphite

EG

65

0.4 vol.%

27

[21]

Cuo

Water

-

0.01 vol.%

53

[22]

MgO

Water/EG

50

3 vol.%

57

[23]

Graphene oxide

Water

60

0. 5 wt%

56

[24]

Graphene

Water

60

0. 5 wt%

32

[25]

Fe3O4

Water/EG

55

3 vol.%

88

[26]

MWCNT

Water/EG

50

1 vol.%

34.7

Water

40

4 vol.%

45

EG

60

5 vol.%

86

[27] [28]

Reduced graphene oxide Graphene

[29]

SWCNTs

EG

50

0.65 vol.%

54

[30]

MWCNTsMgO

EG

25

0.6 vol.%

21.3

Given the high thermal potential of nanofluids, extensive researches have been conducted to evaluate the performance of nanofluids in applied equipment [31-36]. In this regard, Pak and Cho [37] studied forced convective heat transfer in nanofluids. They experimentally investigated nanofluids convective heat transfer in turbulent flow and found that Nusselt number increases with increasing the volume concentration of the nanoparticles and the Reynolds number. Hemmat Esfe et al. [38] empirically examined the effect of water/double-walled carbon nanotubes nanofluids on heat transfer and pressure drop in a double-tube heat exchanger. They used turbulent nanofluid flow

as a coolant in the heat exchanger. Their results showed that the convective heat transfer rate of a nanofluid with a concentration of 0.4% was greater by 32% compared to pure water flows. The maximum pressure loss was 20%. Ebrahimian et al. [39] numerically and experimentally investigated the effects of titanium oxide nanofluid flow in practical applications. They described the use of nanofluids in solar systems as an innovative approach to enhancing heat transfer. In their research, the heat transfer coefficient of nanofluids flow increased by 21%. Amrollahi et al. [40] examined the convective heat transfer in water/multi-walled carbon nanotubes nanofluid flow in both laminar and turbulent regimes in a horizontal tube with constant heat flux boundary condition. They showed that the maximum heat transfer coefficient was increased by 40% at the 0.25% nanofluid weight. Sadeghinezhad et al. [41] examined heat transfer and pressure drop in turbulent water/graphene nanofluid in a two-tube heat exchanger with constant heat flux. Their results showed that convective heat transfer coefficient increased by 13-160%. The maximum heat transfer performance coefficient was reported as 1.77. In another experimental study, convective heat transfer of water/graphene nanofluid was investigated by Mehr-Ali et al. [42]. Their results showed that the thermal performance coefficient of the nanofluid reached 1.15 in the horizontal tube. Based on the studies mentioned above, the main characteristics of nanofluids are increased heat transfer coefficient [43, 44], higher critical heat flux [45, 46], and better thermal energy storage performance [47, 48]. On the other hand, Because of the unique properties of water/graphene oxide nanofluids, using this nanofluid instead of water in air-cooling or air-heating systems is recommended. Thus, a novel system has been designed and constructed to test the flow of graphene oxide/water nanofluids. The water/graphene oxide nanofluid has been stabilized with different volume fractions. The nanofluid samples were then used as heat transfer fluid in a cross-flow heat exchanger. Given the high heat transfer potential of graphene oxide nanosheets, average heat transfer coefficient is predicted to increase in the air flow around the tube. Due to limited water resources and the growing need for energy, it seems that the use of water/graphene oxide nanofluids in thermal systems can be responsive to the needs of various industries. The aim of this study is

increasing the heat exchange between the tube and the air flow using water/graphene oxide. This feature can be very useful in enhancing the heat transfer process.

2.

Problem definition and research objectives

The experiment defined in this study is a simulation of an air-cooled heat exchanger. For this purpose, a copper tube with a circular cross-section under air cross-flow was considered (Fig. 1). Reynolds numbers for air flow around the tube, which is established in the test section of a subsonic wind tunnel, were 3800, 9200, 14500, 19900 and 21500. The range of Reynolds numbers for the nanofluid flow inside the tube that is established by a circulation pump were between 550 and 3250. The effect of nanofluid concentration was examined by volumetric concentrations of 0, 0.05, 0.1 and 0.2%. The main purpose of the experiments was to analyze heat transfer in the air flow around the copper tube and the undesirable pressure drop in the nanofluid flow inside the tube. Results are reported in the form of Nusselt number and friction factor. By defining the heat transfer performance coefficient (HTP), the changes in Nusselt number and friction factor were evaluated as a dimensionless number.

3.

Methods

3.1. Experimental setup As mentioned earlier, in this study, a free air flow and a closed heat transfer cycle are required. To provide closed heat transfer cycle, the nanofluid temperature inside the tank was increased to the designed temperature using a heating element, and the nanofluid was then circulated in the closed heat transfer cycle (inside the copper tube) using a pump. During the experiment, temperature and pressure values in the inlet and outlet of copper tubes were recorded. A subsonic wind tunnel has been used to make free air flow. The air flow temperature at the wind tunnel inlet was measured, and the affected parameters were evaluated.

1. Inlet sir flow 2. Test section

6. Digital differential digital manometer 7. Hot water circulation pump

11. Hot water tank 12. Heating element

3. Copper tube

8. Bypass valve

13. K Type thermocouple

4. Fan

9. Flow control valve

5. Variable frequency drives

10. Flow meter

Fig. 1. Schematic of experimental setup

The combination of these systems, shown in Figs. 1 and 2, was used to study the nanofluid heat transfer and pressure drop in the tube under air cross-flow. The closed heat transfer cycle was designed and built to circulate the nanofluid in the copper tube. The system consists of different parts, such as nanofluid storage tank with an insulated body, a pump (Taifo, GRS 25/6), glass flowmeter, copper tube with an outer diameter of 19 mm, differential pressure gauge model 9102 PM, and a number of K-type thermocouples along with a thermometer. In order to prevent heat loss along the path before the test tube, connection tubes were completely insulated. The designed measurement system is capable of automatically controlling, measuring and recording the

temperature values. The use of this system controls the temperature fluctuations in copper tube inlet, reducing the error due to the changes in the temperature of the nanofluid entering the tubes during experiments.

Fig. 2. Image of experimental setup

3.2. Production and stabilization of nanofluid In this work, homogeneous nanofluid samples were prepared by suspending graphene oxide nanosheets in water. Graphene oxide nanosheets used in the experiments were produced by US Research Nanomaterials Inc. In order to ensure the structure of the nanoparticles, their accuracy was investigated using Scanning Electron Microscope (SEM) and X-Ray Diffraction. The resulting images are shown in Fig. 3. The figure shows that the test results are acceptably consistent with the reported values. Thermo-physical properties of graphene oxide nanosheets are listed in Table 2.

Fig. 3. SEM (left) and X-Ray Diffraction (right) for graphene oxide nanosheets

Table 2. Thermo-physical properties of graphene oxide nanosheets. Thermo-physical property

Value

Appearance/ Morphology Diameter (µm) Purity (%) density (g/cm3) Thickness (nm) Specific surface area (m2/g) Thermal conductivity (parallel to surface) (W/m K) Thermal conductivity (perpendicular to surface) (W/m K)

Black powder/ nanosheets 2 99 1 3.4-7 nm with 6-10 Layers 400-600 2500 5

Chemical, mechanical and ultrasonic processes were used to achieve uniform distribution of nanoparticles and break the aggregates in the base fluid, respectively. In order to make stable nanofluids, the parameters governing the stability of nanofluid, such as pH of the base fluid, duration of the use of magnetic stirrer as well as the duration of ultrasonic process to produce and stabilize the nanofluid, were investigated and the optimum values were determined. To produce seven liters of water/graphene oxide nanofluid, the optimum base fluid pH was first determined, and nanoparticles were gradually added to the base fluid while the magnetic stirrer was on. After a period of one hour and thorough mixing of nanoparticles in the base fluid, the resulting suspension was exposed to ultrasonic bath model PARSONIC 30S with 400 watts power and a frequency of 28 kHz for 4 hours to create a uniform and stable suspension. It should be noted that functional groups

on nanoparticles can lead to their hydrophilicity. Therefore, any surfactant has been used for the production of nanofluids. Accordingly, Fourier transform infrared spectroscopy (FTIR) was used to ensure the presence of functional groups on graphene oxide nanosheets. The FTIR spectrum of graphene oxide is shown in Fig. 4. According to Fig. 4, the peaks of 1218 cm-1 and 1045 cm-1 are related to single bands of C-O. Moreover, the peak of 3392 cm-1 is related to hydroxide group of – OH. In addition, the peak of 1716 cm-1 is related to the stretching band of C=O, which confirmed that the presence of hydroxyl groups of –COOH. According to the results, graphene oxide nanosheets in water cause the formation of hydrogen bands and thus provide high stability in nanofluids. 60

40

10

0

(C=O) 3392-------

20

1716------

30

1045-----1218------

Transmittance (%)

50

(C-O) (C-O)

1000

1500

2000

2500

3000

(-OH)

3500

4000

-1

Wavelength (cm ) Fig. 4. FTIR spectrum of graphene oxide

The sedimentation photograph capturing was used to investigate the stability of the nanofluid. The zeta potential test was then used to further examine its stability. The test was conducted using DLS model Nano ZS (red badge) ZEN 3600. Results are presented in Fig. 5.

Fig. 5. Zeta potential test related to nanofluid stability in different pH

Considering the absolute value of the zeta potential in water/graphene oxide nanofluid, the stability was very good at pH = 9 with   41 . Its long-term stability stabilized the thermophysical properties of the nanofluid. Stability can be mainly attributed to functional groups in graphene oxide nanosheets, lack of density difference with the base fluid, nano-size of nanoparticles in the base fluid, and the determination of optimal pH, which was studied and determined experimentally here.

4.

Governing equations

One of the best ways to evaluate the thermal performance of air flow in a cross-flow heat transfer system is calculating the Nusselt number as the convective heat transfer is the dominant mechanism. The amount of heat loss by the nanofluid while passing the tube is calculated using Eq. (1).  c p (Tin  Tout ) qm

(1)



In the above equation, m , C p , Tin , and Tout are fluid mass flow rate, specific heat capacity, inlet fluid temperature, and outlet fluid temperature, respectively. Assuming that the amount of heat loss by the nanofluid is equal to the amount of heat received by the free flow of air around the tube, the average heat transfer coefficient and the average Nusselt number of the air flow are determined by Eqs. (2) and (3) [13, 49]. h Air 

 c p (Tin  Tout ) m

(2)

Do L(Ts  T )

Nu Air 

(3)

hDo k

In the above equation, Nu Air and h Air are Nusselt number and average heat transfer coefficient of the air flow, respectively. T∞,Ts, Do, and k are air flow temperature, tube surface temperature, outer diameter of tube, and thermal conductivity of air. In this study, the dynamic viscosity of the nanofluid was calculated using Eq. (4) [24]. Density and specific heat of the nanofluid were calculated using Eqs. (5) and (6) [37].

 nf   f (1.36041  0.014984T  1.06383  0.061667T  0.000158452T 2  8.35379 2 )

(4)

 nf   np  (1   )  f

(5)

C P nf   CP np  (1   )CP  f

(6)

In these equations,  is the volumetric concentration of nanoparticles, and  nf ,  nf , and C p nf are viscosity, density and specific heat capacity of the nanofluid, respectively. Reynolds and Prandtl numbers are defined by Eq. (7):

Re 

uD , 

Pr 

cp

(7)

k

Measurement of nanofluid pressure drop along with heat transfer is essential when using them in the industry. As previously mentioned, the pressure drop in the inlet and outlet of the test tube was measured using differential pressure gauge model PM-9102. The friction factor of the flow inside the tube, which is a dimensionless parameter, was calculated using the Darcy-Weisbach equation (Eq. (8)).

f 

P  2 pDi  L u 2 8 m 2 L Di 2

(8)

5

In the above equation, L is the length of the tube, Di is the inner diameter of the tube, u is the average velocity of the fluid inside the tube, m is fluid mass flow rate, P is pressure drop, and f is the friction factor of the internal flow. The heat transfer performance coefficient (HTP) is a dimensionless parameter introduced by Webb [50] in order to compare the heat transfer coefficient to friction factor according to Eq. (9). (9)

Nu E HTP 

Nu 1 f ( E )3 f

In the above equation, NuE and f E are Nusselt number and friction factor of nanofluid flow, respectively.

5.

Validation of results and error analysis

5.1 Validation of Results In order to ensure the accuracy of the test system performance, the experiment was first conducted using pure water, and results were compared with the results of reliable resources. Eq. (10) by Zhukauskas [51], which is proposed for external flow around a circular tube, was used to examine the heat transfer results. 1

Nu D  C Re mD Pr n (

Pr 4 ) PrS

0.7  Pr  500  6 1  Re  10

(10)

Eq. (11) was used to compare and evaluate the results associated with the hydrodynamic behavior of the internal flow. f 

64 Re

(11)

Fig. 6 compares the Nusselt number obtained from Eq. (3) for air and the Nusselt number obtained from Eq. (10). As can be seen, the greatest difference is 7.8%. The experimental results of the

friction factor of pure water flow in a smooth tube obtained from Eq. (9) are compared with those obtained from Eq. (11) in Fig. 7. The greatest difference is 6.2%. 100

Zhukauskas [51] Present work

90 80

__ NuAir

70 60 50 40 30 20

0

5000

10000

15000

20000

ReO Fig. 6. Comparison between the Nusselt number in the present work and the Nusselt number obtained

from Zhukauskas correlation 0.14

f=64/Re Present work

0.12

f

0.1

0.08

0.06

0.04

0.02

500

1000

1500

2000

Rei Fig. 7. Comparision of friction factor of pure water flow in a smooth tube obtained in the present work with those obtained from Eq. (11)

5.2. Error Analysis If an error is identified by the operator, it will be corrected and it will no longer be an error. The actual errors are those that are somewhat ambiguous. In this section, data analysis aims at determining the extent of uncertainty, meaning the extent to which they lack certainty. According to the diffusion theory proposed by Moffat et al. [52] (Eq. 12), the effect of equipment error (the respective error is provided in Table 3 on total uncertainty was examined, and the results are reported in Table 4.

 i 1

(12)

1

n

UR  [

(

R U v )] 2 Vi i

Table 3. Measuring instruments range and accuracy. Description Inlet and outlet temperatures Temp. of in. and out. flow Fluid flow rate Fluid pressure drop

No. 3 1 1 1

Model Type K thermocouple RTD (PT-100) sensor LZB 80 Model: PM-9102

Range -40 - 300 (℃) 0-100 (℃) 1~10 (m3/h) 0~200 (mbar)

Accuracy 0.5 (℃) 0.1 (℃) 2.5% F.S. 2% F.S.

Eqs. (13) and (14) pertain to uncertainty associated with Nusselt number and friction factor. The uncertainty of experimental data are reported in Table 4.

U Nu

1 qw  qw  qw  U Qw ]2  [ U ]2  [ U ]2  [ Uk ] 2 [ 2 T 2 Ts Lk (Ts  T ) Lk (Ts  T ) DLk 2 (Ts  T )  Lk (Ts  T )   qw  [ U ]2  L2 k (T  T ) L s  

(13)

1

2      

1

 d 5 2 2 5pd 4  pd 5 2  2pd 5 2  pd 5 2 2 2 2 2 U f  [ U ]  [ U ]  [ U ]  [ U ]  [ U  ] 2  p d L Q 2 2 2 2 3 2 2 8LQ  8L Q  8LQ  8LQ   8LQ   Table 4. The uncertainty of experimental data Parameters heat transfer Experimental heat transfer coefficient Nusselt number friction factor pressure drop

Uncertainty (%)

 5.3  6.9  7.1  4.2  3.9

(14)

6.

Results and discussion

A subsonic wind tunnel and a closed heat transfer cycle with water/graphene oxide nanofluid flow at volume fractions of 0, 0.05, 0.1, and 0.2% were used for this test. Reynolds numbers of the flow in the tube were 550, 1250, 2000, 2500, and 3250. Effect of changes in the air flow around the copper tube in the wind tunnel test was investigated at Reynolds numbers of 3800, 9200, 14500, 19900, and 21500. In this section, results in terms of velocity field in the wind tunnel, internal flow friction factor, average Nusselt number of air flow around the tube, and heat transfer performance coefficient are presented and discussed. 6.1. Velocity field in wind tunnel test section Since the equations for fluid flow and heat transfer are presented for a uniform velocity, uniformity of velocity was examined here. Therefore, before carrying out the experiments, in order to determine the velocity distribution at the wind tunnel, velocity was measured at various intervals by a hot wire anemometer. Uniform air flow velocity in the wind tunnel test can be changed to up to 22 m/s using a variable drive. As seen in Fig. 8, velocity is almost uniform in the transverse and axial

25

25

20

20

U (m/s)

U (m/s)

length of the test section of wind tunnel.

15

10

5

0

15

10

5

0

10

20

Z (cm)

30

40

0

0

20

40

60

80

100

X (cm)

Fig. 8. Velocity distribution in the transverse and axial length of the test section of wind tunnel

120

6.2. Thermo-physical properties of water/graphene oxide nanofluid In this work, dynamic viscosity, density and specific heat of water/graphene oxide nanofluid were directly used, which were determined using Eqs. (4) to (6), respectively. Thermal conductivity of the base fluid was used to calculate heat transfer in order to clearly show the effect of nanofluid. The nanofluid properties are determined according to the testing condition. Table 5 lists the complete numerical range of the nanofluid properties. Table 5. Full range of nanofluid properties of water / graphene oxide used in all experiments Properties of nanofluid

Units

Range of changes

Volumetric concentration of nanoparticle

%

0 to 0.2

Dynamic viscosity

Kg.m-1.s-1 or pa. s

3.88×10-4 to 4.43×10-4

Density

Kg.m-3

976.17 to 980.3

Specific heat Capacity

J.kg-1.K-1

4164.5 to 4191.42

6.3. Effect of the internal Reynolds number and nanofluid concentration on friction factor inside the tube The Reynolds number in the experiments conducted on internal flow ranged from 550 to 3250. Given the high heat transfer potential of water/graphene oxide nanofluid, its practical use requires a thorough and comprehensive experimental investigation of pressure and friction factor. The results of studying the parameters affecting the friction factor of water/graphene oxide nanofluid flow with volume concentrations of 0, 0.05, 0.1, and 0.2% in the copper tube are shown in Fig. 9. This figure shows the internal flow friction factor by Reynolds number for different volume fractions. As can be seen, in addition to the physical nature of pressure variations due to increased flow velocity, adding graphene oxide to the base fluid increases the dynamic viscosity of pure water. In fact, due to addition of nanoparticles to the base fluid, friction factor at different concentrations always increases in the nanofluid flow. An important reason for the increased friction factor in the nanofluid flow is addition of graphene oxide nanosheets to the base fluid that increases the dynamic viscosity of the nanofluid. As obvious in this figure, the maximum variation in the friction factor of

the nanofluid flow compared to pure water (fnf/fbf) occurs always at the Reynolds number of 3250. The most important reason for such results, in addition to the nanoparticles that alter the physical structure of the fluid flow, is the changes in the structure of nanofluid flow driven by the turbulence caused by flow acceleration. The changes in the friction factor of the nanofluid in this study are similar to the results reported by Mehr-Ali et al. [53]. Friction factor of the nanofluid flow at concentrations of 0.05, 0.1, and 0.2% was increased by 10, 15, and 21%, respectively, as compared to pure water. As observed in Fig. 9, for all nanofluid samples, friction factor decreases with increasing internal Reynolds number for laminar regime. However, with increasing internal Reynolds number, the laminar regime changes to transition regime and the trend of friction factor changes dramatically. This trend is consistent with Moody diagram.

=0  = 0.05%  = 0.1%  = 0.2%

0.12

 = 0.05%  = 0.1%  = 0.2%

1.2

0.1

fnf / fbf

f

1.15

0.08

1.1

0.06 1.05

0.04

0.02

1

0

500

1000

1500

2000

2500

Rei

3000

0

500

1000

1500

2000

2500

3000

Rei

Fig. 9. Friction factor and its ratio versus Reynolds number for various nanofluid samples

6.4. Average air flow Nusselt number Fig. 10 shows the changes in the average Nusselt number of external air flow by Reynolds number for different nanofluid samples. Changes in the internal flow Reynolds number showed an increase in heat transfer. The increasing trend of heat transfer grew as the Reynolds number approached the Reynolds number of the transition regime. This can be mainly attributed to the transition of flow from laminar to turbulent. This is also clear that the improved Nusselt number caused by the

internal flow Reynolds number has a more increasing trend compared to the external flow Reynolds number. The most important reason for this is the higher heat transfer capacity of water compared to air. =0

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

140

120

180 160 140

__ NuAir

__ NuAir

100

 = 0.1%

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

200

80

120 100

60

80 60

40

40 20

20 0

5000

10000

15000

20000

0

ReO

160 140

 = 0.05%

220

15000

20000

 = 0.2%

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

200 180 160 140

__ NuAir

120

__ NuAir

10000

ReO

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

180

5000

100

120 100

80

80 60

60 40

40

20

20 0

5000

10000

15000

ReO

20000

0

5000

10000

15000

20000

ReO

Fig. 10. Changes in the average Nusselt number of external air flow by Reynolds number for different nanofluid samples

In this study, use of nanofluid flow indirectly improved the air flow heat transfer performance. In fact, results suggested effectiveness of nanofluid flow in terms of heat transfer. Results indicate that use of turbulent flow can be very useful in achieving higher average heat transfer coefficient. According to Fig. 10 and the two-phase structure of the nanofluid flow, a reason for the increased heat transfer related to the nanofluid flow Reynolds number appears to be the changes in the

boundary layer inside the tube. Considering the physics of the boundary layer, when the thickness decreases, i.e. transition of laminar flow to turbulent, the temperature gradient increases, which is one of the most important parameters for improved heat transfer. Furthermore, the addition of nanoparticles to the base fluid increases the thermal conductivity of the fluid. The use of water/graphene oxide nanofluid in different conditions increased the air flow Nusselt number. Nusselt number dramatically increased with increasing the concentration of nanofluid from 0.05% to 0.2%, which is attributed to the high heat transfer potential in this nanofluid compared to pure water. Fig. 10 also shows that increased internal flow Reynolds number intensifies the effects of dispersion and irregular movements as well as distribution of nanoparticles in the base fluid, improving the heat transfer to the air flow. On the other hand, given the high heat transfer potential of the nanofluid, nanoparticles near the wall of the tube have a greater impact on the energy exchange rate, greatly enhancing the heat transfer between the fluid and the tube wall on which air flow passes. As can be seen, increased external flow Reynolds number has a significant impact on heat transfer to the air flow. The slope of the heat transfer enhancement increases with increasing air flow Reynolds number. The most important reason for this increase can be the fact that, when the air flow and outer surface of the tube are in contact, heat is transferred by random molecular motion (heat dissipation) and bulk movement of the fluid. This advantage is an inherent property of heat transfer in external flows. Therefore, the improvement of average Nusselt number of air flow is more significant at higher Reynolds numbers. The highest increases in a similar condition were 21.5, 31.8, and 51.4% for concentrations of 0.05, 0.1, and 0.2%, respectively. 6.5. Thermal performance coefficient of water/graphene oxide nanofluid flow As mentioned earlier, nanofluids flow significantly increases the heat transfer rate. However, unwanted friction factor of the flow increases at the same time (Fig. 9). Therefore, by examining the heat transfer performance coefficient defined by Eq. (10), thermal and hydrodynamic analyses were performed analytically. Fig. 11 shows the heat transfer performance coefficient (HTP) as a function

of volume fraction of nanoparticles, external flow Reynolds number, and internal flow Reynolds number. 1.36

Rei=550

 = 0.05%  = 0.1%  = 0.2%

Rei=1250

 = 0.05%  = 0.1%  = 0.2%

1.36

1.32

1.32

HTP

HTP

1.28

1.28

1.24

1.24

1.2

1.2

1.16

1.16 0

5000

10000

15000

20000

0

ReO

Rei=2000

15000

20000

Rei=2550

 = 0.05%  = 0.1%  = 0.2%

1.4

1.36

HTP

1.32

HTP

10000

ReO

 = 0.05%  = 0.1%  = 0.2%

1.36

5000

1.28

1.32

1.28

1.24 1.24

1.2 1.2

1.16 0

5000

10000

15000

ReO

20000

0

5000

10000

15000

ReO

20000

1.48

Rei=3250

 = 0.05%  = 0.1%  = 0.2%

1.44

HTP

1.4

1.36

1.32

1.28

1.24

1.2

0

5000

10000

15000

20000

ReO

Fig. 11. HTP as a function of volume fraction, external Reynolds number, and internal Reynolds number

As can be seen, HTP in all internal flow Reynolds numbers for the highest volume concentration (0.2%) shows a higher increase compared to other concentrations. If heat transfer performance coefficient is considered as a design criteria in a thermal systems, it can be argued that the use of nanofluids with higher concentrations, despite the negative and unwanted changes in friction factor, can be effective, and hydrodynamic performance of nanofluid can be acceptable and heat transfer can be highly efficient. HTP indicates the thermal efficiency of the nanofluid flow and superiority of improved Nusselt number on increased unwanted pressure drop. The highest HTP was observed in the nanofluid with volume concentration of 0.2%. Considering the changes in the Reynolds number, the maximum HTP was 1.313, 1.348, 1.341, 1.381 and 1.422 at internal Reynolds numbers of 550, 1250, 2000, 2550, and 3250, respectively.

7.

Conclusion

Since heat transfer enhancement in thermal systems is particularly important in industry, the use of water/graphene oxide nanofluid, which can provide a significant improvement in heat transfer, was evaluated in this study. Therefore, a cross-flow heat transfer system was constructed for the

feasibility of using water/graphene oxide nanofluid to improve heat transfer. The main results are as follows: 

Considering the zeta potential test, the stability was very good at pH = 9 with   41 . Prepared nanofluids also kept their in the long time that is due to the dispersion of nanoparticles in the base fluid, favorable pH of base fluid, the presence of functional groups of graphene oxide, their nano size and lack of density differences with the base fluid.



The friction factor of nanofluid flow increased with increasing volume fraction of graphene oxide in all internal Reynolds numbers. The maximum increase in friction factor (~21%) was related to maximum concentration of graphen oxide, which is due to unwanted increase in viscosity with increasing volume fraction.



The slope of the heat transfer enhancement increased with increasing air flow Reynolds number. The maximum slope of the average Nusselt number of air flow was related to the nanofluid with the highest volume concentration (φ=0.2%). This means that in the aircooled systems, increasing the amount of air flow leads to an increase in nanofluid heat transfer rate due to the dominance of the effect of convective heat transfer.



At highest volume concentration (0.2%) and for all internal flow Reynolds numbers, heat transfer performance (HTP) showed a higher increase compared to other concentrations. Moreover, the maximum HTP occurred at highest internal flow Reynolds number (Rei=3250) and highest volume concentration (φ=0.2%). Thus, the use of nanofluids in this with higher concentrations can be effective in such circumstances.

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Figure Captions Fig. 1. Schematic of experimental setup Fig. 2. Image of experimental setup Fig. 3. SEM (left) and X-Ray Diffraction (right) for graphene oxide nanosheets Fig. 4. FTIR spectrum of graphene oxide Fig. 5. Zeta potential test related to nanofluid stability in different pH Fig. 6. Comparison between the Nusselt number in the present work and the Nusselt number obtained from Zhukauskas correlation Fig. 7. Comparision of friction factor of pure water flow in a smooth tube obtained in the present work with those obtained from Eq. (11) Fig. 8. Velocity distribution in the transverse and axial length of the test section of wind tunnel

Fig. 9. Friction factor and its ratio versus Reynolds number for various nanofluid samples Fig. 10. Changes in the average Nusselt number of external air flow by Reynolds number for different nanofluid samples Fig. 11. HTP as a function of volume fraction, external Reynolds number, and internal Reynolds number

Figures

1. Inlet sir flow 2. Test section

6. Digital differential digital manometer 7. Hot water circulation pump

11. Hot water tank 12. Heating element

3. Copper tube

8. Bypass valve

13. K Type thermocouple

4. Fan

9. Flow control valve

5. Variable frequency drives

10. Flow meter

Fig. 1. Schematic of experimental setup

Fig. 2. Image of experimental setup

Fig. 3. SEM (left) and X-Ray Diffraction (right) for graphene oxide nanosheets

60

40

10

0

(C=O) 3392-------

20

1716------

30

1045-----1218------

Transmittance (%)

50

(C-O) (C-O)

1000

1500

2000

2500

3000

(-OH)

3500

4000

-1

Wavelength (cm ) Fig. 4. FTIR spectrum of graphene oxide

Fig. 5. Zeta potential test related to nanofluid stability in different pH

100

Zhukauskas [51] Present work

90 80

__ NuAir

70 60 50 40 30 20

0

5000

10000

15000

20000

ReO Fig. 6. Comparison between the Nusselt number in the present work and the Nusselt number obtained

from Zhukauskas correlation 0.14

f=64/Re Present work

0.12

f

0.1

0.08

0.06

0.04

0.02

500

1000

1500

2000

Rei Fig. 7. Comparision of friction factor of pure water flow in a smooth tube obtained in the present work with those obtained from Eq. (11)

25

20

20

U (m/s)

U (m/s)

25

15

10

5

0

15

10

5

0

10

20

30

0

40

0

20

40

Z (cm)

60

80

100

X (cm)

Fig. 8. Velocity distribution in the transverse and axial length of the test section of wind tunnel

=0  = 0.05%  = 0.1%  = 0.2%

0.12

 = 0.05%  = 0.1%  = 0.2%

1.2

0.1

fnf / fbf

f

1.15

0.08

1.1

0.06 1.05

0.04

0.02

1

0

500

1000

1500

2000

Rei

2500

3000

0

500

1000

1500

2000

2500

Rei

Fig. 9. Friction factor and its ratio versus Reynolds number for various nanofluid samples

3000

120

=0

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

140

120

180 160 140

__ NuAir

__ NuAir

100

 = 0.1%

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

200

80

120 100

60

80 60

40

40 20

20 0

5000

10000

15000

20000

0

ReO

160 140

 = 0.05%

220

15000

20000

 = 0.2%

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

200 180 160 140

__ NuAir

120

__ NuAir

10000

ReO

Rei=550 Rei=1250 Rei=2000 Rei=2550 Rei=3250

180

5000

100

120 100

80

80 60

60 40

40

20

20 0

5000

10000

15000

ReO

20000

0

5000

10000

15000

20000

ReO

Fig. 10. Changes in the average Nusselt number of external air flow by Reynolds number for different nanofluid samples

1.36

Rei=550

 = 0.05%  = 0.1%  = 0.2%

Rei=1250

 = 0.05%  = 0.1%  = 0.2%

1.36

1.32

1.32

HTP

HTP

1.28

1.28

1.24

1.24

1.2

1.2

1.16

1.16 0

5000

10000

15000

20000

0

ReO

Rei=2000

15000

20000

Rei=2550

 = 0.05%  = 0.1%  = 0.2%

1.4

1.36

HTP

1.32

HTP

10000

ReO

 = 0.05%  = 0.1%  = 0.2%

1.36

5000

1.28

1.32

1.28

1.24 1.24

1.2 1.2

1.16 0

5000

10000

15000

0

20000

5000

10000

1.48

20000

Rei=3250

 = 0.05%  = 0.1%  = 0.2%

1.44

15000

ReO

ReO

HTP

1.4

1.36

1.32

1.28

1.24

1.2

0

5000

10000

15000

20000

ReO

Fig. 11. HTP as a function of volume fraction, external Reynolds number, and internal Reynolds number

Tables Table 1. Maximum values of thermal conductivity enhancement for different nanofluids reported in previous works Ref. Number

Nanomaterial

Base fluid

Temperature o C

Concentration

Thermal conductivity enhancement %

[18]

SWCNT

Water

50

0.25 wt%

36.5

[19]

Al2O3

Water/EG

50

0.8 vol.%

17.89

[20]

Graphite

EG

65

0.4 vol.%

27

[21]

Cuo

Water

-

0.01 vol.%

53

[22]

MgO

Water/EG

50

3 vol.%

57

[23]

Graphene oxide

Water

60

0. 5 wt%

56

[24]

Graphene

Water

60

0. 5 wt%

32

[25]

Fe3O4

Water/EG

55

3 vol.%

88

[26]

MWCNT

Water/EG

50

1 vol.%

34.7

Water

40

4 vol.%

45

EG

60

5 vol.%

86

[27] [28]

Reduced graphene oxide Graphene

[29]

SWCNTs

EG

50

0.65 vol.%

54

[30]

MWCNTsMgO

EG

25

0.6 vol.%

21.3

Table 2. Thermo-physical properties of graphene oxide nanosheets. Thermo-physical property

Value

Appearance/ Morphology Diameter (µm) Purity (%) density (g/cm3) Thickness (nm) Specific surface area (m2/g) Thermal conductivity (parallel to surface) (W/m K) Thermal conductivity (perpendicular to surface) (W/m K)

Black powder/ nanosheets 2 99 1 3.4-7 nm with 6-10 Layers 400-600 2500 5

Table 3. Measuring instruments range and accuracy. Description Inlet and outlet temperatures Temp. of in. and out. flow Fluid flow rate Fluid pressure drop

No. 3 1 1 1

Model Type K thermocouple RTD (PT-100) sensor LZB 80 Model: PM-9102

Range -40 - 300 (℃) 0-100 (℃) 1~10 (m3/h) 0~200 (mbar)

Accuracy 0.5 (℃) 0.1 (℃) 2.5% F.S. 2% F.S.

Table 4. The uncertainty of experimental data Parameters heat transfer

Uncertainty (%)

Experimental heat transfer coefficient Nusselt number friction factor pressure drop

 5.3  6.9  7.1  4.2  3.9

Table 5. Full range of nanofluid properties of water / graphene oxide used in all experiments Properties of nanofluid

Units

Range of changes

Volumetric concentration of nanoparticle

%

0 to 0.2

Dynamic viscosity

Kg.m-1.s-1 or pa. s

3.88×10-4 to 4.43×10-4

Density

Kg.m-3

976.17 to 980.3

Specific heat Capacity

J.kg-1.K-1

4164.5 to 4191.42

Highlights  Examining the effect water/graphene oxide nanofluid on heat transfer and pressure drop.  Subsonic wind tunnel and a closed heat transfer cycle were used at the same time.  Investigating the effects of concentrations and Reynolds number on Nu, f and HTP.  By using water/graphene oxide nanofluid, Nu and f increased by 51.4% and 21%, respectively.  Heat transfer performance coefficient increased by up to 42.2%.