Journal of Energy Storage 39 (2021) 102630
Contents lists available at ScienceDirect
Journal of Energy Storage
journal homepage: www.elsevier.com/locate/est
Experimental and computational study of using nanofluid for thermal
management of electronic chips
M.M. El-Khouly a, b, M.A. El Bouz a, b, *, G.I. Sultan a
a
b
Mechanical Power Engineering Department, Faculty of Engineering, Mansoura University, El Mansoura 35516, Egypt
Future Higher Institute of Engineering and Technology, Mansoura, Egypt
A R T I C L E I N F O
A B S T R A C T
Keywords:
Thermal management
Nanofluids
CFD
Cooling of electronics
Microprocessor
The compact equipment, area restriction for air-cooled devices, and thermal management of microchips have
attracted the attention of researchers concerned with superior liquid cooling systems. During the present study,
the impact of using nanofluids for cooling a chip was carried out experimentally and numerically. The experi­
ments were conducted to validate the CFD model. Then the CFD model was applied for examining the impact of
nanofluid concentration, and coolant flow rate on the thermal-hydraulic characteristics of the heat sink. CuOwater nanofluid was used with five different nanoparticle volume fractions of 0.5, 0.86, 1.5, 2.25, 3.5 and 5
vol.%. The developed CFD model was validated with the obtained experimental results and with results of
different researchers, which showed good agreement. The results showed that for the investigated electronic chip
with an area of 5 cm by 5 cm and dissipated power of 130 W, an enhancement of 8.1% in thermal conductance
was noticed when using nanofluids as compared to water for the studied operating range. The overall thermal
hydraulic performance of using nanofluid is found to accomplish higher value of 1.47 at nanoparticle volume
fraction of 5% of CuO and Reynolds number of 800.
1. Introduction
Every year, due to the introduction of the most advanced technolo­
gies and growing processor frequency, significant advancements are
made in the performance and speed of personal computers, tablets, cell
phones, and other electronic devices. The trend has grown in the elec­
tronics industry in terms of speed of the processor, leading to a conve­
nient elimination of the high heat generation. The duty of those
components will rapidly deteriorate unless the heat generated by these
components is effectively removed. New cooling methods are needed
because of the limited capacity of currently used technology. In heat
transfer applications, nanofluids with enhanced thermal properties are a
good coolant, as they showed promising results. In the preparation of
nanofluids, different nanoparticles can be used. The feature of such a
procedure is the measurable increase of the coefficient of heat conduc­
tivity. Certain forms of nanofluids that are mainly depended on metal
oxides.
Over the recent past, the improvement of heat characteristics due to
utilization of nanofluid became a priority for many researchers. The
newly developed class of ultrafine nanoparticle coolants (1–100 nm)
showed motivating behavior in lab experiments, providing an increase
in heat transfer characteristics compared with purist fluids [1,2–4]. Xu
et al. [5] researched analytical methods for the estimation of heat
transmission and pressure drops for the heat sink in the parallel plate.
Minimization of the entropy generation rate for fine geometry optimi­
zation and flow conditions. Ultimately, the effects of the numerical
model and the heat sink used to estimate air conditioning limits. Water
cooling technology has been studied by Ellsworth et al. [6], use of hybrid
air to water cooling in the past IBM systems, then passive water cooling.
Beware about how and why water refrigeration has been carried out to
provide the cooling power required while retaining easy operation at the
module level. The enhancement in heat transfer characteristics and
properties of liquid flow of rectangular micro-channel heat sinks
(MCHS) have been studied numerically by Raghuraman et al. [7]. Water
was the working fluid. The flow domain was discretized and solved
using commercially available CFD code, ANSYS CFX 14.5. The laminar
convective coefficient of Al2O3 / water nanofluid was examined both
experimentally and numerically in circular tube by Azari et al. [8] under
identical conditions on the surface. Three specific modules have been
developed, the first was a single-phase fixed physical properties model,
the second was single-phase variable physical properties model and the
third was a double-phase model of discrete particles. The experimental
heat transfer and pressure drop characteristic associated with the MCHS
* Corresponding author.
E-mail address: mostafa_booz@yahoo.com (M.A. El Bouz).
https://doi.org/10.1016/j.est.2021.102630
Received 4 February 2021; Received in revised form 7 April 2021; Accepted 20 April 2021
2352-152X/© 2021 Elsevier Ltd. All rights reserved.
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Nomenclatures
A
Cp
h
hav
I
K
L
ṁ
Nu
p
Q
Re
T
U
V
→
ν
Subscripts
av
F
hs
I
max
min
nf
O
S
W
Surface area [m2]
Specific heat [W/(m2.K)]
Heat transfer coefficient [W/(m2.K)]
Average heat transfer coefficient
Current [A]
Thermal conductivity [W/(m.K)]
Heat source length
Mass flow rate [kg/s]
Nusselt number [-]
Static pressure [N/m2]
Heat transfer rate [W]
Reynolds number [-]
Temperature [ºC]
Velocity [m/s]
Volt [V]
Velocity vector [m/s]
Average
Fluid
heat source
inlet
Maximum
Minimum
Nanofluid
outlet
solid particles
Wall
Abbreviations
CPU
Central Processing Unit
CFD
Computational Fluid Dynamics
3-D
Three Dimension
SiC
Silicon Carbide
GNP
Graphene Nano platelets
LPM
Litre Per Minute
MCHS
Microchannel Heat Sinks
PC
Personal Computer
CNT
Carbon Nanotube
Greek symbols
∅
Volume fraction
М
Viscosity [Pa.s]
ν
Air kinematic viscosity [m2/s]
ρ
Fluid density [kg/m3]
was studied by Ali et al. [9]. As a base fluid, TiO2 nanofluid was used as a
coolant at 15% weight concentrations in water, and its efficiency was
compared to 100 W, 125 W and 150 W distilled water. The results
showed that the thermal efficiency of TiO2 nanofluid relied heavily on
the heat flux and that its utility could be accomplished more effectively
at a lower thermal cost. The effect of angle on the pine-fin heat sink
channel was also investigated by using water-based graphene (GNP)
nanofluids in the 0.25–0.75 LPM.
Al Rashed et al. [10] studied the effect of nanoparticles on the effi­
ciency of a microchip used for CPU cooling. Both experiments and nu­
merical analysis were carried out for distilled water at the beginning,
then for nano-fluid CuO-water with 2 different ∅ of 0.86 and 2.25 vol.%.
The numeric application of nano fluids in micro-pin-fin heat sinks was
studied by Seyf et al. [11]. 3-D steady energy equation and
Navier-Stokes equation were modeled with a finite volume approxima­
tion, iteratively solved using a SIMPLE algorithm, and the heat transfer
behavior in Micro-Pin-Fin Heat Sinks. Jajja et al. [12] studied systemic
effects on the base temperature of the microprocessor of geometry sink,
with cooling fluid (water). They performed a high-heat microprocessor
using a block of copper with cylindrical shape and a power of 325 W,
which was compared with commercially available heat sinks and
nanofluids. The potential of thermal sink geometries was investigated,
which is enough to reduce high-temperature generated microprocessors
to safe and acceptable values.
Panchal [13] explored the thermal properties of a prismatic pouch
battery with LiFePO4 electrode content and components. Experimental
studies are used to characterize the battery, allowing the creation of an
analytical battery thermal model for vehicle models. Electrical evidence
is also provided to validate electrochemistry-based battery thermal
models. Jilte et al. [14] studied a new improved battery module
arrangement employing two-layer nanoparticle improved phase change
materials. The scheme suggested m × n × p organized where m indicated
the number of Li-ion 18,650 cells, n and p denoted to the number of
primary containers and secondary containers. Every Li-ion cell was
permitted to release at 3 conditions for two different arrangements: 7 ×
7 × 1 and 7 × 1 × 1. Kabeel et al. [15] carried out a numerical study
using Icepak4.2.8 package to investigate the performance of air cooling
of an electronic cabinet including heat sources (electronic circuit
boards) by using axial fan. Shah et al. [16] studied the thermal con­
ductivity and viscosity of synthesized α-alumina nanofluids. Their
studies proved that, α-Al2O3 nanofluid was more thermally stable
compared to traditional cooling liquids. Sultan [17] studied the heat
transfer enhancement within protruding heat sources. He also obtained
a relation for the Nu as a function of Richardson number. Esmaeil et al.
[18] carried out experiments for mixed convection heat transfer from a
heating source cooled by forced nanofluid flow. Wiriyasart et al. [19]
studied experimentally the thermal cooling improvement procedure of 2
microprocessors workstation PC with air cooler unit. Because of space
lack and heat generated in PC and electronic equipment, heat pipes are
more suitable for microprocessors cooling purposes, Siricharoenpanich
et al. [20]. Affecting parameters; heat source inclination angle, the
presence of porous material, used fluids and temperature variation were
put into concern. Jawad et al. [21] used an aluminum chip, nano-silicon
carbide (SiC) tubes in addition to paraffin wax to improve the heater
properties. Experimental results demonstrated much suitability of the
new air heater to run under the weather patterns of Bagdad. Sirichar­
oenpanicha et al. [22] used Ag/Fe3O4 nanofluids mixture for cooling
purposes. The thermal dissipation impact of the coolant flow rate, the
coolant form as well as the heat sink design were considered. Putra et al.
[23] investigated the use of nano-fluids in addition to thermoelectric
cooling for a heat pipe fluid block. The kind and effect of nanofluid,
colder temperature, and thermal device volume concentrations, as PC
heat pumps, were considered at the CPU temperature.
Elsayed et al. [24] studied the combination of the use of helical coils
and nanofluids on thermal performance and pressure drops in the tur­
bulent flow pattern. Nguyen et al. [25–33] have studied the behavior of
the nanofluid Al2O3 nanoparticles – water mixture, which is being
flowed into a closed system intended for cooling of chips and different
electronic equipment. Turbulent flow results showed substantial heat
transfer enhancement with the presence of nanoparticles in distilled.
Shukla et al. [34] and Sheikhalipour et al. [35] studied heat transfer
features and the performance of trapezoidal MCHS in case of laminar
flow using several nanofluids. Nazari et al. [36] tested the PC cooling
process using nano-fluids Alumina and the nanofluid CNT. The gained
results were compared to typical base fluids. The Glycol Ethylene with a
volume of 30% and 50% was also used for the cooling process. Ben
2
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Mansour et al. [37] investigated numerically for cooling high thermal
output microprocessors, the thermal transfer performance of two
nanofluids, water-μAl2O3, and ethylene glycol α-Al2O3. The previous
studies showed great interest in the last 20 years, the form and diameter
of the materials and nanoparticles as well as their concentrations. Thus,
nanofluid was used in this work to improve the thermal proprieties of a
cold medium by the experimental and numerical application of
CuO-Water nanofluid in the different particulate concentrations [27–33,
39].
This study aims to examine the effects of using two common nano­
fluids with various nanoparticles volume fractions on the cooling pro­
cess of microprocessors. The thermal management technique is used to
cool down a heating chip with area of 5 cm by 5 cm with heat rate of 130
W. The two nanoparticles were mixed in water with various volume
fractions. A numerical model is developed and validated with the
experimental data in the present study and with data from the literature.
Fig. 1. Details of experimental apparatus used in the present work.
•
h
2. Experimental setup
Cpnf =
∅ρp Cpp + (1 − ∅)ρbf Cpbf
ρnf
μnf = μbf (1 + 2.5 ϕ)
K nf
=
kbf
(
ks
kbf
)ϕ
u̇m =
)
(8)
ρnf V nf
τ
ṁnf
ρnf Ac
(9)
(10)
where Ac is the cross-section area of fluid flow across heater.
Ac = L1 ∗H − L ∗ h
(1)
(11)
h: height of the case
The Reynolds number is calculated based on the heat sink hydraulic
diamter is calculated as follows:
(2)
ReD =
(3)
ρnf um L
μnf
(12)
Where ρ: density of fluid.
Um: average velocity of the fluid.
L: characteristic length (length of heater).
μ : Dynamic viscosity of the fluid.
(4)
3. Theoretical analysis
In this section, steady state laminar flow condition is applied for
simulating the characteristics of heat transfer and friction of a microchip
using nanofluid. The thermophysical properties of a nanofluid are
described by the nanofluidic thermal conductivity. The value of
Ks=76.5 W/(m∙K) is adopted to be constant [43]. To estimate the
thermophysical properties of the base fluid, temperature dependent
properties were used. For water, the value of Kf is calculated using a
fourth order polynomial function of Kelvin temperature and valid for a
temperature ranging from 274 to 633 K [43]
where n is the number of thermocouples fixed on the inside wall of the
test section. The heat generation in the electric heater is estimated using
Eq. (6). This amount of heat is directly transferred to the fluid in the heat
sink and calculated as follows:
K f = − 0.432 + 0.0057255 × T − 8.078 × 10− 6 T 2 + 1.861
/(
)
× 10− 9 T 3 W Km− 1
(6)
where cos φ is the power factor and is equal to unity.
The surface area of the heat source is given by:
Ah,s = L1 ∗L2 + 4∗(L2 ∗h)
Ah,s Tw, avg − Tb
ṁnf =
The test section was insulated using glass wool (40 mm thick) to
minimize the loss of heat from the side isolated walls of the test section
to the surroundings. The impact of heat flux and nanofluid Reynolds
numbers on the heat transfer characteristics are studied. The steady state
condition is attained after one hour. At these conditions, the average
wall temperature of the heat source ΔTw,avg is calculated using [18]:
∑9
Twi
Tw, avg = i=1
(5)
n
Q = I V cos φ
Q
Where; Tb is the bulk cooling water temperature, (Ti + To)/2, and Ah,s
is the surface area of the heat source. The flow rate of nanofluid is
measured by the help of stopwatch and graduated glass as:
In the present study, the nanofluids used was prepared at the labo­
ratories of Faculty of Pharmacy Mansoura University. The nanoparticles
with 50 nm diameter (ρ = 3890 kg/m3 and Cp = 773 kJ/(kg.K)) are
perfectly mixed with Pure water after adding CTAB (cetyl-trimethyl
ammonium bromide) as a dispersing agent, using the ultrasonic device .
The nanoparticles loading was tested against the settlement and sedi­
mentation did not exist during experiment. The nanofluid preparation
method used in this study is followed the same method proposed in
[38–41]. In these references, stable suspension of the nanoparticle in the
base fluid is approved during the experiment. The thermophysical
properties of the nanofluid are estimated as follows [8]:
ρnf = ∅ ρp + (1 − ∅)ρbf
(
=
(13)
Different models were proposed to calculate the nanofluid viscosity.
In the research on the numerical simulation of nanofluids, the classic
Brinkman model [3] appears to have been used extensively.
On the other hand, the density and specific heat capacity nanofluids
are extensively estimated using Pak and Cho correlation [1] Fig. 1.
The computational domain used in the current CFD simulation is
depicted in Fig. 2. The computational domain was built, meshed, and
simulated using Table 1 ANSYS-Fluent 16.0. The computational domain
consists of 1,464,787 elements. This number of elements were obtained
(7)
where Ah,s is the surface area of heat source, m2, L1: length of the outside
wall, L2: length of the heater, and h: is the height of the heater.
The average heat transfer coefficient from the heater to the liquid
passing in the heat sink is given by [42] as:
3
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Fig. 2. (A). Half of the model; and (B) detailed dimensions of heat sink.
walls and the interfaces between the fluid and the fluid. ANSYS Fluent
16 is used to simulate the problem. Second order upwind scheme
pressure-based solver is applied with SIMPLE algorithm for the pressurevelocity coupling [45]. The solution is conducted and simulated, the
results were obtained at monitor residuals below 10− 6 for the mo­
mentum and energy equation. In addition, both the inlet pressure and
outlet temperatures were monitored as an indicator for the convergence
criteria.
Table 1
Properties of CuO nanoparticles [1].
Material
Specific heat (J/
(kg K))
Thermal conductivity
(W/(m K))
Density (kg/
m3)
copper oxide
(cuo)
aluminum oxide
(al2o3)
551
33
6000
733
40
3960
4. Results and discussion
after the mesh independence test as provided in Table 2. More details
about the mesh characteristics were presented in Table 3. The length of
the inlet and outlet tube selected to be five times the diameter of the
heater to confirm a fully developed flow. Through the experimental
work, estimated (Re) on the basis of the hydraulic diameter was not
exceeding 1800. Therefore, the flow is considered laminar flow through
the calculations [11,44].
Continuity equation:
∂ρ
v)=0
+ (ρ→
∂t
4.1. Model validation
In the present study, the heat transfer characteristics of CuO-Water
nanofluid over pure water for cooling of microprocessors was numeri­
cally and experimentally investigated. Fig. 3(A) represents the various
outlet temperature To and mass flow rate m˙ for the experimental and
numerical model results. It is clear that there is an agreement with a
maximum deviation of about 3.0% between the model and the obtained
experimental results. From Fig. 3(A), when the mass flow rate increased
the temperature decreased. Also, it was found that usage of CuO-Water
nanofluid with ∅ = 0.5% in both experimental and model calculations,
the outlet temperature decreased over the usage of pure water as cooling
(14)
Momentum equation:
∂ρ→
v
v→
v ) = − ∇P + (τ) + ρ→
g
(15)
+ ∇.(ρ→
∂t
Where P, τ, and ρ→
g are the pressure, stress tensor and the body force
Table 3
Mesh details.
resulted due to the gravity effect. The stress tensor τ is given by:
τ = μ(∇→
v + ∇→
v )−
T
2
μ∇.→
vI
3
Number of elements
(16)
Min
Max
Average
Standard deviation
The flow energy equation is expressed as follows:
∂ρE
v h) = ∇.(K∇T)
+ ∇.(ρ→
∂t
1,464,787
Orthogonal quality
(17)
0.22328
1
0.87778
8.5712e-002
Element quality
For the boundary condition, at the test section inlet, a uniform
normal inlet velocity with uniform inlet temperature is assumed. The
speed at the inlet is ranging from 0.077 to 0.145 m/s. Zero (gage)
pressure is defined at the test section outlet. Uniform heat flux was
attained with a value of 40,000 W/m2. No slip boundary is used at the
Min
Max
Average
Standard deviation
6.3831e-002
1
0.75433
0.22371
Table 2
Mesh independence test.
No. of Elements
841,547
904,360
1,105,366
1,225,798
1,464,787
1,595,750
h (W/m2K)
1258.63
1258.95
1259.36
1259.69
1260.41
1260.12
4
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Fig. 3. Validation curves: (A) Outlet temperature vs mass flow rate between water and nanofluid 0.5%, (B) Heat transfer coefficient vs Re for water as cooling fluid,
and (C) Maximum temperature vs heat load for water as cooling fluid.
fluid.
From Fig. 3(B), the variation between the maximum temperature and
heat transfer rate obtained by Nguyen [26] and model calculation
showed the same trend during the presence of distilled water by a
maximum deviation of 13% which could be acceptable. Fig. 3(C) rep­
resents the change of Re with heat transfer coefficient h in case of pure
water as cooling fluid by He et al. [46] versus model calculation; which
can be accepted with a maximum deviation of 8%. Thus, the current
model can be used for calculating other heat transfer parameters.
After validating the model, several cases were performed to show the
variation of properties and enhancement of heat transfer parameters.
The following figures show the simulated results in some cases. Fig. 4
represents the temperature profile across the simulation at varying
speeds at ∅ = 2.25%.
An important point was studying the effect of nanoparticles con­
centration by multiple volume fractions on the Nusselt number at
various inlet velocities. In Fig. 5 at the lowest velocity V = 0.077 m/s,
the Nusselt number is low compared with other velocities. When the
velocity was increased, the Nusselt number increased. The second
observation that when CuO-Water nanofluid was used over pure water,
the Nusselt number increased and the same trend was noticed at the
other velocities till V = 0.145 m/s.
At the lowest velocity (V = 0.077 m/s), an enhancement of 24.8% in
the Nusselt number can be observed when a CuO–water nanofluid with a
5% volume–fraction concentration is used instead of pure water.
Moreover, when the velocity increases to V = 0.145 m/s (for the same
volume fraction), an enhancement of 34.15% in the Nusselt number is
obtained. In case of CuO-Water nanofluid, the thermal resistance R was
observed to be decreased as shown in Fig. 6(A). At Re around 1300 the
maximum decrease was observed to be 7.75%. For nanoparticles with
various concentration over pure water at the same Reynolds number,
heat transfer enhancement occurred with maximum% of 7.56% as
shown in Fig. 6(B).
Fig. 7 shows the variation of processor temperature with Reynolds
number for various nanoparticles concentrations, the same trend ach­
ieved for best enhancement at ∅ = 5% for maximum Reynolds number.
It is also noticed that increasing Re significantly decreases the CPU
temperature and enhances the CPU temperature uniformity index. The
temperature uniformity index is defined as the maximum temperature
difference on the CPU surface as in Fig. 7 (right). This factor is essential
to avoid the thermal stresses resulted from the high temperature
gradient.
Fig. 8-a shows that the enhancement in heat transfer characteristics
for CuO-water nanofluids is better than Al2O3-water especially for
5
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Fig. 4. Temperature distribution through the model at various velocities at ∅ = 2.25%.
Fig. 5. Nusselt number with% volume fraction at various velocities.
higher volume fractions by maximum enhancement of 7.28%. This
enhancement is resulted from the increase in the CuO-water thermal
conductivity compared to Al2O3-water nanofluid. Further, the
maximum and minimum temperature at the heater surface were calcu­
lated and presented in Fig. 8-b and 8-c, respectively. The recommended
operating CPU temperature should be less than 75◦ C. It is noticed that at
low Reynolds number and low nanoparticle volume fraction, the mini­
mum and maximum temperature is higher than the recommended
operating temperature of the CPU. On other hand, using nanofluids in­
crease the pressure drop as depicted in Fig.8-d. The channel pressure
drop increases with increasing the flow Re number and with increasing
the nanoparticle volume fraction. Therefore, there is advantage of heat
transfer enhancement and disadvantage resulted from higher pressure
drop.
To compromise between the thermal enhancement due to the use of
nanofluid and the pressure drop drawback, the thermal hydraulic per­
formance (ζ) is proposed and can be calculated as follow:
Nu
ζ = ( Nuo)1
3
(18)
ΔP
ΔPo
where Nu: Nusselt Number at any volume fraction.
Nuo: Nusselt Number at Pure water.
ΔP: Pressure drop at any volume fraction.
ΔPo: Pressure drop at Pure water.
The results of thermal hydraulic performance are depicted in Fig. 8-e
6
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Fig. 6. (A) Thermal resistance vs Reynolds number at different ∅ and (B) Nusselt number vs Re at different ∅.
Fig. 7. Variation of the CPU temperature with Reynolds number (left); and variation of maximum temperature difference (ΔT = Tmax - Tmin) on the chip surface with
Reynolds number for different volume fraction of CuO nanoparticles.
at various Re numbers and nanoparticles volume fraction. It is evident
that the higher nanoparticle volume fraction with lower Re number are
favorable but with experimental confirmation of slight nanoparticle
settlement. The highest attained thermal hydraulic performance is
around 1.47 at nanoparticle volume fraction of 5% of CuO-water
nanofluid and Re of 800.
8.1% in thermal conductance for the examined operating range was
achieved using nanofluids as a cooling liquid instead of water.
3. The highest attained thermal hydraulic performance is around 1.47
at nanoparticle volume fraction of 5% of CuO-water nanofluid and Re
of 800.
The effect of nanoparticles showed at maximum Reynolds’s number,
when we used CuO nanoparticles an enhancement of 7.125% was
observed over used Al2O3 nanoparticles at Q = 130 W.
5. Conclusions
In this work, the impact of nanoparticles on microchip cooling was
evaluated experimentally and numerically. Water and a CuO–water
nanofluid at six different concentrations (0.5, 0.86, 1.5, 2.25, 3.5, and 5
vol.%) were used as cooling liquids.
The results confirmed that:
CRediT authorship contribution statement
M.M. El-Khouly: Methodology, Data curation, Formal analysis,
Writing - original draft, Writing - review & editing. M.A. El Bouz:
Writing - original draft, Writing - review & editing, Supervision. G.I.
Sultan: Methodology, Writing - original draft, Writing - review & edit­
ing, Supervision.
1. When change the volume fraction for nanoparticles, the heat transfer
characteristic was observed to improve at higher concentrations than
lower one and over pure water with maximum enhancement 34.15%
at 5% volume–fraction concentration at maximum Reynolds number
for the examined operating range.
2. The electronic chip (with an area of 5 cm × 5 cm and dissipated
power of 130 W) under these conditions, has an enhancement of
Declaration of Competing Interest
The authors delacre that there is no conflicts of interest to disclose.
7
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
Fig. 8. (a). comparison between Nusselt number and ∅ for CuO & Al2O3 nanoparticles at maximum velocity; (b) variation of the maximum CPU temperature with
Reynold’s number; (c) variation of the minimum CPU temperature with Reynold’s number; (d) variations of heat sink pressure drops with Reynold’s number at
various nanoparticle volume fractions; and (e) variation of thermal hydraulic performance with Reynold number at various nanoparticle volume fraction.
8
M.M. El-Khouly et al.
Journal of Energy Storage 39 (2021) 102630
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