prediction of pressure drop and heat transfer in micro-heat exchangers with nano cryogenic fluids

12 Pages·2017·0.36 MB·English

International Journal of Mechanical Engineering and Technology (IJMET)
Volume 8, Issue 7, July 2017, pp. 1272–1283, Article ID: IJMET_08_07_138
Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=7
ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

PREDICTION OF PRESSURE DROP AND HEAT
TRANSFER IN MICRO-HEAT EXCHANGERS
WITH NANO CRYOGENIC FLUIDS USED IN

THE COOLING OF ELECTRONIC DEVICES
Vishnu Saini
Research Scholar, School of Mechanical Engineering,
Lovely Professional University, Punjab, India.
Abhinav Kumar
Research Scholar, School of Mechanical Engineering,
Lovely Professional University, Punjab, India.
Kumari Neelam Verma
Graduate Student, School of Mechanical Engineering,
Lovely Professional University, Punjab, India.
Raja Sekhar Dondapati
Associate Professor, School of Mechanical Engineering,
Lovely Professional University, Punjab, India.
ABSTRACT
Microelectronic devices are the integral part of advanced technologies such as
space technologies. However, the cooling of these microelectronic devices encounters
various challenges due to large aspect ratios. Further, the conventional liquid
coolants could not dissipate the heat faster due to limited thermal conductivity and
specific heat values. Hence, in the present work, a computational investigation is done
on the feasibility of using cryogenic coolants with nanoparticles such as CuO, SiO ,
2
SiC, Al O and TiO dispersed in the cryogenic coolant (Liquid Nitrogen). A
2 3 2
computational geometry is developed in ANSYS® and the pressure drop and heat
transfer analysis is done using FLUENT®. Relevant boundary conditions are applied
to reflect the practical operating conditions of microelectronic devices. It is observed
from the results that the pressures drop decreases with suspension of CuO. Further,
the heat transfer is observed to be increasing with the addition of Al O and SiO
2 3 2
nanoparticles with the volume concentration of 3%. Finally, it can be concluded that
the dispersion of the nanoparticles in Liquid Nitrogen (LN ) will be beneficial to use
2
in microelectronic devices.
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Vishnu Saini, Abhinav Kumar, Kumari Neelam Verma and Raja Sekhar Dondapati
Key words: Liquid Nitrogen, Micro Heat Exchangers, Nanofluid, Metal Oxide
Nanoparticles, Micro-electronic devices.
Cite this Article: Vishnu Saini, Abhinav Kumar, Kumari Neelam Verma and Raja
Sekhar Dondapati Prediction of Pressure Drop and Heat Transfer in Micro-Heat
Exchangers with Nano Cryogenic Fluids Used in The Cooling of Electronic Devices.
International Journal of Mechanical Engineering and Technology, 8(7), 2017, pp.
1272–1283.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=7
1. INTRODUCTION
With the increasing demand for various electronic devices such as integrated circuits [1],
microreactors [2], microprocessors and laser diodes [3], cooling of these devices is become
essential due to their higher heat dissipation. These demands can be fulfilled by incorporation
of micro-heat exchangers working with nanofluids. These nanofluids play a vital role in
enhancement of thermophysical properties by which desired result of higher heat transfer rate
can be achieved. Koo et al. [4], reported that the performance of micro-heat sinks increased
with addition of CuO nanoparticles with a concentration of 1-4%. Further, Lee et al. [5],
investigated that the heat transfer coefficient increased with decreasing the channel diameter
at a given flow rate. Furthermore, Lelea et al.[6], investigated the numerical modeling of Al O
2 3
with water and observed that heat transfer is increased in axial direction with suspension of
Al O nanoparticles. In addition, Kamyar et al. [7], reviewed that computational simulation has
2 3
good agreement with experimental work and nanofluids leads to enhancement of heat transfer.
Later, Seyf et al. [8], numerically studied the enhancement in convective heat transfer by using
nanofluid in micro-pin-fin heat sink. Shalchi-Tabrizi et al. [9], numerically investigated the effect
of volume concentration and particle diameter on entropy, hydrodynamic and heat transfer
performance of tangential micro-heat sink. Sohel et al. [10], reported that use of nanofluids
enhanced the thermal performance of micro-channel heat sink to be used in electronic heat sink.
Moreover, Azari et al. [11], investigated the heat transfer enhancement in water/Al O based
2 3
nano fluids using Computational Fluid Dynamics (CFD). Bianco et al. [12], reported that
nanofluids (Al O with water) allows to obtain higher heat transfer coefficient in circular
2 3
tubes. Furthermore, Peyghambarzadeh et al. [13], experimented that higher heat transfer can
be obtained by using micro channel with incorporation of nanofluids and showed good
agreement with conductive and convective heat transfer mechanisms. Ray et al. [14]
experimentally and numerically investigated the enhancement of heat performance in compact
plate heat exchangers. Later, Solomon et al.[15], observed that addition of nanoparticles in
screen mesh pipe increased the effective thermal conductivity by which enhancement in heat
transfer is obtained. Salman et al. [16], reported that maximum heat transfer enhancement
was about 22% when using the nanofluids. R.S. Dondapati [17], investigated the performance
of various nano fluids for cooling of transformers. Motivated by the challenges involved in
cooling of micro-electronic devices, in the present work, the nano particle such as CuO [18],
[19], SiO [20], [21] SiC, Al O [22], [18], [23] and TiO [24], [25] is dispersed in Liquid
2 2 3 2
Nitrogen (LN2) and thermophysical properties is evaluated to investigate the performance of
micro-electronic devices. Further, Computational analysis is done to assure the use of these
nanofluids in micro-electronic devices for better heat transfer performance.
2. STUDY OF THERMOPHYSICAL PROPERTIES OF NITROGEN
Nanoparticles such as CuO, SiO , SiC, Al O and TiO with 3% volume concentration are
2 2 3 2
dispersed in LN (basefluid) to obtain the nanofluid to be used in micro-electronic devices. In
2
order to evaluate the performance of micro-electronic devices, it is essential to study the
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Prediction of Pressure Drop and Heat Transfer in Micro-Heat Exchangers with Nano Cryogenic
Fluids Used in The Cooling of Electronic Devices
thermophysical properties such as density, viscosity, thermal conductivity and specific heat
using theoretical and experimental model. However, these correlations are applicable for
water, oil and ethylene glycol basefluid with suspension of nanoparticles. These correlations
implied approximate desired result with 3% of volume fraction nanoparticles suspended in
base fluid (LN ).These thermophysical properties will be used to solve governing
2
conservation equations.
3. THERMOPHYSICAL PROPERTIES OF LIQUID NITROGEN BASED
NANOFLUID
Thermophysical properties such as density, specific heat, with viscosity and thermal
conductivity of nanofluid are computed as function of temperature from 65 to 83K at a
pressure of 2bar. In equation (1) [26], ρ shows the effective density of Nanofluid φ
NF
volume fraction of nanoparticle ( ) and ρ density of basefluid ( ).
NP BF
ρ = (1−φ)ρ +φρ
NF BF NP (1)
In equation (2) [27], C shows the effective specific heat of Nanofluid withφvolume
pNF
fraction of nanoparticle (NP),C is specific heat andρis density of base fluid (BF).
p

(1 − φ)(ρC ) + φ(ρC )
C = p B F p NP (2)
pNF (1 − φ)ρ + φρ
BF NP

TABLE 1 Theoretical and Experimental correlation for Thermal Conductivity
Relevant
Model Reference Correlation
information
Liquid and
Maxwell- k k + 2k + 2φ(k − k )
Theoretical eff = p f p f solid
1881 k k + 2k −φ(k − k )
f p f p f suspension
k −k Al2O3/water
Li and eff f = 0.764φ+0.0187(T −273.15)−0.462 Nanofluid
k
f
Experimental Peterson
k −k
-2006 eff f =3.761φ+0.0179(T −273.15)−0.307 CuO/water
k
f Nanofluid
Table 1 shows both the experimental and theoretical correlations for effective thermal
conductivity of nanofluid ( ) withφvolume fraction of nanoparticle for ( ) thermal
k k
eff f
conductivity (base fluid) andk thermal conductivity of nanoparticle. To compute the
p
effective thermal conductivity of LN , Maxwell correlation [28] and Li and Peterson [29]
2
corelation are considered. Table 2 shows both experimental and theoretical formulas of
effective viscosity of nanofluid (µ ) withφvolume fraction of nanoparticle for (µ )
eff f
viscosity of basefluid. To evaluate the effective viscosity of LN and LHe, Einstein [30],
2
Drew and Passman [31] correlation are considered.

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Vishnu Saini, Abhinav Kumar, Kumari Neelam Verma and Raja Sekhar Dondapati
TABLE 2 Theoretical and Experimental correlation for Viscosity
Model Reference-Year Correlation Relevant information
µ
Theoretical Einstein-1906 eff =1+ 2.5ϕ Infinitely dilute suspension
µ of spheres
f
Drew and passman- µ Concentration is than less 5
Experimental eff = 1+ 2.5φ
1993 µ vol%
f
4. COMPUTATIONAL STUDY ON LIQUID NITROGEN BASED
NANOFLUID
In FIG 1, geometrical configuration of micro tube used in micro-electronic device is shown.
To model this device the mesh generation using ANSYS [32] is shown in FIG 2. In this
present study the fluid enters through inlet with uniform temperature and the wall heat flux is
considered uniform.

Figure 1 Geometrical configuration of micro tube used in micro heat exchanger
4.1. Governing Equations
To obtain numerical results in this geometry, governing equations are to be solved. There are
three governing equation such as conservation of mass, momentum and energy equation.
These equations for single phase fluid are given in (3) to (5).
Mass Conservation equation:
∂ρ →
+∇.(ρv)=S (3)
∂t m
Where ,S is the source term for mass.
m
Momentum Conservation equation:
∂ → →→ ( ) → →
(ρv)+∇.(ρv v)=−∇p+∇. τ +ρg+F
∂t (4)
Energy Conservation equation:
∂ → ∑ 
(ρE)+∇.(v(ρE+ p))=−∇. h J +S (5)
∂t  j j h
j

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Prediction of Pressure Drop and Heat Transfer in Micro-Heat Exchangers with Nano Cryogenic
Fluids Used in The Cooling of Electronic Devices
4.2. Discretization
To convert the governing equation in to algebraic form finite volume method (FVM)
Descritization is done. FLUENT [33] is used to solve these equations to obtain numerical
result. In this work Descritization of momentum and energy equation with second order
upwind scheme, turbulent kinetic energy and turbulent dissipation rate with first order upwind
scheme, pressure with standard scheme and solution of linear equation with least square cell
based is done. For pressure-velocity coupling SIMPLE (semi implicit method for pressure
linked equation) algorithm is used. Transport equation [34] for the Realizable k-€ model is:
∂ ∂ ∂  µ  ∂k 
(ρk)+ (ρku )= µ+ t  +G +G −ρε−Y +S (6)
∂t ∂t j ∂x  σ ∂x  k b M k
i k i
∂ ∂ ∂  µ  ∂ε ε2
(ρε)+ (ρεu ) = µ+ t  +ρC Sε−ρC
∂t ∂x j ∂x  σ ∂x  1 2 k + vε (7)
j j ε j
ε
+C C G + S
1ε k 3ε b ε
 η  k
Where C = max 0.43,  , η=S
1  η+ 5  ε

Figure 2 Mesh generation of micro tube used in micro heat exchanger
4.3. Boundary Conditions
At inlet of exchanger, the fluid flow with 0.1 to 0.14 kg/s mass flow rate at 65 K temperature.
Moreover, fully developed flow is dominated at outlet of exchanger. To compute the
Reynolds Number temperature dependence properties, viscosity and density, is taken.
Uniform heat flux of 91300 W/m2 is applied on exchanger wall and no slip condition is
considered.
4.4. Solution
The present descritized model is solved by using FLUENT [33] for further analysis. To
simulate the present model 0.001 residuals are considered to iterate the equations. To initialize
the solution standard method is considered. Assumption of this wall function gave desired
result.
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5. RESULTS AND DISCUSSIONS
5.1. Pressure Drop Analysis
In this study, Computational Fluid Dynamics (CFD) is used to investigate the pressure drop
and heat transfer with the combined effect of temperature dependent thermo-physical
properties and suspension of nanoparticles due to turbulent flow of LN2. FIG 3 and FIG 4
shows the velocity profiles of fully developed flow at outlet of circular pipe for Maxwell
theoretical [28] and Li and Peterson experimental model [29] respectively with dispersion of
nanoparticles at 0.13 kg/s mass flow rates.
Firstly, Pressure drop is computed for circular channel with 3% volume concentration of
nanoparticles. FIG 5 shows increase in pressure drop with increase in Reynolds Number and
decrease in pressure drop with suspension of nanoparticles. Moreover, Pressure drop is less
for CuO nanoparticles with 3% of volume concentration in LN . For circular pipe friction
2
factor is computed from equation (8) for turbulent flow. Reynolds Number is can be
calculated from equation (9)
8τ
f = wall (8)
ρv2
avg
DV ρ
Re= h avg (9)
µ
FIG 6 and FIG 7 shows the friction factor for Maxwell theoretical [28]and Li and Peterson
experimental model [29] with dispersion of 3% of nanoparticles. It is observed that friction
factor decrease with increase with increase in Reynolds Number and with dispersion of
nanoparticles. Moreover, Fiction factor is less for Al O nanoparticles with 3% of volume
2 3
concentration inLN
2.
5.2. Pumping Power
To pump the nanofluids in micro channels, Pumping power at various mass flow rates with
3% volume concentration of nanoparticles is calculated as
•
W =∆PV (10)

In FIG 8, pumping power has been shown for various mass flow rates. It is observed that
pumping power increases with increase in mass flow rate whereas decreases with the
dispersion of different nanoparticles at 3% volume concentration.
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Prediction of Pressure Drop and Heat Transfer in Micro-Heat Exchangers with Nano Cryogenic
Fluids Used in The Cooling of Electronic Devices

Figure 3 Velocity profiles with different nanoparticles at 0.13 kg/s mass flow rate for Maxwell
theoretical model

Figure 4 Velocity profiles with different nanoparticles at 0.13 kg/s mass flow rate for Li and Peterson
experimental model [32]

Figure 5 Pressure drop of Liquid Nitrogen with different nanoparticles
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Figure 6 Friction factor with different nanoparticles for Maxwell theoretical model [31]
This is due to decrease in friction factor and wall shear stress. Moreover, pumping power
is very less for CuO dispersed nanofluid which is desirable result to pump the nanofluids in
micro heat exchangers.
5.3. Heat transfer analysis
To cool the microelectronic devices, LN based nano-cryogens is passed the through circular
2
channels. The heat dissipates from these devices transfer their heat to the channels. Hence,
temperature difference will be created between outlet and inlet sections. FIG 9 shows these
differences for different flow rate. It is found that temperature difference decreases with
increase in mass flow rate and increase with the dispersion of nanoparticles. Moreover,
temperature difference is more for suspension of CuO in Liquid Nitrogen.

Figure 7 Friction factor with different nanoparticles for Li and Peterson experimental model [32]
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Prediction of Pressure Drop and Heat Transfer in Micro-Heat Exchangers with Nano Cryogenic
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Figure 8 Pumping power of Liquid Nitrogen at different mass flow rate
FIG 10 and FIG 11 shows Nusselt Number with Reynolds Number for Maxwell
theoretical model [28] and Li and Peterson experimental model [29] respectively. It is
detected that Nusselt Number increases with increase in Reynolds Number and with the
suspension of different nanoparticles. Moreover, Nusselt Number is higher with dispersion of
Al O nanoparticles for Maxwell theoretical model and with dispersion of SiO nanoparticles
2 3 2
for Li and Peterson experimental model. Furthermore, increase in Nusselt Number shows
increases the heat transfer which is desirable property to use nanoparticles in LN2.
Enhancement in heat transfer due to increase in thermal conductivity of LN with suspension
2
of different nanoparticles with 3% volume concentration.

Figure 9 Temperature difference Vs mass flow rate with different nanoparticles
5.4. Cooling Capacity
Cooling capacity of nanofluids is estimated as
•
Q =VρC (T −T ) (11)
cc p inlet outlet
FIG 12 shows the cooling capacity of LN with suspension of different nanoparticles at
2
various mass flow rates. It is observed that cooling capacity decrease with increase in mass
flow rate and also with the suspension of different nanoparticles.
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Figure 10 Nusselt Number Vs Reynolds No of LN with different nanoparticles for Maxwell
2
theoretical model [28]

Figure 11 Experimental Nusselt Number Vs Reynolds No of LN with different nanoparticles for Li
2
and Peterson experimental model [29]

Figure 12 Cooling Capacity of LN at different mass flow rate
2
6. CONCLUSIONS
The present work shows the pressure drop and heat transfer analysis using FLUENT code.
The suspension of nanoparticles with 3% volume concentration in LN shows increase in heat
2
transfer and decrease in pressure drop. Heat transfer is high with the suspension of Al O
2 3
(Maxwell theoretical model) and SiO for Li and Peterson (experimental) model. Moreover,
2
Pressure drop is less with the suspension of CuO nanoparticle in LN Further, pumping
2.
power and cooling capacity of nanofluids decreases with 3% dispersion of nanoparticles. It
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