Synthesized CuFe2O4/SiO2 nanocomposites added to water/EG

Synthesized CuFe

₂

O

₄

/SiO

₂

nanocomposites added to water/EG:

Evaluation of the thermophysical properties beside sensitivity analysis &

EANN

Arash Karimipour

^a

, Seyed Amin Bagherzadeh

^a

, Marjan Goodarzi

^b

, Abdulwahab A. Alnaqi

^c

, Mehdi Bahiraei

^d

, Mohammad Reza Safaei

^e,f,⇑

, Mostafa Safdari Shadloo

^g

aDepartment of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

bYoung Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

cDepartment of Automotive and Marine Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training, Kuwait

dDepartment of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran

eDivision of Computational Physics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

fFaculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

gCORIA-UMR 6614, CNRS-University & INSA of Rouen, Normandie University, Rouen 76000, France

a r t i c l e i n f o

Article history:

Received 10 June 2018

Received in revised form 23 August 2018 Accepted 23 August 2018

Keywords:

Enhanced artificial neural network Thermo-physical properties Electrical conductivity Nanocomposites

a b s t r a c t

The new nanocomposite material of CuFe2O4(copper ferrite) nanoparticles coated by SiO2is synthesized.

Then, this newly generated nanocomposite is dispersed in water/ethylene glycol (60:40) to make a new homogeneous nanofluid in order to avoid settling and agglomeration. Through suitable accurate experi- ments, density, viscosity and electrical conductivity of the mixture are measured at various temperatures and nanoparticles concentrations. Besides we empirical correlations for the same parameters developed via the curve fitting method. To have a better statistical view, the optimization procedure based on the enhanced artificial neural network (EANN), developed at present study, is performed. Furthermore, according to the obtained empirical results, the sensitivity analysis is provided and the margin of devia- tions is represented for each proposed correlation. Generation, stabilization and measuring the density, viscosity and electrical conductivity of the newly mentioned nanofluid, make present work different from the previous ones in this field. The highest amount of relative electrical conductivity is observed at T = 75°C andu= 0.02 (g/mL); however, the case of T = 30°C andu= 0.02 (g/mL) represents the maximum value of relative viscosity. Moreover, density is decreased by temperature augmentation, through all cases.

Ó2018 Elsevier Ltd. All rights reserved.

1. Introduction

Nowadays, nanoscience represents an important branch of the new progressed sciences. Thermal and electrical industrials beside other possible mechanical performances lead to have many research topics in this way. Once you disperse a little value of nanoparticles through a liquid, the archived substance will be a homogeneous fluid called nanofluid [1–9]. Several works illus- trated the improved rate of heat transfer using nanofluids; how- ever, it depended on the type of nanoparticles as well as the base fluid. It encouraged the scientists to make the new ones such as the hybrid nanofluids to have the better increase in the mixture

thermal conductivity; although, it needed the new approaches to produce these new mixtures[10–23].

The concentration of nanoparticles and the nanofluid tempera- ture should be mainly considered, as the nanofluid behavior is significantly affected by these factors. However, several other ones can be introduced like nanoparticles dimeter, nanoparticles shape, usage of surfactant and other possible physical and chemical con- ditions. Many works have been conducted to show the volume/

mass fraction and temperature influences on the hydrodynamic and thermal properties of the mixture[24–31].

The value of different thermo-physical properties of a nanofluid must be measured and reported while a new nanofluid is prepared;

among them, the thermal conductivity, viscosity, density, specific heat and electrical conductivity are the most important variables which must be involved. It should be mentioned that there are some detours in achievements between the various papers https://doi.org/10.1016/j.ijheatmasstransfer.2018.08.112

0017-9310/Ó2018 Elsevier Ltd. All rights reserved.

⇑ Corresponding authors at: Division of Computational Physics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

E-mail address:cfd_safaei@tdtu.edu.vn(M.R. Safaei).

Contents lists available atScienceDirect

International Journal of Heat and Mass Transfer

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j h m t

concerned the thermo-physical properties of a nanofluid via an experimental work especially for the hybrid mixtures [32–40].

That can be due to different conditions such as stabilizing and dis- persing of an each experiment and probably errors through it. On the other hands, the proposed correlations in some articles would help researchers to have a suitable estimation for the nanofluid properties at various working conditions. The high cost of each experiment can be a drawback of such these works which makes a real problem for ones to study and generate the new kinds of nanofluids. Therefore, using computer-based estimations and solutions seems necessary in nanofluids research area[41–63].

The new type of nanoparticle consists of CuFe2O4 (Copper ferrite) coated by SiO₂is synthesized at present study. The reason for choosing copper ferrite as the studied nanoparticle is due to its broad application in different industries as a useful spinel ferrites nanomaterial. The layer of SiO₂around the nanoparticle of CuFe₂O₄ leads to make a hydrophilic surface for it. Then, it is tried to disperse the material in a base fluid composed of water- ethylene glycol (60:40) to make a homogeneous mixture. In the following, the density (

q

), electrical conductivity (

r

) and dynamic viscosity (

l

) of the mixture are measured at various temperatures as

Fig. 1.TEM image for dry nanoparticles of CuFe2O4/SiO2.

Table 1

Dynamic viscosity validation.

Present work Sundar et al.[29]

T (°C) l(m Pa s) T (°C) l(m Pa s)

30 2.04 30 2.6

45 1.41 40 1.21

60 1.06 60 0.9

Table 2

Density of the mixture atu= 0.02 (g/mL).

T (°C) q^(g/cm3)

20 1.1158

30 1.0960

45 1.0743

60 1.0534

75 1.0438

T ( C)

(mPa.s)

0 1 2 3 4

(gr/mL) (gr/mL) (gr/mL)

T ( C)

30 45 60 75

30 45 60 75

(s/cm)

0 100 200 300 400 500 600

(gr/mL) (gr/mL) (gr/mL)

Fig. 2.l^&rwith T at various concentrations.

Nomenclature

EANN enhanced artificial neural network CuFe2O4 copper ferrite

EG ethylene glycol

n number of weights and biases t network targets

RE relative electrical conductivity RV relative viscosity

Greek symbols

a

outputs of the network w network weights and biases

l

dynamic viscosity (m Pa.s)

q

density (g cm³)

r

electrical conductivity (

l

^{s cm1)}

u nanoparticles concentrations (g mL¹)

T = 30, 45, 60, 75 (°C) and nanoparticles concentrations asu= 0, 0.005, 0.01, 0.02 (g/mL) by the appropriate experiments; besides proposing the suitable correlation for each one. Finally, an opti- mization procedure test is presented, according to the achieved experiment results via the ‘‘enhanced artificial neural network”

(EANN) and the sensitivity analysis.

2. Experiments

2.1. Generate the nanofluid sample

CuFe2O4/SiO2 nanoparticles were provided by solvothermal approach in Razi Chemistry Research Center (RCRC), Shahreza Branch, Islamic Azad University, Isfahan, Iran while the mixture of distilled water/ethylene glycol was prepared as the base fluid.

Fig. 1shows the TEM image for the dry CuFe2O4/SiO2nanoparticles.

Using a magnetic stirring for 2.5 h, corresponds to produce the stable homogeneous nanofluid. However, the ultrasonic waves from Hielscher UP200St-Germany homogenizer were applied (50 Hz, for 2 h) to avoid agglomeration and adherence.

2.2. Measurement devices and validation

ULA spindle of cylindrical viscometer of LVDV III Ultra- Brookfield-U.S.A was used to measure the dynamic viscosity with

±1% accuracy. Moreover, the RS232 conductivity meter was chosen to measure the electrical conductivity. In the following, the valida- tion of present study with Sundar et al.[29]is presented inTable 1.

They reported the viscosity of stabilized ethylene glycol and water mixture/Al2O3 nanofluid at various temperatures. Acceptable agreements are observed between the results.

Measuring the density of liquids is done by dividing mass by volume. This is done through a glass container with a specific vol- ume called pycnometer which is equipped with a thermometer.

The volume of liquid increases by heating; therefore, it rises from the capillary tube of the pycnometer and overflows. By overflowing the liquid from the pycnometer at higher temperatures, more liq- uid exits. After this phenomenon and cleaning pycnometer wall, the liquid inside the pycnometer is weighed and for calculating density, the empty pycnometer mass is deducted from the total mass to obtain the liquid mass. Therefore, the mass of liquid is divided by liquid volume. The liquid volume is the pycnometer volume filled up to the mark (for example 10 ml).

2.3. Measured values of

q

^,

l

^&

r

Table 2 represents the density of the mixture composed by CuFe2O4/SiO2nanocomposites dispersed in water/ethylene glycol (60:40). It is observed that density is decreased by augmenting the temperature.Fig. 2shows the electrical conductivity & viscos- ity versus temperature at different concentrations of nanoparticles (u). Less viscosity values are achieved at higher temperatures. That trend is overserved at eachu, which implies the weak interaction

(gr/mL)

(mPa.s)

0 1 2 3 4

T=30^oC T=45^oC T=60^oC T=75^oC

(gr/mL)

0.000 0.005 0.010 0.015 0.020

0.000 0.005 0.010 0.015 0.020

(s/cm)

0 100 200 300 400 500 600

T=30^oC T=45^oC T=60^oC T=75^oC

Fig. 3.l^&rwith concentrations at various T.

T ( C) RV

0.5 1.0 1.5 2.0 2.5

(gr/mL) (gr/mL) (gr/mL)

T ( C)

30 45 60 75

30 45 60 75

RE

1 2 3 4 5 6 7 8 9 10

(gr/mL) (gr/mL) (gr/mL)

Fig. 4.RV & RE at various values of T.

forces among the liquid molecules at larger temperatures. The vis- cosity at 30°C is completely different from one another; so that, its greatest value is achieved atu= 0.0 2 (g/mL). However, the plots corresponded to u= 0.0 05 andu= 0.0 1 are almost the same. It can be said that higher nanoparticles agglomerations are observed at larger concentrations which means the weaker influences of temperature on the interactions of the particles and their corre- sponded attractive Van der Waals forces. The second plot of Fig. 2implies the positive effect of temperature on electrical con- ductivity, especially at higher u. An interesting point would be the neglecting influence of temperature on

r

in the absence of nanoparticles (u= 0) which illustrates that electrical conductivity is mainly affected by the dispersed nanoparticles inside the mix- ture, and not from the base fluid molecules.

Viscosity and electrical conductivity with variousuat different temperatures are represented inFig. 3. The increasing effect ofuon viscosity is hardly observed fromu= 0 tou= 0.0 1. However, that enhancement is occurred more severely at u= 0.01 to u= 0.02.

Also, stronger resistance to the motion of fluid layers is provided by more viscosity, which considerably affects the transferred momentum among the fluid layers. Moreover, electrical conductiv- ity increases withuat each values of temperature; however, that event is strongly occurred at T = 75°C, compared to the lower val- ues of temperature. Hence, the greatest value of electrical conduc- tivity is achieved at the uppermost values ofu& T.

Fig. 4represents the relative viscosity (RV =

l

nf@T0/

l

basefluid@T0) and relative electrical conductivity (RE =

r

nf@T0/

r

basefluid@T0) at

various temperatures. Temperature causes a decrease on relative viscosity; as it is occurred more severely at larger temperatures.

However, higherucorresponds to higher RV and RE. That’s while, temperature enhances the relative electrical conductivity values, despite relative viscosity. As a result, the most value of relative electrical conductivity is achieved for T = 75°C and u= 0.02 (g/mL) and the highest relative viscosity is provided at T = 30°C andu= 0.02 (g/mL) (please seeFig. 5).

3. Numerical studies 3.1. Correlations

To estimate the values of electrical conductivity and dynamic viscosity at the range of 0.0 <u< 0.02 (g/mL) & 30 < T < 75 (°C), the following correlations are suggested, based on the achieved experimental data.

l

^¼43947802:6097

u

^{3:3239ð1=TÞ1:2107þ}22:7550ð1=TÞ^0:58910:9317 ð1Þ The absolute averaged deviation of the recent correlation equals to 3.5%, while there are just 4 points with the error larger than 5%.

r

^{¼60:8254þ525:4013}

u

^{0:4642T0:3842 ð2Þ}

The absolute averaged deviation of this correlation is 2.4%, with just 3 points with the error more than 5%. Through the mentioned (gr/mL)

RV

0.5 1.0 1.5 2.0 2.5

T=30^oC T=45^oC T=60^oC T=75^oC

(gr/mL)

0.005 0.010 0.015 0.020

0.005 0.010 0.015 0.020

RE

1 2 3 4 5 6 7 8 9 10

T=30^oC T=45^oC T=60^oC T=75^oC

Fig. 5.RV & RE at various amounts ofu.

(gr/mL)

0.000 0.005 0.010 0.015 0.020

-10 -8 -6 -4 -2 0 2 4 6 8 10

T=30 ^oC T=45^oC T=60^oC T=75^oC

(gr/mL)

0.000 0.005 0.010 0.015 0.020

Margin ofdeviation for (%) Margin ofdeviation for (%)

-10 -8 -6 -4 -2 0 2 4 6 8 10

T=30 ^oC T=45^oC T=60^oC T=75^oC

Fig. 6.Proposed correlations’ margin of deviations forl^&r^.

correlations, the errors and margin of deviations are calculated as follows:

Margine of Dev:¼

r l

Corr

l r

l

Exp

r

Exp

100 ð3Þ

Fig. 6illustrates the margin of deviations for the dynamic vis- cosity and electrical conductivity, respectively. In addition,Fig. 7 represents the experimental data and the correlation output for the both

l

^&

r

, which implies the nice results due to existence the most points around the bisector.

3.2. Enhanced artificial neural network

Many works of using of artificial neural networks (ANNs) can be addressed to estimate the nanofluid thermo-physical properties [30,31,64,65]. Several difficulties are grown from the little size of present datasets, so that the little length of training data-set makes a small-precision determination which means the predicted model cannot be improved by increasing or decreasing the amount of secret layer neurons. More secret layer neurons correspond to over-fitting which leads to have less amount of detour. That phe- nomenon is occurred due to more complex of fitted functions which is achieved at larger levels of neurons. In contrast, the less amount of secret layer neurons results to have sooner convergence through the learning algorithm and eventually, to get the inadequate fit. Hence, the conventional interpolation approaches such as linear and cubic interpolations, might be more suitable selections than ANN. However, the usual interpolation approaches might not work well while a large-precision approach is needed;

so, ANNs can be examined. Regularization might increase the pre- cision of model achieved by ANN because of the following points:

Regularization would not need a validation data-set. It can apply the whole present input-goals to examine the network.

Regularization might stop the sooner convergences from Levenberg-Marquardt backpropagation.

Regularization works according to the modification of network detour as follows,

F¼

c

^F1þ ð1

c

ÞF2 ð4Þ

F1represents the network averaged square detour and F2shows the mean square of the network weights and biases. The modified network detour performance function decreases the error of Experimental data

Correlation output

0 1 2 3 4 5

Experimental data

0 1 2 3 4 5

0 100 200 300 400 500 600

Correlation output

0 100 200 300 400 500 600

Fig. 7.Empirical results against the correlations’ output (viscosity: upper, electrical conductivity: lower).

Fig. 8.Used network architecture.

Table 3

The percentage of output detour forr(examined at non-trained data ofu= 0.005 g/mL).

Temperature (°C) Linear interpolation Cubic interpolation Levenberg-Marquardt Bayesian regularization

30 15.6736 6.8632 27.9349 2.897

45 18.2384 8.8565 16.8049 0.5565

60 21.9557 12.722 20.5655 0.0132

75 26.3444 17.4893 11.9926 2.1787

Table 4

The percentage of output detour for viscosity (examined at non-trained data ofu= 0.005 g/mL).

Temperature (°C) Linear interpolation Cubic interpolation Levenberg-Marquardt Bayesian regularization

30 3.5556 10.0556 0.0909 3.9662

45 6.5625 12.5781 37.1845 5.9229

60 8.9091 8.0682 29.6552 2.0147

75 7.5495 7.6923 61.0615 1.0714

Fig. 9.ANN Regression with Levenberg-Marquardt onr^.

Fig. 10.ANN Regression with Bayesian regularization onr^.

results as well as the weights and biases of the network in order to prevent the over-fitting; therefore, it leads to achieve the smoother fitted functions. Moreover, the modified network detour perfor- mance function would delay the convergence which corresponds to increase precision of models.

c

implies the performance ratio inside the last equation which it estimates F1 & F2 in the detour of network. It should be men- tioned that estimating a suitable performance ratio is usually diffi- cult to avoid from more network detour or over-fitting. As a result, the Bayesian regularization approach can determine the regular- ization parameters such as the performance ratio within an opti- mal manner. It chooses the suitable distributions by using the statistical techniques; more details can be found in the literature [66,67].

It is tried to estimate viscosity and electrical conductivity in present article. The used model has two inputs of concentrations of nanoparticles (u) and temperature (T); and two outputs of viscosity (m) and electrical conductivity (

r

). To do this, two conventional interpolation approaches of linear and cubic beside 2-EANNs approaches are applied. The pack of 16 inlet and outlet samples are examined from the experiments: twelve ones are applied for training data; and 4 non-trained ones (u= 0.005 g/

mL) are applied for test data. An architecture of the utilized enhanced artificial neural network has two secret and outlet layers with transfer functions of sigmoid (f¹) & linear (f²) as shown in Fig. 8. ANNs have fifteen neurons through secret domain.

Outlet detour of studied approaches containing the interpola- tions of linear and cubic beside artificial neural network with reg- ularization of Levenberg-Marquardt and Bayesian, examined the non-trained data, are illustrated for electrical conductivity and viscosity inTables 3and4, respectively. It is seen that the interpo- lations of linear and cubic show moderate detours as Levenberg- Marquardt training algorithm represents unacceptable detours. In opposite, Bayesian regularization decreases the errors, because of algorithm convergence delay of Bayesian regularization to apply the whole twelve ones.

Figs. 9and 10 show the regression diagrams of EANNs with regularization of Levenberg-Marquardt and Bayesian on electrical conductivity. Moreover, the regression plots for

l

are illustrated inFigs. 11 and 12. It is observed that the outcomes of ANNs are in contrast with the goals. The regression slope and offset must be equals to ‘‘one” & ‘‘zero” to have an ideal training so that the regression amount of ‘‘one” implies to a perfect fit.

Levenberg-Marquardt algorithm leads to have an acceptable fit along the training. The accuracy of the predicted approach for training and testing phases is preserved by Bayesian regular- ization training algorithms. When a model is developed, the out- comes are able to be predicted in the studied range of the inputs. Hence Enhanced artificial neural network interpolate among present data. The findings of approach outcomes on the dissimilar inlet amounts are presented through Figs. 13 and 14, respectively.

Fig. 11.ANN Regression with Levenberg-Marquardt on viscosity.

3.3. Sensitivity analysis

In this subsection, the sensitivities of the estimated outputs are achieved with respects to the inputs. For this purpose, the ANN presented in the previous subsection is employed to estimate the

outputs at desired input values. Afterwards, the finite central difference is utilized for obtaining the sensitivities. The sensitivi- ties of the outputs for electrical conductivity and viscosity with respect to the inputs of

u

and temperature are illustrated inFigs. 15 and 16.

Fig. 12.ANN Regression with Bayesian regularization onl^.

Fig. 13.Estimated output ofron dissimilar input amounts ofu& T. Fig. 14.Estimated output of viscosity on dissimilar input values ofu^{& T.}

4. Conclusion

Nanocomposites of CuFe2O4 coated by SiO2 were added to water/ethylene glycol so that a homogeneous mixture was achieved. Several thermo-physical properties of the mixture such as density, dynamic viscosity and electrical conductivity were evaluated at various temperatures and concentrations. Then new optimization approaches of enhanced artificial neural net- work (EANN) and sensitivity analysis were developed according to the empirical results. For this purpose, two methods of linear and cubic interpolations are developed by two EANN approaches with dissimilar training algorithms of Levenberg-Marquardt and Bayesian regularization backpropagations, respectively.

Less amounts of viscosity were achieved at higher tempera- tures; despite the positive effect of temperature on the electrical conductivity especially at higher u. The neglecting effect of temperature on electrical conductivity was observed at u= 0.

Moreover, larger concentration corresponds to larger electrical conductivity, especially at T = 75°C which means the most value for the electrical conductivity was achieved at maximum uand temperature. It should be also noted that, more concertation leads to a larger amounts of relative electrical conductivity and relative viscosity.

Conflicts of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, in the online version, athttps://doi.org/10.1016/j.ijheatmasstransfer.

2018.08.112.

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