Cooling of Electronics With Nano Fluids

7/31/2019 Cooling of Electronics With Nano Fluids

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Cooling Of Electronics With Nanofluids

ByRahul .T ,

Christopher ,Third year ,JJCET .

Abstract

Nanofluids are solutions of a small fraction of suspended nanoparticles in a bulk fluid.

Nanofluids have shown great promise as heat transfer fluids over typically used bulk fluidsand fluids with micron sized particles. The nanoparticles do not settle in the fluid and do not

cause clogging or damage to surfaces as with micron sized particles. Much attention has been

paid in the past decade to this new type of composite material because of its enhanced

properties and behaviour associated with heat transfer .Nanofluids have been proposed to

improve theperformance of microchannel heat sinks. In this paper, we present a systematic

characterization of nanofluids on cooling electronics The Nusselt number was extracted from

the experimental results and compared with the theoretical predictions considering the change

of fluids bulk properties. We demonstrated a deviation of less than 10% between the

experiments and the predictions. We showed that the relative thermal conductivity

enhancement must be larger than the relative viscosity increase in order to gain a sizeable

performance benefit. Furthermore, we showed that it would be preferable toincrease the volumetric heat capacity of the fluid instead of increasing its thermal

conductivity.

Introduction

High heat flux removal is a major challenge in the design of future electronic devices. The

trend to address these high heatfluxes is to introduce microchannel arrays directly in the heat

generating electronic component . Commonly, water is suggested to be used as a single-

phase coolant in combination with microchannel heat sinks for cooling electronics, as it

possesses the most adequate thermal and hydrodynamic transport properties in the required

range of operating temperatures. However, the thermal conductivity of water is two to three

orders of magnitude lower than of most metals and metal oxides. Therefore, an innovative

way to elevate the thermal conductivity of fluids may be the addition of nanometer-sized

metal or metal oxide particles into a base-fluid, most suitably

Nanofluids are dilute liquid suspensions of nanoparticles with at

least one critical dimension smaller than ~100nm. The nanoparticles used in nanofluids are

typically made of metals, oxides, carbides, orcarbon nanotubes. Common base fluids include

water ,ethylene glycoland oil .The nanofluids have drawn much attention in the heat transfer

society .Nanofluids are promising to meet and enhance the challenges. The major interest of
http://en.wikipedia.org/wiki/Carbon_nanotubehttp://en.wikipedia.org/wiki/Carbon_nanotubehttp://en.wikipedia.org/wiki/Carbon_nanotubehttp://en.wikipedia.org/wiki/Ethylene_glycolhttp://en.wikipedia.org/wiki/Ethylene_glycolhttp://en.wikipedia.org/wiki/Ethylene_glycolhttp://en.wikipedia.org/wiki/Ethylene_glycolhttp://en.wikipedia.org/wiki/Carbon_nanotube


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the research on nanofluids has focused on the enhanced thermal conductivity of the colloids

under stationary condition .There has been a broad range of experimental investigations using

common measurement techniques such as the transient-hot-wire method, the 3-wrie method,

and the parallel plate method. However, there is a large scatter in the data of thermal

conductivity enhancements. To give just one example, 160% thermal conductivity increasefor 1 vol % of multiwalled carbon nanotubes in silicone Oil. Thermal conductivity of the

tested nanofluids increased with particle loading, particle aspect ratios, and decreasing base-

fluid thermal conductivity. Nevertheless, all experimental data could be well explained by the

classical effective medium theory for well-dispersed particles . Forced convective heat

transfer is not only influenced by the thermal conductivity of the coolant. Other transport

properties such as the fluid density, specific heat capacity, and dynamic viscosity have an

impact on the performance of the cooling solution and thus are being investigated to greater

or lesser extent . The enhanced convective heat transfer coefficient and the increased pressure

loss are in good agreement with the traditional model predictions for laminar flow.

Nanofluid preparation and characterization

Two nanofluid production methods has been developed in ANL to allow

selection of the most appropriate nanoparticle material for a particular application.

In two-step process for oxide nanoparticles (Kool-Aid method), nanoparticles are

produced by evaporation and inert-gas condensation processing, and then dispersed (mixed,

including mechanical agitation and sonification) in base fluid.

A patented one-step process simultaneously makes and disperses nanoparticles directly intobase fluid; best for metallic nanofluids.

The fluids were prepared by adding the nanoparticles at a specified mass to the DI-water to

achieve the desired volume loading of the mixture. No surfactant was added to the mixture so

that the only components were the nanoparticle and the DI-water. The fluids were then

sonicated for at least 2 h in a UP200S portable ultrasonicator to break up any agglomerates.

The nanofluids were characterized to determine the actual size of the particles within the

suspension. Dynamic light scattering (DLS) was performed. Even though the DLS results did

not agree with the specified particle size, the suspensions were stable on the shelf for a period

of a month for the 20 to 30 nm particle suspensions. Because these suspensions contain

particles that are larger than the typical size of particles in nanofluid suspensions (1to100 nm

in diameter) these may not actually be nanofluids, but they are considerably smaller thanmicron sized particles therefore they should exhibit some of the features of nanofluids with

respect to the stability and reduced damage to interior of the components in the system. The

10 nm and 150 nm particle suspensions were much less stable when compared to the particle

suspensions therefore we have chosen to only perform the thermal measurements on the

particle suspensions

Mechanisms of the thermal conduction enhancement

A number of mechanisms have been proposed for interpreting the experimentally

observed thermal conduction enhancement including Brownian motion of nanoparticles, the

interfacial ordering of liquid molecules on the surface of nanoparticles, the ballistic transportof energy carriers within individual nanoparticles and between nanoparticles that are in


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contact, as well as the nanoparticle structuring and networking There has been much debate

on these mechanisms over the past few years and the focus of debate has been on the role of

Brownian motion . A brief discussion will be made in the following text on the two much-

debated mechanisms.

The role of Brownian motion

The Brownian motion of nanoparticles could contribute to the thermal conductionenhancement through two ways, a direct contribution due to motion of nanoparticles that

transport heat, and an indirect contribution due to micro-convection

of fluid surrounding individual nanoparticles. The direct contribution of Brownian motion has

been shown theoretically to be negligible as the time scale of the Brownian motion is about 2

orders of magnitude larger than that for the thermal diffusion of the base liquid The indirect

contribution has also been shown to play a minute role by theoretical analysis . Furthermore,

nanoparticles are often in the form of agglomerates and aggregates. The Brownian motion

should therefore play an even less significant role. In the following text, further experimental

evidence of the minor role of the Brownian motion is presented. The thermal conductivity

enhancement as a function of temperature for nanofluids made of three types of metal-oxide

nanoparticles. One can see that, for CuO/H2O nanofluids, the thermal conductivityenhancement is a very weak function of temperature. The weak temperature dependence

suggests that the Brownian motion of nanoparticles is not a dominant mechanism of the

enhanced thermal conductivity of nanofluids

No clear trend in the dependence of the thermal conductivity enhancement on the base liquid

viscosity again suggests the minor role of the Brownian motion.

The role of liquid molecular layering At the solid-liquid interface, liquid molecules could be

significantly more ordered than those in the bulk liquid. By analogy to the thermal behaviour

of crystalline solids, the ordered structure could be a mechanism of thermal conductivity

enhancement On such a basis, a number of macroscopic models have been proposed to

interpret the experimental data; see for example, It is now clear that the liquid-nanoparticle

interface is one of the main factors that decrease (rather than increase) the effective thermal

conductivity due to the so-called Kapita interfacial resistance

It should be noted that the effect of interfacial resistance on

the overall effective thermal conductivity depends on the particle size When particle size is

relatively small in comparison with the characteristic length scale due to the interfacial

resistance, nanoparticles act as insulators. This leads to deterioration of the thermal

conduction of nanofluids.

The last standing mechanism The above discussion indicates that neither Brownian motion

nor interfacial liquid layering can be a dominant mechanism. As the ballistic transport of

energy carriers in nanofluids has been excluded as a dominant mechanism, the last

mechanism standing is the nanoparticle structuring / networking . This has actually beenvalidated by our experimental results and theoretical analyses of ethylene-glycolbased titania

nanofluids. We found that the size of the aggregates is approximately 3.5 times that of the

primary nanoparticles .By using the Maxwellmodel for aggregate suspensions and the

Bruggeman model for aggregates a nanoparticle structuring model is formulated which gives

a fairly . Ballistic phonon could lead to a significant increase in thermal conductivity. In

particular, if the ballistic phonons initiated in one particle can persist in the liquid and reach a

nearby particle, a major increase of thermal conductivity is expected. Be- cause the phonon

mean free path is much shorter in the liquid than in the particle, such may only operate if the

separation between particles is very small, likely on the order of the thickness of the layered

liquid(%12 nm).


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Schematic diagram 1 : Ballistic and diusive phonontransport mechanisms in a solid particle.

Theoretical ModelsIncrease in thermal conductivity depends on nanoparticle material, size and concentration.

Nanoparticles have a large surface area-to-volume ratio; a 1 nm spherical particle has asurface area-to-volume ratio 1000 time greater than that of a 1 m particle; Kapitza resistance

becomes important for such large surface areas. Increase in thermal conductivityis beyond the classic Maxwellian model predictions. Literature survey reveals that there are

large number of models for estimating thermal conductivity of nanofluids. The existing

models can be categorised into two general groups:(i) Static models which assume stationary

nanoparticles in the base fluid in which the thermal conductivity is predicted by

conduction-based models such as Maxwell45, Hamilton- Crosser46, and others47,48, using

conductivity of phase constituents and volume fractions, (ii)Dynamic models based on

random motion of the nanoparticles in fluid (Brownian motion) and responsible for

transporting energy through collision between nanoparticles or micro liquid convection,

mixing that enhances the transport of thermal energy. Modelling of nanofluid properties hasbeen done through molecular diffusion simulation. Some of the basic models used to estimate

the thermal conductivity based on above two approaches are shown in Table 2. Heat transfer

coefficient increase are on top of the thermal conductivity. Possible mechanisms considered

for this increase are nanoparticle diffusion and boundary layer thinning, dispersion and

enhanced turbulence. Increase in critical heat flux may be attributed to alteration of

nucleation site by nanoparticle

Experimental SetupThe microchannel heat sink used in the present investigation. A test-vehicle

consists of a silicon die and a glass chip being 16 mm in size. The silicon die containsA 10 mm in 2 array of parallel microchannels. The microchannel array was fabricated by

standard photolithography and deep reactive ion etching into the 525 mm thick silicon chip.

In the same process, step pressure taps at 0 mm and 10 mm were integrated to resolve the

pressure drop across the microchannel array. In a second DRIE sequence, lateral fluid ports

with an opening , implemented into the silicon chip for fluid supply and return. We integrated

a resistive heater on the center back side of the silicon chip covered with a 100 nm thick

silicon oxide layer for electrical insulation. The heater covered an area of 10_10 mm2 and

was realized by depositing a 300 nm thick NiCr 80/20 metal layer with a low temperature

resistance coefficient of 80 ppm for optimal heat flux uniformity structured by a lift-off resist

providing a total electrical resistance .At the center line of the heater orthogonal to the flow

direction, the heater was separated into two sections by a resistive temperature device . A 300nm thick gold metallization was used as contact pads and the formation of the RTD,


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providing a temperature resistance coefficient of 2300 ppm/K. The heat transfer structure, the

fluid ports and the pressure taps were covered by a 500 mm thick glass _Pyrex, thermal

expansion coefficient close to silicon wafer to seal the structure and to allow optical access

from the top. The glass wafer was spin coated with a 4 mm to 5 mm thick polyimide layer.

We achieved a leak-proof bond between the silicon and the glass wafer by applying a

uniform pressure of 7 bars in a membrane oven at 320C and 1 mbar atmosphere. Thepackage was cut by a dicing saw into individual chips. The individual test-vehicles were

connected to a test-section providing interfaces for the fluid supply and return and the

pressure taps and electrical connections in form of spring loaded probs. The pressure drop

across the microchannel array was measured by a differential pressure transducer with a

maximum pressure difference of 2 bars . We used T-type thermocouples with a specified

error of 0.1 K to measure the fluid inlet and outlet temperatures. A spatial temperature

distribution of the heater backside was obtained by observing the heater by an infrared _IR_

camera with a noise-equivalent temperature difference of 25 mK .we incorporated a particle

filter with a pore size of 7 mm to prevent the microchannel array from contamination with

larger impurities. The flow rate and the fluid density were measured by a coriolis flow meter.

We monitored the fluid density during all runs to assure that there was no change in thecomposition of the fluids. The heat was removed by a 1.8 kW chiller guaranteeing constant

fluid inlet temperature of 20 to1.5C. Other uncertainties are listed in Table 1, where the

uncertainties of the secondary variables were derived by applying standard uncertainty

propagation theory.

Experimental ResultsThe heat flux as a function of the wall superheat (temperature difference between the bulk

fluid and the boiling surface), together with the prediction by the

classical correlation of Rehsenow for pool boiling. One can see that the experimental data for

water agree well with the Rehsenow correlation. The data of nanofluids deviate from theRehsenow equation and the deviation increases with nanoparticle concentration.

The data shown in are processed to give the heat transfer coefficient. The results in the form

of the ratio of heat transfer coefficient of nanofluids to that of pure water given other

conditions. Enhancement of the boiling heat transfer is significant for both alumina and

titania nanofluids in the nucleate regime, and the enhancement cannot be entirely attributed to

the thermal conduction enhancement also shows that the heat transfer enhancement increases

with nanoparticle concentration and the enhancement for titania nanofluids is more sensitive

to the change of particle concentration in comparison with that for alumina nanofluids. The

different heat transfer behaviour of alumina and titania nanofluids indicates that the nanofluid

properties have an influence on the boiling heat transfer in the nucleate regime. The

experimental results of this work as presented above agree with that and our results, however,disagree with observed deterioration of boiling heat transfer in the nucleate regime. The

exact reason for the discrepancy is unclear. Possible reasons are discussed in the following

text: Thermal conductivity and viscosity affect the heat transfer behaviour of nanofluids in

opposite ways. As a result, a combination of thermal conductivity enhancement and

increment of the viscosity can give either enhancement or deterioration of the heat transfer

coefficient. However, there is too little information in the published studies to permit making

a conclusive assessment. Stability of nanofluids and the presence of a dispersant / surfactant

affect the behaviour of nanofluids, which are often not provided in the published studies. For

example, settling of nanoparticles in nanofluids with poor stability can change the properties

of the boiling surface, and surfactants / dispersants may fail at elevated temperatures.Boiling heat transfer consists of a number of subprocesses in parallel and/or series, including


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unsteady-state heat conduction, growth and departure of bubbles, and convection due to

bubble motion and liquid re-filling. These sub-processes are affected by parameters such as

heater geometry, properties of the boiling surface, orientation of the heater, liquid sub-

cooling, system pressure, and the mode in which the system is operated. the boiling heat

transfer. The surface properties include surface finish (roughness), surface wettability, and

surface contamination, as they all influence the number and distribution of active nucleationsites for bubbles and their subsequent growth. In the published studies, however, surface

roughness is the most often-used parameter, and interpretation of the effect of surface

roughness on the boiling heat transfer has been based on the size of the suspended particles

relative to the surface roughness. For example, a boiling surface of nanometre-scale

roughness, hence sedimentation of the particles was regarded to effectively increase the

roughness of the surface, whereas a commercial cartridge heater with a micron-scale surface

roughness onto which sedimentation of the nanoparticles was thought to decrease the

effective surface roughness. Different temperature measurement methods may lead to the

different experimental results obtained by different investigators. For example, all

thermocouples were welded on the outer surface of the cartridge heater .This would

inevitably influence the surface characteristics of the boiling surface, as bubbles have atendency to nucleate on the welded positions and the measured temperature may not be

representative of the boiling surface used fine resistance wires for temperature measurements.

Large uncertainties are expected for this sort of method as temperature is converted from the

measured resistance of the heating wire against the standard temperature-resistance curve.

Indeed, for boiling with pure water, more than 10 deviance of superheat was observed under

a fixed heat flux condition in different runs; It may be sensible for a qualitative comparison of

the critical heat flux (CHF), but it may not be adequate for a quantitative comparison of

nucleate boiling heat transfer. Obviously, the above discussion is crude and on aqualitative

basis. Nevertheless, these points provide possible ways towards interpreting the controversies

in the literature.

Theoretical Evaluation of the Nanofluid Potential

Effectiveness for Electronics Coolingwe concentrated on the convective heat transfer of nanofluids and neglected to discuss the

increased viscous pressure drop for nanofluids and its impact on the performance of

microchannel heat sinks. The characteristic number for a heat sink is its coefficient of

performance, COP, defined as the ratio of the dissipated heat to the invested pumping power

The dissipated heat scales with the total thermal resistance of the system,

whereas for a constant volumetric flow rate and geometry, the pressuredrop across the heat

sink; thus, the invested pumping power are proportional to the dynamic viscosity of the

coolant. As depicted in none of the nanofluids provided a region with a lower thermalresistance than water, irrespective of the channel width of the heat sink. Since all silica

nanofluids showed an increased viscosity compared with water, there would be no

benefit of using one of these nanofluids in combination with the present heat sink designs.

There are several investigations reporting higher thermal conductivity enhancements at lower

particle concentrations than for the present silica nanofluids. Also, the international

benchmark demonstrated that the thermal conductivity enhancement increases with

increasing particle aspect ratio and thermal conductivity ratio of the particle and the base-

fluid. Hence, there might be better performing nanofluids than the present silica nanofluids.

To study the general potential of nanofluids to enhance the performance of microchannel heat

sinks, we applied the experimentally validated one-dimensional model presented to

determine the heat sink performance as a function of the fluid thermophysical properties. Wedemonstrated in a previous study that there exists an optimum channel width for a constant


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filling factor of 0.5 and constant channel height maximizing the COP of a microchannel heat

If the channel width is reduced further than the optimum width, the increase of bulk thermal

resistance due to an increased hydrodynamic resistance reduction of the volumetric flow rate

cannot be compensated by a reduction of convective thermal resistance. Since the bulk and

the convective thermal resistance are a function of the thermophysical properties of the

coolant, the optimum channel width changes with varying fluid properties. Thus, for ameaningful evaluation of the impact of the individual fluid properties, the heat sink

performance has to be compared at the respective design optimum.

We studied the impact of a relative change of the thermophysical fluid properties on the heat

sink performance for a constant pumping power of 1 W corresponding to the higher pumping

power range of the experiments. If water is used as the coolant, the optimum channel width is

estimated to be wch ,opt=35.7 _m, resulting in a total thermal resistance of 0.121 cm2 K/W

for a volumetric flow rate of 0.215 l/min and a pressure drop of 2.78 bars. Figure 8 shows the

relative change of the heat sink total thermal resistance, the optimum channel width, and the

volumetric flow rate for a relative change of the thermal conductivity, dynamic viscosity,

specific heat, and density compared with water. If we consider a heat sink optimized for

water and assume that the thermal conductivity of the coolant is increased by 50% forexample, the convective thermal resistance will be reduced. Thus, the bulk thermal resistance

becomes dominant. It would be preferential to increase the channel width in order to reduce

the hydrodynamic resistance and thus increase the volumetric flow rate .Consequently, the

bulk thermal resistance would be reduced at the expense of an increased convective

resistance. At the new optimum channel width, the total thermal resistance would be reduced

by about 10%. A 50% increase in dynamic viscosity entails a shift of the optimum channel

width to wider channels to reduce the hydrodynamic resistance but, on the other hand, the

convective thermal resistance is increased. The increased hydrodynamic resistance due to

higher viscosities is not fully compensated by the enlargement of the channel width. Thus, the

bulk thermal resistance is increased as well, resulting in a 12% larger total thermal resistance

. Increasing the specific heat capacity of the fluid or the fluid density has a similar effect on

the heat sinks performance. If one of these properties is increased by 50%, the effective

thermal mass of the coolant is enlarged, causing a reduction of the bulk thermal resistance.

Hence, the optimum is shifted to narrower channels and the convective thermal resistance is

reduced, leading to a 20% reduction of the heat sink total thermal resistance. To evaluate the

potential of nanofluids to increase the performance of microchannel heat sinks, we

considered a best case scenario. Therefore, we neglected the reduction of volumetric heat

capacity of the fluid. We determined the relative COP of the heat sink operated by a

nanofluid with a random combination of relative thermal conductivities and viscosities

compared with water

ConclusionsIn this paper, we evaluated the potential of nanofluids for cooling of electronics .The

natural convective heat transfer coefficient systematically decreases with increasing

nanoparticle concentration. Although the exact reason is still unclear, the deterioration can be

partially attributed to the high viscosity of nanofluids. We observed the total thermal

resistance as a function of volumetric flow rate and extracted the Nusselt number for the

individual parameters. The experimentally determined Nusselt number was compared with

the analytical predictions, considering the change of fluid bulk properties. We could

demonstrate a deviation between the experiment and the predictions of less than 10%,

indicating that there is no significant additional effect from the particles themselves on the

convective heat transfer. Hence, standard correlations can be applied to adequately determinethe convective heat transfer of nanofluids if the thermophysical properties of the fluid given.


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Cooling of Electronics With Nano Fluids