Study on Parameters of Thermal Conductivity Enhancement in Oxide Nanofluids

International Journal of Engineering, Management & Sciences (IJEMS)ISSN: 2348 –3733, Volume-1, Issue-12, December 2014

10 www.alliedjournals.com

Abstract— A mathematical model for thermal conductivity of

metal oxides nanofluids was developed incorporating thefollowing: considering the effectsof temperature, volumefraction and size of the nanoparticle is developed and presented.For development of thermal conductivity model first we have toanalyze the brownian motion of nanoparticle .The particlessuspended in the liquid are very small, Brownian movement ofthe particles is quite possible. Using nonlinear regressionanalysis and solving this in MATLAB for find out the unknownparameter ,an empirical correlation is developed topredict thethermal conductivity of TiO2 , ZnO& Al2O3nanofluids.

The expression developed was successfully validated againstexperimental data obtained from the literature. The models wasable to comprehensively explain the enhanced thermalconductivity of nanofluids. Following the validation withexperimental data and existing models. It was found that in thisstudy that the contribution of Brownian motion of nanoparticlesto the overall thermal conductivity of nanofluids was found to bevery important parameter.

Index Terms— Thermal conductivity, nanoparticle,brownian motion, nanofluids.

I. INTRODUCTION

Heat transfer is one of the most important processes in manyindustries. Passive enhancement methods such as enhancedsurfaces are usually employed in thermo fluidsystems.Conventional heat transfer fluids widely used in industries(e.g. water, ethylene glycol and oil) However, inherently lowthermal conductivity is a primary limitation in developingenergy-efficient heat transfer fluids.Therefore, thedevelopment of advanced heat transfer fluids with higherthermal conductivity and improved heat transfer is in strongneed to enhance heat transfer. The use of additives is anothertechnique applied to enhance the heat transfer performance ofbase fluids. In general, most of the solids have better heattransfer properties compared to traditional heat transfer fluids.It is found an effective way of improving the thermalconductivity of fluids is to suspend small solid particles in thefluids The suspended metallic or nonmetallic particles changethe transport properties and heat transfer characteristics of thebase fluid. In the past, solid particles of micrometer ormillimeter magnitudes were mixed in the base liquid. On the

Manuscript received December 15, 2014.Khagendra Kumar Upman, M.Tech Scholar, Department of

Mechanical Engineering, AIET JaipurAmit Srivastava, M.Tech Scholar, Department of Mechanical

Engineering, AIET Jaipur

basis of this concept Conventional Soild–Liquid Suspensiontechnique used by Maxwell (1873), Maxwell dispersedmicrometer sized particles in heat transfer fluids. The majorproblem with suspensions containing micrometer sizedparticles is the rapid settling of micro particles. If the fluid iskept circulating to prevent particle settling, micrometer-sizedparticles would wear out pipes, pumps, and bearings soabrasion, fouling of components and clogging of flowpassages these problems are also occurred . By usingmicrometer sized particle itsconclude that particle size is ofprimary importance in developing stable and highlyconductive nanofluids. In 1995, Choi created the concept ofnanofluids (Choi, 1999) by suspending nano-sized metals,metal oxides, metal carbides and carbon nanotubes inconventional base fluids (such as water, ethylene glycol andoil). Mainly due to their smaller sizes, nanofluids offer lot ofadvantages over conventional heat transfer fluids, such as staysuspended much longer and possess a much higher surfacearea (The surface/volume ratio of nanoparticles is 1000 timeslarger than that of microparticles) Furthermore, becausenanoparticles are so small, they may reduce erosion andclogging . Other benefits envisionedfor nanofluids includedecreased demand for pumping power show significantenergy savings. These novel properties make nanofluidsattractive to various industries requiring heat transferapplications - such as microelectronics, transportation,biomedical, micro fluids, nuclear, automobile, generation ofpower and x-ray etc.

II. MATERIALS FOR NANOPARTICLES AND DIFFERENT BASEFLUIDS

Nanoparticles are the particles with average size below 100nm. Nanoparticles have superior Electrical, mechanical,optical, magnetic and thermal properties than that ofconventional bulkmaterials due to coarse grain structure withenhanced heat transfer area.

A. Nanoparticle materialsIn general, most of the solids have better heat transfer

properties compared to traditional heat transfer fluids .It isfound an effective way of improving the thermal conductivityof fluids is to suspend small solid particles in the fluids . Manydifferent particle materials are used for nanofluid preparation.Al2O3, CuO, TiO2, SiC, TiC, Ag, Au, Cu, and Fenanoparticles are frequently used in nanofluid research(Das etal., 20007; Choi, 1999; Yoo et al., 2007; Zhu et al., 2006 andYu et al., 2009). Carbon nanotubes are also utilized due to

Study on Parameters of Thermal ConductivityEnhancement in Oxide Nanofluids

Khagendra Kumar Upman, Amit Srivastava

Study on Parameters of Thermal Conductivity Enhancement in Oxide Nanofluids


their extremely high thermal conductivity.

B. Base fluidsBase fluids mostly used in the preparation of nanofluids are

the common working fluids of heat transfer applications; suchas, water, ethylene glycol and engine oil. In order to improvethe stability of nanoparticles inside the base fluid, someadditives are also added to the mixture in small amounts. (Daset al., 2007, Choi, 1999)

III. PRODUCTION METHODS

A. Production of NanoparticlesFabrication of nanoparticles can be classified into two

broad categories: (Kimoto etal, 1963; Granqvist and Buhrman, 1976; Gleiter 1989).(i)) Physical synthesis and(ii) Chemical synthesis.Production techniques of nanofluids as follows:(i)Physical SynthesisMechanical grinding: The grinding is a fine particle

production method by applying mechanical energy on thesolid materials to break the bonding between atoms ormolecules. It is the oldest technique. Nowadays the particlesize requirement for grinding is getting finer and finer. Thegrinding limit has long passed the submicrons and reaches theNano sized range in recent days. This method is not soused.(Wei Yu and HuaqingXie)

Inert gas condensation technique: In this method, thenanoparticle is made in the gas phase by creating a conditionof supersaturation. The condition in the preparation chamberis made such that the solid phase is more stable than the gasphase. At this point the cluster nucleates

and deposits from the gas phase. The deposition will lead toaggregation and can be controlled suitably by surfactants orsuitable protecting agents. Recent study of nanomaterialsprepared by inertgas condensation using ultra high vacuumchamber.(SRAMASAMY, P THANGADURAI)

(ii) Chemical Synthesis:Chemical precipitation: Traditional chemical precipitation

methods are frequently used in recent years Homogeneouschemical precipitation method is often consideredeconomically viable for preparation of mono disperse metaloxide particles of different shapes and sizes. The method alsoprovides better control of chemical and morphologicalcharacteristics.

Chemical vapor deposition: CVD is a well-known processin which a solid is deposited on a heated surface via achemical reaction from the vapor or gas phase, first moleculesand atoms are separated by vaporization and then allowed todeposit in a carefully controlled and orderly manner to formnanoparticles.CVD classified by method of activation ofreaction(Jong he park) as Plasma CVD, Laser CVD andphoto-laser CVD. In plasma CVD, the reaction is activated byplasma at high temperature .In laser CVD reaction occurswhen laser thermal energy heats an absorbing substrate. Inphoto-laser CVD, the chemical reaction is induced by ultraviolet radiation which has sufficient photon energy, to breakthe chemical bond in the reactant molecules.Now a day’s

chemical Vapor Synthesis (CVS) is a modified ChemicalVapor Deposition (CVD) method where the processparameters are adjusted to form nanoparticles instead of film.(Nanoparticle process technology NPTT)

Micro-Emulsions: Microemulsions are colloidal‘nano-dispersions’ of water in oil (or oil in water) stabilizedby a surfactant film (M. Arturo Lopez-Quintela). Thesethermodynamically stable dispersions canbe considered astruly nanoreactors which can be used to carry out chemicalreactions and, in particular, to synthesize nanomaterials. Themain idea behind this technique is that by appropriate controlof the synthesis parameters one can use these nanoreactors toproducetailor-made products down to a nanoscale level withnew and special properties.

Spray pyrolysis: Spray pyrolysis is the aerosol process thatatomizes a solution and heats the droplets to produce solidparticles. It is very convenient method for synthesis ofnanoparticles that provide good control of particle size andnarrow particle size distribution and it is suitable for usage ofavailable and cheap precursors.(George Biskos)

B. Production of Nanofluids:

There are mainly two methods of nanofluid production,namely-

1. Two-step technique and2. One-step technique

In the two-step method, nanoparticles were firstseparately produced, and then the prepared nanoparticleswere dispersed in the base fluid with the assistance of variousphysical treatment techniques, including the stirrer, theultrasonic bath, the ultrasonic disruptor, and the high-pressurehomogenizer. Two-step technique is play an important rolewhen mass production of nanofluids is considered. The maindisadvantage of the two-step technique is that thenanoparticles form clusters during the preparation of thenanofluid which prevents the proper dispersion ofnanoparticles inside the base fluid. Eastman et al. (1997),Wang et al. (1999) and Lee et al.(1999) have used ultrasonicmethod to prepare Al2O3 nanofluids. Similarly Murshed et al.(2005) employed the same method to prepare TiO2nanofluids. It was observed that this technique holds good forthe preparation of nanofluids having oxides nanoparticles.While for metallic nanoparticles this method was lesssuccessful.

One-step method combines the production ofnanoparticles and dispersion of nanoparticles in the base fluidinto a single step .Magnetron sputtering process is generallyapplied in one step technique. This technique is moreadvantageous for considering dispersion characteristics ofnanofluids produced, better than those produced withtwo-step technique. The main disadvantage of one-steptechniques is that they are not proper for mass production incomparison to two step method, which limits theircommercialization. Choi et al developed a Direct Evaporationtechnique for the production of nanofluids in one step. Zhu etal. (2004) presented a one-step chemical method for thepreparation of Cu nanofluids.



IV. OBJECTIVES:

Specific objectives include:

1. To evaluate the effects of different properties of particleand experimental conditions on nanofluids thermalconductivity.

2. To evaluate the accuracy of existing models for thermalconductivity of nanofluids/ two phase system by comparingthe calculated values with available experimental data.

3. Development of new empirical model for the thermalconductivity of metal oxides (Al2O3,ZnO& TiO2water asbase fluid ) nanofluid by using wide range of availableexperimental data in literature.

The research conducted under this paper was aimed atdeveloping a more comprehensive model including criticalfactors responsible for the abnormal thermal conductivitybehavior of nanofluids. Considering the factors like effects oftemperature, volume fraction and size of the nano-particle anempirical correlation was developed. To understand theaccuracy of the predicted results and relative improvement inthe predictability, the results from developed model werecompared to experimental observation and predictionobtained from other models in existence. After that, anevaluation was carried out to develop an insight of thedependence of effective thermal conductivity of nanofluids onthe properties of nanoparticles and base fluid.

V. EFFECTS OF SOME PARAMETERS ON THERMALCONDUCTIVITY OF NANOFLUIDS

A. Thermal conductivity variation at differentconcentrations:

There are many studies in the literature about the effect ofparticle volume fraction on the thermal conductivity ofnanofluids. Masuda et al. measured the thermal conductivityof nanofluids containing Al

2O

3(13 nm), SiO

2(12 nm), and

TiO2

(27 nm) nanoparticles. This is the first experimentalstudy regarding the thermal conductivity of nanofluids. Waterwas used as the base fluid and a two-step preparation methodwas utilized. An enhancement as high as 32.4% was observedfor the effective thermal conductivity of 4.3 vol. %Al2O3/water nanofluid at room temperature. Lee et al.studiedthe room temperature thermal conductivity of nanofluids bydispersing Al2O3 (38.5 nm) and CuO (23.6 nm) nanoparticles.a linear relationship was observed between thermalconductivity and particle volume fraction (thermalconductivity increases with particle volume fraction). Highestenhancement was 20%, which was observed for 4 vol.%CuO/ethylene glycol nanofluid.

Figure 1 Effect of volume concentration(Al2O3/H2O) onthermal conductivity ratio [lee. et. al]

Figure 2 Effect of volume concentration(CuO/H2O) onthermal conductivity ratio [lee. et. al ]



Figure 3Effect of volume concentration (Al2O3/CuO withH2O) on thermal conductivity ratio [lee. et. al]

A linear relationship was observed between thermalconductivity and particle volume fraction (thermalconductivity increases with particle volume fraction). Highestenhancement was 20%, which was observed for 4 vol.%CuO/ethylene glycol nanofluid.A similar study was performed by Wang et al., whoexamined the thermal conductivity performance of nanofluidswith Al

2O

3(28 nm) and CuO (23 nm) nanoparticles.

Figure 4 Thermal conductivity ratio (Al2O3) at differentbas fluid [Wang. et. al]

Particle volume fraction is a parameter that is investigated inalmost all of the experimental studies and the results areusually in agreement qualitatively. Many researchers reportincreasing thermal conductivity with increasing particlevolume fraction and the relation found is usually linear. Butthere are lots of differences between experimental data of

researchers and reason behind this difference is as all authorshave considered various factors for their model formulation.These factors include fluid temperature, phonon dispersion,size and shape of nanoparticle, clustering of particles,Brownian motion of nanoparticles, liquid layering at theparticle surface, agglomeration and interfacial layer betweenthe fluid and the particle.

Thermal conductivity variation due to particle size:Particle size is another important parameter of thermalconductivity of nanofluids. It is possible to producenanoparticles of various sizes, ranging between 5 to 100 nm.Mintsa et al. measured the thermal conductivity ofAl

2O

3/water nanofluids. Two different sizes of Al

2O

3nanoparticles were used in the experiment (36 and 47 nm).Particle volume fraction was varied between 0 and 18% andtemperature was varied between 20 and 50°C. It was observedthat the thermal conductivity enhancements were nearly thesame for the two different particle sizes of Al

2O

3nanoparticles at room temperature. However, at highertemperatures, Al

2O

3/water nanofluid with smaller particles

showed higher enhancement.

Figure 5 Effect of particle size (Al2O3 ) on thermalconductivity [Mintsa et. al ]

Murshed et alreported in their paper various results of theeffective thermal conductivity of nanofluids as a function ofnanoparticle volume fraction from various research groups. It



was observed that for the same nanofluids, different groupswith different nanoparticle size report differentenhancements. The result was summarized in Fig. 6 As itdepicts that Al2O3 (33 nm) / Water nanofluid show 29 %enhancement for 5 % volume fraction while samenanoparticle of diameter 20 nm at 1 % volume fraction shows16% enhancement. Similarly CuO nanoparticle of dimension36 nm and 50 nm when mixed with water shows anenhancement of 60 % for 5 % volume fraction and 17 % for0.4 % volume fraction respectively.

Figure 6Comparison of experimental results of theenhanced thermal conductivity of nanofluids [Murshed etal.]

Tenget. alproduced nanofluid from direst synthesis methodultrasonic vibration was used for dispersing the differentparticle sizes (20, 50, and 100 nm) into four weight fractions(0.5, 1.0, 1.5, 2.0 wt %)(figure 7 )

Figure 7Comparison between the experimental data andthe results of regression of the thermal conductivity ratio

of Al2O3/water nanofluid

The results shown in Figs. 7 reveal the comparison betweenthe experimental data and the results of regression of thethermal conductivity ratio of Al2O3/water nanofluid thatchange with different particle sizes under differenttemperatures and weight fraction. The nominal particle sizesof the nanoparticles were 20, 50, and 100 nm respectively.The experimental results indicate that increase in temperatureand shrinkage of particle size enhances the thermalconductivity ratio of nanofluid. . As the temperature of thethree samples rose to 30C, the thermal conductivity ratios could be enhanced by5.1e12.8%, 1.4e6.9%, and 0.7e5.3%, respectively.

Experimental results depicts that thermalconductivity of nanofluid increases when particleconcentration is increased but the experimental valuesobtained increase in a nonlinear fashion. The general trend inthe experimental data is that the thermal conductivity ofnanofluids increases with decreasing particle size. This trendis theoretically supported by two mechanisms of thermalconductivity enhancement; Brownian motion of nanoparticlesand liquid layering around nanoparticles. However, there isalso a significant amount of contradictory data in the literaturethat indicate decreasing thermal conductivity with decreasingparticle size. In fact, for the case of nanofluids with Al

2O

3nanoparticles, such results are more common than the resultsshowing increasing thermal conductivity with decreasingparticle size. Decreasing thermal conductivity withdecreasing particle size may be the result of severe clusteringof nanoparticles in the associated samples.



Thermal conductivity variation with different Base fluids:Wang et al.,(figure 8) Al

2O

3and CuO nanoparticles were

used to prepare nanofluids with several base fluids; water,ethylene glycol, vacuum pump fluid, and engine oil.

Figure 8Thermal conductivity as a function of volumefraction of (a) Al2O3 and (b) CuO nanoparticles indifferent fluids [Wang et. al.]

According to experimental result with Al2O

3nanoparticles,

the highest thermal conductivity ratio was observed whenethylene glycol was used as the base fluid. Engine oil showedsomewhat lower thermal conductivity ratios than ethyleneglycol. Water and pump fluid showed even smaller ratios,respectively. With CuO nanoparticles, only ethylene glycol-and water-based nanofluids were prepared and it is interestingto note that they showed exactly the same thermalconductivity ratios for the same particle volume fraction.

The effect of the base fluid on the thermal conductivity ofnanofluids was also analyzed by Xie et al. (figure 9)Nanofluids with Al

2O

3nanoparticles were prepared by using

different base fluids; deionized water, glycerol, ethyleneglycol, and pump oil. In addition, ethylene glycol-water andglycerol-water mixtures with different volume fractions werealso used as base fluids and the variation of the thermalconductivity ratio with thermal conductivity of the base fluidmixture was examined.

Figure 9Enhanced thermal conductivity ratio of Al2O3nanofluid with different base fluid (Xie et al)

It was seen that, thermal conductivity ratio decreased withincreasing thermal conductivity of the base fluid.

Thermal conduction variation at different temperature:The temperature dependence of the thermal conductivity ofAl2O3(38.4 nm)/water and CuO (28.6 nm)/water nanofluidswas studied by Das et al.

Figure 10 Temperature dependence of thermalconductivity enhancement



Figure 10 shows the enhancement of thermal conductivity ofAl2O3 based nanofluids with temperature. It is interesting tosee both for 1% and 4% volume. Particle concentrations thereare a considerable increase in the enhancement from 21°C to51°C. With 1% particles at room temperature ~21°C. Theenhancement in only about 2%, but at 51°C this valueincreases to about 10.8%. The measurement with 4%concentration shows enhancement goes from 9.4% to 24.3%with temperature rising from 21°C to 51°C. Thus it can besaid that the enhancement of thermal conductivity shows adramatic increase with temperature and the rate of thisincrease depends on the concentration of nano particles.

Li and Peterson also investigated the effect oftemperature (figure 11) on thermal conductivity of CuO (29nm)/water and Al

2O

3(36 nm)/water nanofluids.

Figure 11effect of temperature on thermal conductivity ofAl2O3 (36 nm)/water nanofluids [Li and Peterson]

For both nanofluid types, it was observed that at a constantparticle volume fraction thermal conductivity ratio increasedwith temperature. In addition, it was noted that forAl

2O

3/water nanofluid, the dependence of thermal

conductivity ratio on particle volume fraction became morepronounced with increasing temperature. A regressionanalysis based on the experimental data showed that particlevolume fraction dependence of thermal conductivity is muchhigher than the temperature dependence.

Effect on Viscosity of Nanofluids:Choi et. al performed an experiment to measure effectiveviscosity of Al2O3 using a viscometer of oscillating type , theeffective viscosities of Al2O3 nanofluids with lowconcentrations from 0.01 to 0.03 vol % are measured at thetemperature range from 21OC to 39OC .

Figure 12Effective viscosities of Al2O3-nanofluids withlow concentration from 0.01 to 0.3 vol. % as a function oftemperature. [Choi. et. al ]

Figure 12 shows the effective viscosities of Al2O3-nanofluidswith various concentrations of Al2O3 nanoparticles as afunction of temperature as effective viscosity of Al2O3 - Waternanofluids decreases with increasing temperature.

Models of the effective viscosity of nanofluids based on theexperimental data are limited to certain nanofluids. Masudaet al. were the first to measure the viscosity ofSeveral water-based nanofluids for temperatures rangingfrom room condition to 67oC. Wang et al. obtained, usingthree different preparation methods, some data for thedynamic viscosity of Al2O3–water and Al2O3–ethylene glycolmixtures at various temperatures.Because the formulas such as the one proposed by Einsteinand later improved by Brinkman and Batchelorunderestimate the viscosity of the nanofluids when comparedto the measured data, Maiga et al. performed a least-squarecurvefitting of some experimental data of Wang et al.including Al2O3 in water and Al2O3 in ethylene glycol.Table 1 illustrates a summary of the viscosity models at room



temperature based on the experimental data available in theliterature.Table 1 Summary of Viscosity Models at Room TemperatureBased on experimental data

Moreover, Fig. shows a comparison of the relative dynamicviscosity of Al2O3–water nanofluid from various sources atroom temperature.

Figure 13 Relative viscosity measurement as a function ofthe volume fraction, ϕ, at atambient temperature(Al2O3–water nanofluid). (Reprinted from K. Khanaferand K. Vafai.2011, A critical synthesis of thermophysicalcharacteristics of nanofluids, InternationalJournal ofHeat and Mass Transfer 54, 4410–4428, Copyright 2011

MODELS USED FOR THERMAL CONDUCTIVITY OF NANOFLUIDS :A large body of literature has been contributed to thetheoretical modeling of effectivethermal conductivity of liquids containing suspended solidparticles but currently, there is no reliable theory to predict theanomalous thermal conductivity of nanofluids. From theexperimental results it is concluded that the thermalconductivity of nanofluids depends on various parameterslike thermal conductivities of the base fluid and thenanoparticles, the volume fraction, the surface area, and theshape of the nanoparticles, and the temperature. There are notheoretical formulas currently available to predict the thermalconductivity of nanofluids satisfactorily.There are severalmodels presented by researchers for calculating the effectivethermal conductivity of nanofluids these are discussed below.Maxwell Model (1876): Early attempts to explain thisbehavior have made use of the classical model of Maxwell.Maxwell was one of the first persons to investigateconduction analytically through a suspension particle.

It was one of the classical models present to determine thethermal conductivity ratio of nanofluids. This model relates

)(2)(22

fppfp

fppfp

f

eff

kkkkkkkk

kk



the thermal conductivity of spherical particles, base fluid andthe volume fraction. Although the model was presented longback still it is used for comparing the values predictedexperimentally. It should be noted that even given its form,Maxwell’s equation is only afirst-order approximation andapplies only to mixtures with low particle-volumeconcentrations.Bruggeman Model (1935):Bruggemanproposed a model toanalyze the interactions among randomly distributedparticles. For a binary mixture of homogeneous sphericalinclusions, the Bruggeman model gives

Where

This model can be applied to spherical particles with nolimitations on the concentration of inclusions. For low solidconcentrations, the Bruggeman model results in almost thesame results as the Maxwell model but as concentration goeson increasing its values deviate from values of Maxwellmodel.Hamilton and Crosser Model :(1962) Hamilton and Crosser(1962) proposed a model for liquid-solid mixtures ofnon-spherical particles. The thermal conductivity, in whichthe ratio of conductivity of the solid and fluid phases is largerthan 100 can be expressed as follows:

It introduced a shape factor n which is given as n =3

where

Ψ is the sphericity.For cylindrical shape nanoparticles Ψ = 6

For spherical shape nanoparticles Ψ = 3Yu and Choi Model (2003): They modified the Maxwellmodel and included the effect of nanolayer and it can predictthe presence of nano layers of thickness less than 10 nm.However it fails to predict the behavior of general oxide andmetallic based nanofluids.

Wang et. al:Wang et al proposed a statistical structural modelto determine the macroscopic characteristics of clusters, andthen the thermal conductivity of nanofluids can be estimatedaccording to the existing effective media approximationtheory. The proposed statistical model was sound in physicalconcepts and potentially useful as aneffective tool forscreening and optimizing nanofluids as advanced workingfluids.

4

])32()13[(41 f

fpppeff

kkkk

))(992(2)32()()13( 2222

f

pppp

f

pp k

kkk

)()1()()1()1(

pfpfp

pfpfp

f

eff

kkknkkknknk

kk

3

3

)1)((2)1)((22

fppfp

fppfp

f

eff

kkkkkkkk

kk



Table 2 A brief summary of the currently available theoretical models for predicting the effective thermal conductivityof nanofluid



CONCLUSION

From the review of above literature following conclusions can

be drawn:

1. Among the discussed models, there are a few of themwhich able to significantly explain the thermalconductivity enhancement in nanofluids. The reviewof literature indicated that a single factor is notresponsible for high thermal conductivity of thenanofluids. Instead a combination of factors willprovide the answer for the overall thermalconductivity of nanofluids.

2. Nanofluids having small amounts of nanoparticleshave substantially higher thermal conductivity thanthose of base fluids. The thermal conductivityenhancement of nanofluids depends on variousfactors such as particle volume fraction, type of basefluid, size and shape of nanoparticles, temperatureand also with type of nanoparticles used.

3. A general form of thermal conductivity model is notso accurate they show a large difference in thermalconductivity enhancement with experimentsvalue.An empirical correlation for the thermalconductivity of two most promising nanofluidsAl2O3 with water, ZnO and TiO2water, consideringthe effects of temperature, volume fraction and sizeof the nanoparticle is developed and presented.

REFERENCES

[1] Choi, U.S., 1995, Enhancing Thermal Conductivity of Fluids withNanoparticles, Developments and Applications ofNon-Newtonian Flows.

[2] A new thermal conductivity model for nanofluids, Junemoo Kooand Clement Kleinstreuer, Journal of Nanoparticle Research(2004) 6:577–588.

[3] New temperature dependent thermal conductivity data forwater-based nanofluidsHonorineAngueMintsa, Gilles Roy ∗,International Journal of Thermal Sciences 48 (2009) 363–371.

[4] Temperature Dependence of Thermal Conductivity Enhancementfor Nanofluids, Sarit Kumar Das, Nandy Putra ,Peter Thiesen,WilfriedRoetzel.

[5] Thermal Conductivity of Nanoparticle–Fluid Mixture XinweiWang¤ and XianfanXu journal of thermophysics and heattransfer ,Vol. 13, No. 4, October–December 1999.

[6] Anomalously increased effective thermal conductivities ofethylene glycol based nanofluids containing copper nanoparticlesJ. A. Eastman, S. U. S. Choi, S. Li, W. Yu, and L. J. Thompsonapplied physics letters volume 78, number 6.

[7] Experimental Studies of Natural Convection Heat Transfer ofAl2O3/DIWater Nanoparticle Suspensions (Nanofluids) CalvinH. Li andG. P. Peterson Hindawi Publishing CorporationAdvances in Mechanical Engineering Volume 2010, Article ID742739.

[8] hydrodynamic and heat transfer study of dispersed fluids withsubmicron metallic oxide particles Bock Choon Pak & Young I.Cho , Experimental Heat Transfer: A Journal of Thermal EnergyGeneration, Transport, Storage, and Conversion.

[9] Thermal conductivity of heterogeneous two component system ,Hamilton and Crossor ,Chemical Engineering Division, AtomicEnergy Establishment, Trombay, Bombay, India.

[10] Effective viscosities and thermal conductivities of aqueousnanofluids containing low volume concentrations of Al2O3nanoparticles .Ji-Hwan Lee International Journal of Heat andMass Transfer 51 (2008) 2651–2656.

Study on Parameters of Thermal Conductivity Enhancement in Oxide Nanofluids

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