study of forced convection nanofluid heat transfer in the automotive cooling system

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Case Studies in Thermal Engineering

Case Studies in Thermal Engineering 2 (2014) 50–61

2214-15http://d

E-m1 Te

journal homepage: www.elsevier.com/locate/csite

Study of forced convection nanofluid heat transfer in the automotivecooling system

Adnan M. Hussein a,b,1, R.A. Bakar a, K. Kadirgama a

a Faculty of Mechanical Engineering, University Malaysia Pahang, Pekan 26600, Pahang, Malaysiab Al-Haweeja Institute, Foundation of Technical Education, Iraq

a r t i c l e i n f o

Article history:Received 15 November 2013Accepted 2 December 2013Available online 11 December 2013

Keywords:LaminarNanofluidHeat transferCar radiatorCFD

7X & 2013 The Authors. Published by Elsevix.doi.org/10.1016/j.csite.2013.12.001

ail address: [email protected] (A.Ml.: þ60 94246232; fax: þ60 94246222.

a b s t r a c t

The heat transfer enhancement for many industrial applications by adding solid nano-particles to liquids is significant topics in the last 10 years. This article included the frictionfactor and forced convection heat transfer of SiO2 nanoparticle dispersed in water as abase fluid conducted in a car radiator experimentally and numerically. Four differentconcentrations of nanofluids in the range of 1–2.5 vol% have been used. The flowratechanged in the range of 2–8 LPM to have Reynolds number with the range 500–1750.The results showed that the friction factor decreases with an increase in flowrate andincrease with increasing in volume concentration. Furthermore, the inlet temperature tothe radiator has insignificantly affected to the friction factor. On the other side, Nusseltnumber increases with increasing in flowrate, nanofluid volume concentration and inlettemperature. Meanwhile, application of SiO2 nanofluid with low concentrations canenhance heat transfer rate up to 50% as a comparison with pure water. The simulationresults compared with experimental data, and there is a good agreement. Likewise, theseresults compared to other investigators to be validated.

& 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.

1. Introduction

In the last 10 years, there has been more attention paid to enhance the convective heat transfer performance of nanofluids [1],due to the recognition that in practical applications nanofluids. Experimental studies of friction factor and nanofluids heattransfer enhancement with the flow velocity and nanofluid volume fraction inside heated tube under laminar flow condition hasbeen presented by [2–5]. There are many different applications of thermal-fluid systems, including automotive cooling systems[6]. Base fluids, such as water, ethylene glycol and glycerol, have been used as conventional coolants in automobile radiators formany years; however, these fluids have low thermal conductivities. The low thermal conductivities have thus promptedresearchers to search for fluids with higher thermal conductivities than that of conventional coolants. Therefore, nanofluids havebeen used instead of the commonly used base fluids [7–9]. A numerical study on laminar heat transfer using CuO– and Al2O3–

ethylene glycol and water inside a flat tube of a car radiator was performed by Vajjha et al. [10]. A CFD model of the massflowrate of air passing across the tubes of a car radiator was presented by Peyghambarzadeh et al. [11]. The air flow wassimulated using the commercial software ANSYS 12.1, where the geometry was created in the software SOLID WORKS, followedby creating both the surface mesh and the volume mesh accordingly. The results were compared and verified according to the

er Ltd.

. Hussein).

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Nomenclature

C specific heat [W/kg oC]D diameter [m]E energy [W]f friction factorhtc convection heat transfer coefficient [W/m2 oC]k thermal conductivity [W/m oC]Nu Nusselt Number [htcD/Knf]P pressure [N/m2]Pr Prandtl number [Cμ/Knf]Re Reynolds number [ρnfDhu/Knf]u velocity [m/s]

μ viscosity [N s/m2]ρ density [kg/m3]τ shear stress [N/m2]ϕ volume concentration

Subscripts

f liquid phasesp solid particlenf nanofluidh hydraulic

A.M. Hussein et al. / Case Studies in Thermal Engineering 2 (2014) 50–61 51

known physical situation and existing experimental data. The results serve as a good database for future investigations. Newcorrelations for the viscosity and thermal conductivity of nanofluids as a function of volumetric particle concentration andtemperature developed from the experiments were used in this paper. The convective heat transfer coefficient and shear stress ofthe nanofluid showed marked improvement over the base fluid, showing higher magnitudes in the flat regions of the tube. Theresults showed that increasing the nanofluid volume fraction increased the friction factor and convective heat transfercoefficient; however, there was also an increase in pressure loss as the particle volume fraction increased. A numerical study thatanalyzed mixed convection flows in a U-shaped grooved tube “in a radiator” was conducted by Park and Pak [12]. A modifiedSIMPLE algorithm for the irregular geometry was developed to determine the flow and temperature field. The results have beenused as fundamental data for tube design by suggesting optimal specifications for radiator tubes. Two liquids, water and anethylene glycol/water mixture, used as the coolant fluid in a meso-channel heat exchanger were studied numerically byDehghandokht et al. [13]. The predicted results (heat transfer rate, pressure and temperature drops in the coolants) from thenumerical simulation were compared with the experimental data for the same geometrical and operating conditions andshowed good agreement. Additionally, the results showed the heat exchanger was enhanced, with heat transfer rateapproximately 20% higher than that of a straight slab of the same length; the enhanced heat exchanger has a good potentialto be used as a car radiator with reasonably enhanced heat transfer characteristics using an ethylene glycol/water mixture as thecoolant. The application of a copper–ethylene glycol nanofluid in a car cooling system has been studied by Leong et al. [14]. Thefriction factor has been evaluated experimentally by [15–17], but heat transfer enhancement using water and ethylene glycolwater in a multi-port serpentine meso-channel heat exchanger has been determined as [18]. The overall heat transfercoefficient of CuO/water nanofluids investigated experimentally under laminar flow regime in a car radiator by Naraki et al.[19]. The results showed that the overall heat transfer coefficient with nanofluid was more than the base fluid. The overall heattransfer coefficient increased with the enhancement in the nanofluid concentration from 0 to 0.4 vol%. Conversely, the overallheat transfer coefficient decreased with increasing the nanofluid inlet temperature from 50 to 80 1C. The implementation ofnanofluid increases the overall heat transfer coefficient up to 8% at nanofluid concentration of 0.4 vol% in comparison with thebase fluid. Theoretical results of nanofluid heat transfer of Maiga et al. [20] have been verified with the correlations of Seider–Tate [21] for base fluid only. Forced convection heat transfer of both CuO and Fe2O3 nanofluids flow through the car radiatorhas been studied experimentally by Peyghambarzadeh et al. [22]. An experimental investigation for the determination of heattransfer coefficient with SiO2 nanofluid under cooling and heating conditions at fluid inlet temperatures of 20, 50 and 70 1C forReynolds number range from 200 to 10,000 and 22 nm size diameter particles suspension in water has been conducted byFerrouillat et al. [23]. The heat transfer coefficients at particle volume concentrations of 2.3% and 19% have been determined as10% and 50% respectively greater than the values obtained with water.

In this paper, an experimental and computational study of forced convection heat transfer in a car radiator with SiO2

nanoparticles suspension in water under laminar flow is presented. The test rig is setup to measure temperatures andpressure drop between inlet and outlet of the radiator. Furthermore, the CFD model is simulated by finite volume methodand using FLUENT software to solve governing equations. The range of Reynolds number is 250–1750 and the volumefraction of nanofluid is from 1% to 2.5%. The aim of this study is to enhance heat transfer in the car cooling system. Thecomparison among experimental and simulation results are carried out then validated with other researchers data.

2. Experimental work

2.1. Experimental setup and procedure

The test rig shown in Fig. 1a has been used to measure heat transfer coefficient and friction factor in the automotiveengine radiator. This experimental setup includes a reservoir plastic tank, electrical heater, a centrifugal pump, a flow meter,

L d

D

Fig. 1. (a) Schematic diagram of experimental test rig and (b) flat tube configuration.

A.M. Hussein et al. / Case Studies in Thermal Engineering 2 (2014) 50–6152

tubes, valves, a fan, a DC power supply; 10 thermocouples type T for temperature measurement, manometer tube withmercury and heat exchanger (automobile radiator). An electrical heater (1500 W) inside a plastic storage tank (40 cm heightand 30 cm diameter) put to represent the engine and to heat the fluid. A voltage regular (0–220 V) provided the power tokeep the inlet temperature to the radiator from 60 to 80 1C. A flow meter (0–70 LPM) and two valves used to measure andcontrol the flowrate. The fluid flows through plastic tubes (0.5 in.) by a centrifugal pump (0.5 hp and 3 m head) from thetank to the radiator at the flowrate range 2–8 LPM. The total volume of the circulating fluid is 3 l and constant in all theexperimental steps. Two thermocouples (copper–constantan) types T have been fixed on the flow line for recording the inletand outlet fluid temperatures. Eight thermocouples type T have been fixed to the radiator surface to ensure more of surfacearea measurement. Two thermocouples type T also fixed in front of the fan and another side of radiator to measure airtemperatures. Very small thickness and high thermal conductivity of the copper flat tubes caused to make the insidetemperature of the tube with the outside one are equated. A handheld (�40 1C to 1000 1C) digital thermometer with theaccuracy of 70.1 1C used to read all the temperatures from thermocouples. Calibration of thermocouples and thermometerscarried out by using a constant temperature water bath and their accuracy estimated to be 0.15 1C [19]. Two small plastictubes with 0.25 in. diameter connected at inlet and outlet radiator and joined to U-tube mercury manometer with theaccurate scale 0.5 mm Hg to measure the pressure drop on the inlet and outlet of it. The car radiator has louvered fin and 32flat vertical copper tubes with flat cross sectional area. The distances among the tube rows filled with thin perpendicularcopper fins. For the air side, an axial force fan (1500 rpm) installed close on axis line of the radiator. The DC power supply(type Teletron 10–12 V) used to turn the axial fan instead of a car battery. The thermal properties of nanofluids have beenmeasured by [24] and used in this study. The pH values of nanofluid have been measured using OAKTON device shown in forthe nanofluid volume concentration of (1–2.5%). The pH values before and after experimental tests referring to nanofluidsstability and changing in thermal properties. If the pH values of the suspension decrease, the force among particles willincrease which the moving of the nanoparticles suspension lead to enhance the heat transfer process. So, to augment heattransfer for many applications should keep low values of the nanofluid pH [25]. The pH meter has been calibrated using asingle point calibration technique, with a Hatch standard buffer solution of pH 7.0070.02. According to [24] to find out thepH values, samples with pH of 8.2, 8.6, 9.3 and 10.2 were prepared. Samples checked after finishing each test but found novisible sedimentation. SiO2 nanoparticles with volume concentration (1, 1.5, 2.0 and 2.5 vol%) and 30 nm size diameteradded to base fluids (deionized water). The thermal properties of SiO2 nanoparticle and base fluids are shown in Table 1.

2.2. Experimental data analysis

According to Newton's cooling law the following procedure followed to obtain heat transfer coefficient and correspondingNusselt number as [26]:

Q ¼ hAΔT ¼ hAsðTb�TsÞ ð1Þ


As is surface area of tube, Tb is the bulk temperature,

Tb ¼TinþTout

2ð2Þ

Tin and Tout are inlet and outlet temperatures, respectively and Ts is the tube wall temperature which is the mean value by twosurface thermocouples as

Ts ¼T1þ :::þT8

8ð3Þ

And heat transfer rate calculated by

Q ¼m�CΔT ¼m�CðTin�ToutÞ ð4Þm� is mass flowrate which determined as

m� ¼ ρV� ð5ÞThe heat transfer coefficient can be evaluated by combining Eqs. (1) and (4)

hexp ¼m�CðTin�ToutÞAsðTb�TsÞ

ð6Þ

and Nusselt number can be calculated as

Nu¼ hexpDh

kð7Þ

Dh is the hydraulic diameter of the tube which estimated by description the problem undertaken as cylindrical geometrycoordinates shown in Fig. 1b, dimensions of the flat tube are major and minor diameter (D¼9mm, d¼3mm), the length (L) andhydraulic diameter (Dh) of the flat tube are 345mm and 4.68 mm. Reynolds number calculated regarded hydraulic diameter (Dh)as [27]

Dh ¼4� areaperimeter

Dh ¼4� π

4 d2þðD�dÞ � d

h i

π � dþ2� ðD�dÞ ð8Þ

Reynolds number (Re) is determined as

ReD ¼ ρnf Dhuμnf

: ð9Þ

Table 1Thermal property of nanoparticle and base fluids.

Materials Density (Kg/m3) Specific heat (J/kg 1C) Thermal conductivity (W/m 1C) Viscosity (Pa s) References

SiO2 2220 745 1.4 – [24]Pure water 998 4180 0.6067 0.0014 [24]

Table 2Uncertainties of measured parameters.

No. Parameter Values Uncertainties (%)

1 Dh 4.68 mm 0.782 Re 250–1750 4.33 Nu 8–22 2.42

Fig. 2. Meshing by GAMBIT.


2.3. Uncertainty analysis

The uncertainty analysis performed by calculating the measurements error. The Reynolds number uncertainty range maybe come from the errors in the measurement of volume flowrate (Eq. (5)) and hydraulic diameter of the tubes (Eq. (8)). Theuncertainty of Nusselt number refers to the errors in the measurements of volume flowrate (Eq. (5)), hydraulic diameter(Eq. (8)), and all the temperatures (Eqs. (2) and (3)). According to uncertainty analysis described by [28], the measurementerrors of the main parameters are summarized in Table 2. Furthermore, to check the reproducibility of the experiments,some runs were repeated later which proved to be excellent.

0 500 1000 1500 2000Reynolds Number

0.00

0.10

0.20

0.30

0.40

Fric

tion

Fact

or

Pure Water

Grid: 18x345

Grid: 36x345

Grid: 72x345

Grid: 108x345

Darcy Eq (13)

0 500 1000 1500 2000Reynolds Number

8

12

16

20

24

Nus

selt

Num

ber

Pure Water

Grid: 18x345

Grid: 36x345

Grid: 72x345

Grid: 108x345

Shah-London Eq (14)

Fig. 3. Grid independent test. (a) Friction factor and (b) Nusselt number.


3. Theoretical analysis

3.1. Physical model

The assumptions of the study undertaken were steady, incompressible and Newtonian laminar fluid flow with constantthermo-physical properties of the nanofluid. Additionally, heat conduction in the axial direction and wall thickness of thetubes was neglected. Assuming, also, all thermal properties estimated at bulk temperature of the fluid.

3.2. Governing equations

The single-phase method can be adopted by using infinitesimal (less than 100 nm) solid particles in the liquid. Thedimensional conservation equations at steady state are the continuity, momentum and energy equations [26]

∇Vð Þ ¼ 0 ð10Þ

ρnf ∇Vð ÞV ¼ �∇Pþμnf∇2V ð11Þ

ρnf Cnf V∇ð ÞT ¼ knf∇2T ð12Þ

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 500 1000 1500 2000

Fric

tion

Fact

or

Reynolds Number

pure water1%EXP.1%CFD1.5%EXP.1.5%CFD2%EXP.2%CFD2.5%EXP.2.5%CFD

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 500 1000 1500 2000

Fric

tion

Fact

or

Reynolds Number

pure water1%EXP.1%CFD1.5%EXP.1.5%CFD2%EXP.2%CFD2.5%EXP.2.5%CFDDarcy Eq (13)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 500 1000 1500 2000

Fric

tion

Fact

or

Reynolds Number

pure water1%EXP.1%CFD1.5%EXP.1.5%CFD2%EXP.2%CFD2.5%EXP.2.5%CFDDarcy Eq (13)

Fig. 4. Friction factor at different Reynolds number: (a) at 60 1C; (b) at 70 1C and (c) at 80 1C.


A low Reynolds number was used as an input parameter, and the pressure treatment was adopted using the SIMPLE scheme.For all the governing solutions, when the residuals were lower than 10�6, solutions were considered converged. The results of thesimulation were compared with the equation for the friction factor which correlated by Darcy–Weisbach equation (Eq. (13)) andNusselt number which correlated by Shah–London equation (Eq. (14)) for laminar flow respectively as [29]

f ¼ 64Re

ð13Þ

Nuav ¼ 1:953 ReDhPr Dh

L

� �ð1=3Þfor RePrDh=L

� �Z33:33

Nuav ¼ 4:364þ0:0722 ReDhPr Dh

L

� �for RePrDh=L

� �o33:33

ð14Þ

3.3. Boundary conditions

SiO2–water nanofluid volume concentrations of 1%, 1.5%, 2% and 2.5% at a base temperature of 25 1C were used as theinput fluids. For comparison purposes, water was also used as the working fluid. CFD studies were performed with uniform

49

1419242934394449

0 500 1000 1500 2000

Nus

selt

Num

ber

Reynolds Number

pure water1%EXP.1%CFD1.5%EXP.1.5%CFD2%EXP.2%CFD2.5%EXP.2.5%CFDShah-London Eq (14)

4

14

24

34

44

54

0 500 1000 1500 2000

Nus

selt

Num

ber

Reynolds Number


4

14

24

34

44

54

64

0 500 1000 1500 2000

Nus

selt

Num

ber

Reynolds Number


Fig. 5. Nusselt number at different Reynolds number: (a) at 60 1C; (b) at 70 1C and (c) at 80 1C.


velocity profile at the inlet, and a pressure outlet condition was used at the outlet of the radiator. The walls of the radiatortubes were assumed to be perfectly smooth. The Reynolds number was varied from 500 to 1750 at each iteration step asinput data. The output data are the friction factor and Nusselt number.

3.4. Grid independence test

Grids independence was determined using the GAMBIT software and was found for 72�345 with subdivisions in theaxial directions and the face of the flat area. The grid independence test for the physical model performed to determine themost suitable size of the mesh faces. In this study, rectangular cells were used to mesh the surfaces of the tube wall, and thesurfaces of the gap, as shown in Fig. 2. Grid independence was checked using different grid systems, and four mesh faceswere considered, 18�345, 36�345, 72�345 and 108�345 for pure water. The friction factor and Nusselt number havebeen determined for all four mesh faces, and the results all agreed with each other and Eqs. (13) and (14). All four mesh facescould have been used, and in this study, the mesh face with 12,000 cells was adopted because it was the best in terms ofaccuracy, as shown in Fig. 3.

30

40

50

60

70

80

90

100

0 2 4 6 8 10

T out

Flowrate (LPM)


40

50

60

70

80

90

100

110

0 2 4 6 8 10

Tou

t

Flowrate (LPM)


45

55

65

75

85

95

105

115

0 2 4 6 8 10

T out

Fowrate (LPM)


Fig. 6. Outlet temperature at different volume flowrate: (a) at 60 1C, (b) at 70 1C and (c) at 80 1C.


3.5. CFD simulation

CFD simulations were performed using FLUENT software with the solver strategy, and the GAMBIT software used toanalyze the problems. The governing equations have been solved by using the control volume approach for simulating thegoverning equations numerically. Using FLUENT software for CFD analysis has been done in the literature, and a detaileddescription of the mathematical model can be found in [30]. There are distinct steps to design a region using CFD: in the firststep, the pre-process stage, the geometry of the problem was constructed as a flat tube, and the computational mesh wasgenerated in GAMBIT. The next step involves creating a physical model and choosing the boundary conditions and otherparameters required to define the model setup and solving stage. All scalar values and velocity components of the problemwere calculated at the center of the control volume interfaces, where the grid schemes are used intensively. Throughout theiterative process, the residuals were continually monitored. When the residuals for all governing equations were less than10�6, all solutions were assumed to be converging. Finally, the results were obtained when the FLUENT iterationsconverged, which was defined by a set of convergence criteria. The friction factor and Nusselt number inside the flat tubecould then be defined throughout the computational domain in the post-process stage.

4. Results and discussion

The friction factor at different Reynolds number and nanofluid volume concentration is shown in Fig. 4. It appears thatthe friction factor decreases with increasing in Reynolds number and nanofluid volume concentration. Fig. 4a–c illustratesthe effect of inlet temperature on the friction factor. It seems that slightly effect of inlet temperature and nanofluid volumeconcentration on the friction factor. A reason related to the slow flow is generated high friction factor. The friction factor atReynolds number less than 1000 has been given the maximum deviation is 82% but after that the maximum deviation

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 2 4 6 8 10

Fric

tion

fact

or

Flowrate (LPM)

1%EXP at 60 oC1%CFD at 60 oC1%EXP at 70 oC1%CFD at 70 oC1%EXP at 80 oC1%CFD at 80 oCDarcy Eq (13)

0

5

10

15

20

25

30

35

0 2 4 6 8 10

Nus

selt

Num

ber

Flowrate (LPM)

1%EXP at 60 oC1%CFD at 60 oC1%EXP at 70 oC1%CFD at 70 oC1%EXP at 80 oC1%CFD at 80 oCShah-London Eq (14)

Fig. 7. The effect of inlet temperature on: (a) friction factor and (b) Nusselt number.


appears 40%. Fig. 5 shows Nusselt number at different Reynolds number and nanofluid volume concentration. The Nusseltnumber increases with increasing of Reynolds number and nanofluid volume concentration. The deviation is approximately40% when adding the nanoparticles on base fluid (pure water). Likewise, Fig. 5a–c demonstrates the effect of inlet radiatortemperature on Nusselt number. The highest values of Nusselt number found at inlet temperature 80 1C followed by at 70 1Cand finally at 60 1C inlet temperature. The maximum values of Nusselt number are 17.8, 21 and 25 at 60, 70 and 80 1Crespectively. This refers to high heat transfer from the radiator when high inlet temperature would apply. Fig. 6 shows theoutlet temperature of the radiator (Tout) with different volume flowrate circulating and nanofluid volume concentrations.It seems that the outlet temperature increases with increasing in the volume flowrate and decreasing in nanofluid volumeconcentration. The outlet temperature at pure water is the highest which referred the heat transfer increasing from theradiator. It should be noted that all the data in Fig. 6a, b and c obtained when the radiator inlet temperatures were 60, 70and 80 1C, respectively. It also showed that the outlet temperature increases with increasing of inlet temperature and thedifference between inlet and outlet temperature for each run. This means the heat transfer increases with increasing of inlettemperature too. Fig. 7a shows the friction factor at different volume flowrates and different inlet temperatures. It appearsthat the friction factor decreases with increasing of volume flowrate and slightly decreases with increasing of the inlettemperature. Darcy equation (Eq. (13)) is also indicated as solid black line for pure water. The friction factor of 1% nanofluidincreases by approximately 15% as compared with pure water. On the other side, Fig. 7b illustrates Nusselt number atdifferent Reynolds numbers and inlet temperatures. The Shah–London equation (Eq. (14)) is also indicated as solid black linefor pure water. The Nusselt number increases with increasing of volume flowrate and inlet temperature. The Nusseltnumber of 1% nanofluid at 80 1C has 52% deviation than pure water but 32% at 60 1C. Fig. 8 indicates the percentage of heattransfer enhancement depending on nanofluid volume concentration and inlet temperature to the radiator. The percentageenhancement can be evaluated by

Enhancement%¼ Nunf �Nuw

Nunf� 100 ð15Þ

It is observed that the heat transfer enhancement increases with increasing of nanofluid volume concentration and inlettemperature respectively. The values of heat transfer enhancement are from 31% to 46% for nanofluid volume concentration

05

101520253035404550

0

10

20

30

40

50

60

EXP at 60 oC

CFD at 60 oC

EXP at 70 oC

CFD at 70 oC

EXP at 80 oC

CFD at 80 oC

Enha

ncem

ent %

En

hanc

emen

t %

Fig. 8. (a) The effect of nanofluid volume concentration and (b) the effect of inlet temperature.


from 1% to 2.5% while the values of heat transfer enhancement are from 39% to 56% for inlet temperature from 60 to 80 1C.This refers to nanofluid volume concentration affected more than inlet temperature on heat transfer enhancement, butsignificantly of using all of them. Fig. 9 demonstrates that the experimental and simulation data agreed with otherinvestigator data to be validated. Fig. 9a shows the friction factor results from the experimental and simulation work andexperimental data of [15,17] and the curve fitting data of [16]. The present work agreed with data of [15,16] with deviation,not more than 9% because their data has been done on Al2O3 nanofluid, but the present work has been done on SiO2

nanofluid. On the other hand, Fig. 9b shows the validation of Nusselt number from experimental and simulation work withcurve fitting data of [20]. It seems that there is a good agreement with the maximum deviation 7% may be related to sizediameter all type of nanofluids.

5. Conclusions

The friction factor and forced convection heat transfer enhancement of SiO2 suspended in water carried out. Significantincreases of the friction factor and heat transfer enhancement observed with the volume concentration of nanoparticlesaddition. The maximum values of friction factor increased to 22% for SiO2 nanoparticles dispersed in water with 2.5% volumeconcentration. A highest Nusselt number enhance up to 40% obtained for SiO2 nanoparticles in water. The simulation resultsshowed that the friction factor and Nusselt number action of the nanofluids were highly depended on the volumeconcentration and Reynolds number. On the other side, the experimental study concluded that there was a significantincreasing in Nusselt number and agreeing with the numerical study. The friction factor decreases with increasing of volumeflowrate and the inlet temperature. The friction factor at Reynolds number less than 1000 has a maximum deviation 82% butafter that the maximum deviation becomes 40%. A highest Nusselt number enhancement is 56% obtained for SiO2

nanoparticles in water as a base fluid. The experimental results showed that the Nusselt number behavior of the nanofluids

0

0.1

0.2

0.3

0.4

0.5

0 500 1000 1500 2000

Fric

tion

Fact

or

Reynolds Number

Present EXP Work (pure water)

Present CFD Work (pure water)

EXP data [17]

EXP data [15]

Present EXP Work (1%SiO2-W)

Present CFD Work (1%SiO2-W)

Darcy Eq (13)

Curve fitting of [16]

2

7

12

17

22

0 500 1000 1500 2000

Nus

selt

Num

ber

Reynolds Number

CFD Work (pure water)

EXP Work (pure water)

EXP Work (1%SiO2-W)

CFD Work (1%SiO2-W)

Shah-London Eq (14)

Sieder-Tate Corelation [21]



Fig. 9. Validation of (a) friction factor and (b) Nusselt number.


was highly depended on the volume concentration and the volume flowrate. The Nusselt number of 1% SiO2 nanofluid at80 1C has 52% deviation than pure water but 32% at 60 1C. There is a good agreement among present data of friction factorwith data of [15–17] and Nusselt number data with data of [18,20]. The results proved that SiO2 nanofluid have highpotential for hydrodynamic flow and heat transfer enhancement and are highly appropriate to industrial and practicalapplications. When adding nanoparticles to the base fluid the possible enhancement of car engine cooling rates will improveotherwise the engine heat remove or reduce size cooling system. The small cooling systems have led to benefit almost everyaspect of car performance and increased in fuel economy.

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