1. article in mathematical problems in engineering 2020

Research Article
Numerical Simulation of the Natural Convection with Presence of
the Nanofluids in Cubical Cavity
Mohamed Sannad , Abourida Btissam, and Belarche Lahoucine
National School of Applied Sciences, Ibn Zohr University, Agadir, BP 1136, Morocco
Correspondence should be addressed to Mohamed Sannad; mohamedsannad@gmail.com
Received 15 June 2020; Revised 14 October 2020; Accepted 22 October 2020; Published 21 November 2020
Academic Editor: Arkadiusz Zak
Copyright © 2020 Mohamed Sannad et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
This article consists of a numerical study of natural convection heat transfer in three-dimensional cavity filled with nanofluids.
This configuration is heated by a partition maintained at a hot constant and uniform temperature TH. The right and left vertical
walls are kept at a cold temperature TC while the rest is adiabatic. The fluid flow and heat transfer in the cavity are studied for
different sets of the governing parameters, namely, the nanofluid type, the Rayleigh number Ra � 103
, 104
, 105
, and 106
, and the
volume fraction V varying between V � 0 and 0.1. The obtained results show a positive effect of the volume fraction and the
Rayleigh number on the heat transfer improvement. The analysis of the results related to the heat transfer shows that the copper-
based nanofluid guarantees the best thermal transfer. In addition, the increase of the heating section size and Ra leads to an
increased amount of heat. Similarly, increasing the volume fraction improves the intensification of the flow and increases the
heat exchange.
1. Introduction
The flow and heat transfers induced by natural convection
have been considerably studied during these last decades
because of their direct application in various fields of
engineering such as air conditioning, energy efficiency,
cooling of electronic components, etc. The previous
works had mostly focused on the improvement of heat
transfer induced by natural convection, which led to the
development of a new research area based on the mo-
lecular structure of the fluid: modern nanotechnology,
which can produce metallic or nonmetallic particles of
nanometer dimensions. These nanomaterials, with av-
erage sizes below 100 nm, have unique mechanical, op-
tical, electrical, magnetic, and thermal properties and
nanofluids are mixtures of these particles and traditional
fluids such as water, oil, and ethylene glycol. Hence, a
minimal amount of guest nanoparticles, when dispersed
uniformly and suspended stably in host fluids, can
provide improvements in the thermal properties of the
host fluids.
The primary early interest in nanofluids from a tech-
nological viewpoint was the possibility of using these fluids
for cooling purposes. Although the higher conductivity is an
encouraging phenomenon of the cooling capabilities of such
fluids, it is also important not only to reveal the convective
behavior of nanofluids but also to understand the dynamics
of fluids and the theories of heat transfer of nanofluids. Till
now, convective studies of nanofluids have been very limited
compared to experimental and theoretical studies on
conduction.
Numerical work on the natural convection of nanofluids
was carried out by Khanafer et al. [1]. They studied a dif-
ferentially heated cavity with hot and cold vertical walls and
adiabatic horizontal walls. They showed that the rate of heat
transfer increases with an increase in the volume fraction of
the nanoparticles for all the considered values of the Grashof
number. In addition, they have shown that the heat transfer
rate is favored when the volume fraction of the nanoparticles
increases. Among the works dealing with heat transfer in the
presence of nanoparticles, Murshed et al. [2] showed that the
thermal conductivity increases with the increase of the
Hindawi
Mathematical Problems in Engineering
Volume 2020,Article ID 8375405, 17 pages
https://doi.org/10.1155/2020/8375405

volume concentration, and they also found that, for a vol-
ume concentration of 5%, the improvement of the effective
thermal conductivity is about 33% for the nanotubes and of
30% for spherical nanoparticles. They found that the size and
shape of the particles have a significant influence on the
thermal conductivity of nanofluids.
Similar work was carried out by Jou and Tzeng [3] inside
a differentially heated cavity. They also used the stream
function vorticity formulation in a way identical to that used
in a previous study by Khanafer et al. [1]. In addition to the
Grashof number effects, they then described the effect of the
cavity aspect ratio (width/height) on thermal behavior.
However, they concluded that, at 20% volume fractions,
Newtonian behavior of the fluid is doubtful and it is ex-
tremely difficult to make stable nanofluids to use. Also, at
such volume fractions, Newtonian behavior of the fluid is
doubtful. A recent numerical study by Ho et al. [4] in a
vertical square enclosure filled with a nanofluid (water-
Al2O3) shows clearly the effects of different parameters on
flow simulation. They examined the effects of uncertainties
due to the adoption of different models for thermal con-
ductivity and dynamical viscosity. These authors found that
the heat transfer through the enclosure could be enhanced or
attenuated depending on the models used for the dynamical
viscosity of the nanofluid. Hwang et al. [5] have made several
measurements on the thermal conductivity of nanofluids
and have shown that this parameter depends strongly on the
volume fraction of the suspended particles and the thermal
conductivity of the base fluid. Recently, Oztop and Abu-
Nada [6] have numerically studied heat transfer and fluid
flow as a function of buoyancy forces in a partially heated
enclosure filled with nanofluids containing different types of
nanoparticles. They showed that the improvement of the
heat transfer was more pronounced for low values of the
aspect ratio. They found that, for all the considered Rayleigh
numbers, the average Nusselt number increases as the
volume fraction of the nanoparticles increases.
Anilkumar and Jilani [7] presented a numerical inves-
tigation on the natural convection of a mixture of ethylene
glycol and aluminum oxide in an enclosure heated by an
isothermal partition located on its lower wall. The study
indicates the influence of the nanoparticle volume fraction,
the Rayleigh number, and the shape ratio of the enclosure on
the dynamical and thermal behavior of the fluid. The results
obtained show that the heat transfer improves with the
increase of the volume fraction of the nanoparticles, and the
presence of the nanoparticles in the base fluid modifies the
structure of the flow. Natural convection in an inclined
square enclosure heated by a source placed in the center of
the left wall was studied by Öğüt [8]. The cavity was filled
with a mixture of water and nanoparticles (Cu, Ag, CuO,
Al2O3, and TiO2), and the results show that the type of
nanoparticle is a major factor in improving heat transfer.
Hussein et al. [9] investigated numerically the magneto-
hydrodynamic (MHD) natural convection flow of Cu-water
nanofluid in an open enclosure using lattice Boltzmann
method (LBM) scheme. They found that the absolute values
of stream function decline significantly by increasing
Hartmann numbers while these values rise by increasing
Rayleigh numbers. In addition, they have shown that,
depending on the value of the Hartmann and Rayleigh
numbers, the solid volume fraction has a significant influ-
ence on the function of flux and heat transfer. The authors in
[10] studied numerically the natural convection in three
types of inclined wavy cavities filled with Al2O3-water and
Ag-water nanofluids and subjected to a discrete isoflux
heating from its left sidewall. They found that the geometry
of the wavy cavity has a cursive role on the flow and thermal
fields pattern. Also, the local Nusselt number along the heat
source increases with the solid volume fractions and wave
amplitudes and the heat functions increase for both nano-
fluids and base fluids with these parameters. The same
authors have other articles in this context (Hussein et al.
[11–13]).
The effects of the inclination angle of an external
magnetic force on the natural convection inside a cubical
cavity filled with a carbon nanotube- (CNT-) water nano-
fluid are investigated by Al-Rashed et al. [14]. They found
that, for larger Rayleigh number, the heat transfer is in-
creased significantly with the percentage of CNT particles.
Moreover, they observed that a repelling effect of the
magnetic force inhibits the heat transfer by 50% when Ha is
increased from 50 to 100. In another work [15], they in-
vestigated the effect of magnetic field on convective heat
transfer of highly electrically conductive fluids taking into
consideration the case of a low-conductive LiNbO3 oxide
melt. Chand et al. [16] presented a numerical investigation of
the thermal instability in a low Prandtl number nanofluid in
a porous medium. They indicated that the Prandtl and Darcy
numbers have a destabilizing effect while the Lewis number
and modified diffusivity ratio have a stabilizing effect for the
stationary convection.
Hussein et al. [17, 18] have studied two numerical cases
in a three-dimensional cavity filled with nanofluids. The first
study concerns natural convection and the second the mixed
convection. For the first one, they found that the generation
of total entropy becomes insignificant under the effect of the
tilt angle for the low Rayleigh number. In addition, the flow
circulation and the average Nusselt number increase with
Rayleigh number. In the second study, they indicated that
the average Nusselt number increases with the increase in
the Richardson number and the solid volume fraction of the
nanoparticles.
Kolsi et al. [19] performed a numerical investigation on
the effect of a periodic magnetic field inside a cubical en-
closure filled with nanofluids (Multi-Walled Carbon
Nanotubes). They mentioned that the oscillation period, the
magnitude of the magnetic field, and adding nanoparticles
have an important effect on heat transfer, temperature field,
and flow structure. Haq et al. [20–22] analyzed the heat
transfer with a presence of the Magnetohydrodynamic
(MHD) water-based Single-Wall Carbon Nanotubes
(SWCNTs), the water-based copper oxide (CuO) nanofluid,
and uniform magnetic field. They, respectively, concluded
that the increase of the Rayleigh number enhances the
stream flow and isotherms behaviors. However, the heat
transfer rate inside the cavity is decreasing with the increase
in volume fraction, Hartmann number, and LT due to
2 Mathematical Problems in Engineering

dominant convection. In addition, this transfer rate is in-
creased due to increase in Rayleigh number and wavelength
parameter. In addition, they observed that Darcy and
Hartmann number do not have significant effects on the
temperature distribution.
Muhammad et al. [23] investigated numerically the
mixed convection flow of Ag-ethylene glycol nanofluid flow
in a cavity having thin central heater. Their results, which are
expressed by isotherms, streamlines, velocity field, and av-
erage Nusselt numbers, show that the heat transfer for
Fourier’s law is improved as compared to the results ob-
tained for the case of Cattaneo–Christov heat flux. The
augmentation of heat transfer in most of these studies was
attributed to the virtue of the abnormal enhancement of the
nanofluid thermal conductivity [24–26].
In a study by Guiet et al. [27], the laminar natural
convection of a mixture of water and copper nanoparticles
was numerically studied by the Boltzmann method in a
square cavity heated from below and cooled laterally. The
heating is provided by a block, which is either kept at a
constant temperature or subjected to constant heat flow.
This authors used the Brinkman [28] and the Patel et al. [29]
models to calculate ,respectively, the effective viscosity and
thermal conductivity of the nanofluid. Almeshaal et al. [30]
had studied numerically the effect of water-based hybrid
nanofluid of CNT-aluminum oxide on natural convection
inside the T-shaped cavity. They found that the heat transfer
performance manifests as a function of the size, volumetric
percentage of nanoparticles, fraction of CNT composites,
and Rayleigh number. Similarly, with the explosive advances
in nanofluids in recent years, adding nano-sized metal
particles in impinging cooling fluids has also received great
interest from the community of process intensification for
heat dissipation equipment [31–34].
The industrial applications of the heat transfer by natural
convection are the electronic component cooling, the nu-
clear reactors, and the heat losses in solar collectors. Our
study is part of this framework, particularly related to the
intensification of heat change in the electronic component.
The major studies cited considered two-dimensional cavity,
but in our case, we are going to consider three-dimensional
cavity. The fundamental parameters of the problem are the
Rayleigh number, the volume fraction of the nanoparticles,
and the dimensions and position of the heating block. The
results of the study show that the heat transfer improves with
the increase of the volume fraction of the nanoparticles
independently of the boundary conditions applied on the
heating block. Flow and heat transfer are significantly af-
fected by the size of the heating source. Hence, the purpose
of the present investigation is to study numerically the
laminar natural convection in a cubical enclosure filled with
nanofluid. The temperature distributions, the velocity pat-
terns, and the heat transfer rates are analyzed and discussed
in this paper.
2. Problem Formulation
The physical model considered is shown schematically in
Figure 1. It consists of three-dimensional cavity of length
(H), filled with nanofluids and heated by the partition.
The mathematical model used in this paper is based on
the Navier–Stokes and energy equations. These equations
are discretized by the finite volume method, taking into
account the Boussinesq approximation and neglecting the
viscous dissipation. The physical parameters are given by the
following equations:
(i) The density [35]:
ρnf �(1 − ϕ)ρf + ϕρnp. (1)
(ii) The heat capacitance of the nanofluid [35]:
ρCp
􏼐 􏼑nf
�(1 − ϕ) ρCp
􏼐 􏼑f
+ ϕ ρCp
􏼐 􏼑np
. (2)
(iii) Thermal expansion coefficient [35]:
βnf �(1 − ϕ)βf + ϕβnp. (3)
(iv) The dynamical viscosity of the nanofluid:
Brinkman [28] has extended the Einstein formula to
cover a wide range of volumetric concentrations,
which gives the following relation:
μnf �
μf
(1 − ϕ)2.5. (4)
(v) Thermal diffusivity of nanofluids:
αnf �
knf
ρCp
􏼐 􏼑nf
. (5)
(vi) The thermal conductivity of nanofluids:
The formula of Maxwell [36] is given by
knf
kf
�
knp + 2kf − 2 kf − knp
􏼐 􏼑ϕ
􏽨 􏽩
knp + 2kf + ϕ kf − knp
􏼐 􏼑
􏽨 􏽩
, (6)
knf, kf, and knp are, respectively, the thermal conduc-
tivities of the nanofluid, the basic fluid, and the solid
Mathematical Problems in Engineering 3

nanoparticles. The thermophysical properties of the
basic fluid, water, and the considered nanoparticles are
given in Table 1.
The governing equations can be written as follows:
zu
zx
+
zv
zy
+
zw
zz
� 0,
ρnf
zu
zt
+ u
zu
zx
+ v
zu
zy
+ w
zu
zz
􏼠 􏼡 � −
zP
zx
+ μnf
z2
u
zx2 +
z2
u
zy2 +
z2
u
zz2
􏼠 􏼡,
ρnf
zv
zt
+ u
zv
zx
+ v
zv
zy
+ w
zv
zz
􏼠 􏼡 � −
zP
zy
+ μnf
z2
v
zx2 +
z2
v
zy2 +
z2
v
zz2
􏼠 􏼡 − ρnfg,
ρnf
zw
zt
+ u
zw
zx
+ v
zw
zy
+ w
zw
zz
􏼠 􏼡 � −
zP
zz
+ μnf
z2
w
zx2 +
z2
w
zy2 +
z2
w
zz2
􏼠 􏼡,
zT
zt
+ u
zT
zx
+ v
zT
zy
+ w
zT
zz
� αnf
z2
T
zx2 +
z2
T
zy2 +
z2
T
zz2
􏼠 􏼡.
(7)
In order to limit the number of variables involved in the
problem under study, the system of equations is dimen-
sionalized by considering dimensionless parameters based
on reference quantities as presented in Table 2.
Therefore, the governing equations of continuity, mo-
mentum, and energy conservation for stable laminar flow in
Cartesian coordinates can be written in the following di-
mensionless form:
zU
zX
+
zV
zY
+
zW
zZ
� 0,
U
zU
zX
+ V
zU
zY
+ W
zU
zZ
� −
zP
zX
+
μnf
αfρnf
z2
U
zX2 +
z2
U
zY2 +
z2
U
zZ2
􏼠 􏼡,
U
zV
zX
+ V
zV
zY
+ W
zV
zZ
� −
zP
zY
+ Ra ∗ Pr ∗ θ ∗
ρfβnf
ρnfβf
+
μnf
αfρnf
z2
V
zX2 +
z2
V
zY2 +
z2
V
zZ2
􏼠 􏼡,
U
zW
zX
+ V
zW
zY
+ W
zW
zZ
� −
zP
zZ
+
μnf
αfρnf
z2
W
zX2 +
z2
W
zY2 +
z2
W
zZ2
􏼠 􏼡,
U
zθ
zX
+ V
zθ
zY
+ W
zθ
zZ
�
αnf
αf
z2
θ
zX2 +
z2
θ
zY2 +
z2
θ
zZ2
􏼠 􏼡.
(8)
The adopted thermal boundary conditions are as follows:
θ � −0.5, on the cold wall, and θ � 0.5, on the heated
partition.
(zθ/zn) � 0 on the adiabatic walls.
The adopted hydrodynamic boundary conditions are as
follows:
U � V � W � 0 for all walls.
The average Nusselt number, Nua, is defined as the
integral of the temperature flux through the vertical right
cold wall and formulated as
Nua �
knf
kf
􏽚
S
∇
→
θ · e
→
xdydz. (9)
3. Materials and Methods
3.1. Mesh. It is the subdivision of the studied field into
volume elements in the X direction, Y direction, and Z di-
rection whose intersection represents a node, where the
variables P and Tare found. The U, V, and W components of
the velocity vector are located in the middle of the faces
connecting two adjacent nodes. The discretization of the
domain is obtained by a mesh consisting of a network of

points (nodes). Thus, a volume element (control volume) is
defined around each node. In order to obtain a fine mesh at
the level of the active walls (X � 0 and X � 1), the computa-
tional domain has been discretized by adopting a mesh that is
non-uniform in the X direction and uniform in the Y and Z
directions. To test the influence of the number of nodes on the
obtained results, we solved the transport equations for mesh
sizes of 41 × 41 × 41, 51 × 51 × 51, 61 × 61 × 61, 71 × 71 × 71, and
81 × 81 × 81 and Ra � 105
(Table 3). Following this mesh
sensitivity test, we chose the 61 × 61 × 61 mesh.
3.2. Validation. A numerical code was developed based on
the finite volume method to discretize the governing equa-
tions. The conservation equations of momentum coupled
with the continuity equation are solved using the algorithm
SIMPLE. The resolution of the obtained discrete algebraic
system is based on the Alternating Direction Implicit (ADI)
scheme. The code was then validated by comparing our results
with those obtained in two different cases for a differentially
heated three-dimensional cavity: the first case concerns a
three-dimensional cavity filled with nanofluids (Ravnik et al.
[37]) and the second is related to a three-dimensional cavity
filled with air (Lo et al. [38] (Table 4)). It is clear that the
results of our code are in good agreement with those proposed
by Ravnik et al. [37] (Figure 2 and Table 5). Indeed, the
maximum deviation is of the order of 3.59%.
3.3. Convergence Criterion. The partial differential equa-
tions and the method applied to solve the resulting alge-
braic system require iterative calculation. In an iterative
process, we must start with an evaluation of the solution,
which does not automatically check the equation to be
solved. When subsequent iterations produce no significant
change in the values of the dependent variables ϕ, con-
vergence is said to have been achieved. In practice, this
convergence is expressed by a test used to stop the iterative
process, also called the convergence criterion, which de-
pends on the nature of the problem and the objectives of
the calculation.
By adopting the procedure used by Penot [39], this test
can be carried out, for a given variable, as follows:
􏽘
i max,j max,k max
i,j,k�1
ϕn+1
i,j,k − ϕn
i,j,k
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
ϕn
i,j,k
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
􏼌
≤ 10−5
, (10)
where ϕ represents the field variables (U, V, W, T, P). The
subscripts i, j, and k are the points of the mesh. The
Tc
Th
Z
e
d
H
X
H
H
g
Y
E
Tc
Figure 1: Studied configurations and coordinates.
Table 1: Thermophysical properties of pure water and nanoparticles.
ρ (kg·m−3
) β (K−1
) k (W·m−1
·K−1
) Cp (J·kg−1
·K−1
)
Pure water 997.1 21 × 10−5
0.613 4179
Al2O3 3970 0.85 ×10−5
40 765
Cu 8933 1.67 ×10−5
400 385
TiO2 4250 0.90 ×10−5
8.9538 686.2
Table 2: Dimensionless parameters.
Dimensionless variables References variables
Spatial coordinates (X, Y, Z) � ((x, y, z)/Lref ) Lref � H
Velocity (U, V, W) � ((u, v, w)/Vref ) Vref � (αf/H)
Pressure P � ((p + ρ0gy)/Pref ) Pref � (ρnfα2
f/H2
)
Temperature θ � ((T − Tref )/ΔT) ΔT � TC − TF with Tref � ((TC + TF)/2)

exponents n and n + 1 represent the previous iteration and
the current iteration, respectively. The parameter ε1 � 10− 5
has been chosen small enough to avoid truncation errors
while remaining above rounding errors [40, 41].
4. Results and Discussion
The results presented in this article are obtained for
Rayleigh numbers Ra ranging between 103
and 106
and the
Table 3: Mesh sensitivity of the average Nusselt number to the grid sizes.
41 × 41 × 41 51 × 51 × 51 61 × 61 × 61 71 × 71 × 71 81 × 81 × 81
Numoy 4.948685 4.921563 4.898485 4.895235 4.892066
Table 4: Comparison of the average Nusselt number between our results and those of Lo et al. [38].
Lo et al. [38] Our study Relative gap (%)
Ra � 103
1.0710 1.0770 0.56
Ra � 104
2.0537 2.0932 1.9
Ra � 105
4.3329 4.4329 2.3
Ra � 106
8.6678 8.8997 2.6
0.0
–5
–4
–3
–2
–1
0
V
(X)
1
2
3
4
5
–5 –4
Ravnik Present
work
–3 –2 –1 0
U (Y)
1 2 3 4 5
1.0
0.8
0.6
0.4
Y
0.2
0.0
0.2 0.4 0.6
X
0.8 1.0
0.0
0.1
0.2
0.0
0.1
0.2
Figure 2: Comparison of the velocity between our results and those of Ravnik et al. [37] for ϕ � 0, 0.1, and 0.2.
Table 5: Comparison of the mean Nusselt number between our results and those of Ravnik et al. [37] for diﬀerent types of nanoﬂuids and
values of volume fraction (ϕ � 0.0, 0.1, and 0.2).
Ra
Water Water-Cu Water-Al2O3 Water-TiO2
ϕ � 0.0 ϕ � 0.1 ϕ � 0.2 ϕ � 0.1 ϕ � 0.2 ϕ � 0.1 ϕ � 0.2
Ravnik et al. 103
1.071 1.363 1.758 1.345 1.718 1.297 1.598
This work 103
1.080 1.371 1.767 1.353 1.727 1.304 1.607
Relative gap 103
0.84% 0.58% 0.51% 0.59% 0.52% 0.53% 0.56%
Ravnik et al. 104
2.078 2.237 2.381 2.168 2.244 2.115 2.132
This work 104
2.121 2.277 2.415 2.206 2.275 2.153 2.162
Relative gap 104
2.06% 1.78% 1.42% 1.75% 1.38% 1.79% 1.4%
Ravnik et al. 105
4.510 4.946 5.278 4.806 4.968 4.684 4.732
This work 105
4.672 5.095 5.409 4.948 5.086 4.823 4.845
Relative gap 105
3.59% 3.01% 2.48% 2.95% 2.37% 2.96% 2.38%

volume fraction equal to 0 and 0.1. The Prandtl number is
fixed at Pr � 6.2 (water). Isotherms and streamlines are
presented in order to illustrate the nanofluid motion and
the heat transfer within the cavity. Interesting results have
been found, in terms of fluid flow and heat transfer through
the cavity, depending on the adopted values of the gov-
erning parameters and also the nature of the used
nanofluid.
4.1. Flow and Temperature Fields. Figure 3 shows the dis-
tribution of the temperature in the three-dimensional cavity
for different lengths d, Ra � 105
, and ϕ � 0.04. The figure
illustrates that fluid movement occurs from the partition to
the cold walls so that the heat transfer rate is maintained
permanently in the cavity and this phenomenon is done by
convection due to the high thermal gradients in the active
walls, for that value of Ra. The increase in the heating
surface, resulting from the increase in the length d, naturally
leads to an increase in the energy supplied to the heat
transfer fluid. In order to have a better view of the tem-
perature profile within the cavity (in 3D), different planes
were considered. Thus, the plan chosen for the rest of our
study will be that corresponding to Z � 1/2 and which passes
perpendicularly through the middle of component 1. This
plane suitably represents the flow and heat transfer within
the cavity and being the most representative. The results
presented are the results of simulations carried out by taking
a Rayleigh number varying between 103
and 106
and a
heating partition varying from 20 to 80% of the left vertical
wall.
4.2. Isotherms and Streamlines. Figure 4 shows the current
lines (left) and isotherms (right) for the nanofluid (water-
Al2O3) in the plane Y � 0.5, for different dimensionless
partition lengths, Ra � 105
, and volume fraction ϕ � 4%. It
can be seen that the increase in the length of the heating
partition leads to an increase in flow resistance and con-
sequently the intensity of the longitudinal flow decreases.
For a length d � 0.75, the distance between the partition and
the vertical wall of the cavity (located at Z � 1) becomes
small and consequently causes a decrease in the intensity of
the longitudinal flow of the fluid passing through the
passage located between the planes located between
Z � 0.75 and Z � 1. The fluid that is heated by the block
moves towards the adiabatic upper wall, where it splits into
two flows heading towards the cold vertical walls. This
configuration leads to the formation of almost identical
counter-rotating cells and a symmetrical flow structure
compatible with the symmetry of the boundary conditions.
Note also that the increased length of the partition con-
tributes to the generalization of the flow in almost the entire
cavity through larger, resistant convective cells. The cor-
responding isotherms become more constricted near the
cold vertical walls.
Figure 5 illustrates the structure of the left streamlines
and the right isotherms in plan Z for nanofluid (Al2O3)
and pure water with the length d � 0.5; the temperature
distribution has different behavior near the active zones
for different values of the Rayleigh number. In fact, in-
creasing “Ra” improves the heat transfer from the warm
walls of the cavity to the cold ones as shown in this figure.
For the low value of Rayleigh number (Ra � 103
and 104
),
the viscous forces are greater than the buoyancy forces
and diffusion is the main heat transfer mode. The figure
shows that the flow consists of two cells occupying the
entire cavity whose nuclei are located in the center. For
low Ra, the viscosity determines the intensity of the
convection motion, due to the decreasing of the driving
force for both fluids, that is, the water, which has the
greatest intensity for these moderate Rayleigh numbers.
The fluid that is heated by the heating walls moves to the
adiabatic upper wall, where the flow goes to the right and
left vertical cold walls, because the density of the heated
fluid decreases and so moves upwards, and that is the flow
intensity of the pure fluid which is the strongest. The
temperature field is stratified along the diagonal of the
cavity and the thermal gradients are weak in the vicinity
of the active walls. On the other hand, for high Ra, the
nanofluid circulates faster than water due to increasing of
the buoyancy force. The shape of the isotherms changes
under the effect of the viscosity, which is due to the
change in the heat transfer mode, which goes from
conduction to convection when increasing the Ra. The
fluid motion drives the heat of the active areas through
the cavity. When Ra � 106
, the thermal boundary layers
become thinner and the isotherms become laminated in
the central region of the cavity. The increase of the
stratification is a function of the position and the di-
mension of the heated source (Figure 4). For Ra � 103
, the
isotherms display a symmetrical structure with respect to
the vertical plane passing through X � 1/2. For a fixed
value of the Rayleigh number, the degree of increase of
the stratification with the increase of the dimensionless
length of the heating source becomes important. The
profiles show that, for the low values of Ra (Ra ≤ 104
), the
temperature increases away from the left cold wall
(θ � −0.5) with the increase of Ra until reaching the value
θ � 0.175 for X � 1/2 above the heating partition. This
temperature decreases gradually until reaching the value
−0.5 on the opposite cold wall of the enclosure. For
Ra ≥ 105
, the temperature increases from the value −0.5 at
the level of the cold wall located at X � 0 until reaching its
maximum value. This behavior is due to the buoyancy
force that becomes important and subsequently the heat
transfer mode goes from conduction to convection. This
will lead to significant heat transfer within the cavity for
the nanofluids and the base fluid (water) and much of this
transfer is by convection. It is also noted that, for different
values of the Rayleigh number, as the heat exchange
surface increases, consequently the temperature increases
significantly.
In this part, the influence of different types of nano-
particles (Al2O3, Cu, and TiO2) dispersed in the base fluid on
the heat transfer is studied. Numerical simulations are
performed for different Ra and volume fraction ϕ � 0.04. The
comparisons conducted between the three types of nano-
particles concern primarily the dynamic and thermal fields,

Y
Z
T
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.1
–0.15
–0.2
–0.25
–0.3
–0.35
–0.4
–0.45
X
(a)
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.1
–0.15
–0.2
–0.25
–0.3
–0.35
–0.4
–0.45
T
Y
Z
X
(b)
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.1
–0.15
–0.2
–0.25
–0.3
–0.35
–0.4
–0.45
T
Y
Z
X
(c)
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.1
–0.15
–0.2
–0.25
–0.3
–0.35
–0.4
–0.45
T
Y
Z
X
(d)
Figure 3: 3D temperature ﬁeld for ϕ � 0.04 of the Al2O3. (a) d � 0.25. (b) d � 0.5. (c) d � 0.75. (d) d � 1.
1
0.8
0.6
Z
0.4
0.2
0
0 0.2 0.4 0.6
X
0.8 1
Y
Z
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.3
–0.35
–0.4
–0.45
–0.1
–0.15
–0.2
–0.25
T
X
T
X
X
X
X
X
X
X
(a)
1
0.8
0.6
Z
0.4
0.2
0
0 0.2 0.4 0.6
X
0.8 1
Y
Z
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.3
–0.35
–0.4
–0.45
–0.1
–0.15
–0.2
–0.25
T
X
T
X
X
X
X
X
X
X
X
X
X
X
(b)
1
0.8
0.6
Z
0.4
0.2
0
0 0.2 0.4 0.6
X
0.8 1
Y
Z
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.3
–0.35
–0.4
–0.45
–0.1
–0.15
–0.2
–0.25
T
X
T
X
(c)
1
0.8
0.6
Z
0.4
0.2
0
0 0.2 0.4 0.6
X
0.8 1
Y
Z
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
–0.05
–0.3
–0.35
–0.4
–0.45
–0.1
–0.15
–0.2
–0.25
T
X Z
T
T
T
T
T
T
X
X
X
X
X
X
X
X
X
X
(d)
Figure 4: Isotherms for (water-Al2O3) Ra � 105
, ϕ � 4%, and diﬀerent lengths d according to plane Y � 0.5. (a) d � 0.25. (b) d � 0.5. (c)
d � 0.75. (d) d � 1.

and then the heat transfer. Figure 6 illustrates the isotherms
and the streamlines for different types of nanoparticles
(Al2O3, Cu, and TiO2) and a pure fluid in the Z � 1/2 plane,
the volume fractions varying between 0 and 0.04, and the
Rayleigh number (Ra � 104
, 105
, and 106
). The comparison
between the nanofluids and the pure fluid shows a significant
deviation of the isotherms by increasing the Rayleigh
number and the volume fraction. For Ra � 103
, these
Y
X
1
0.8
0.6
0.4
0.2
0
–0.45
–0.4
–0.35
–0.3
–0.25
–0.2
–0.15
–0.1
–0.05
0
0.5
1.0
0.15
0.2
0.25
0.3
0.35
0.4
0.45
T
0 0.2 0.4 0.6 0.8 1
X
Y
Y
Z
X
|ψ|f,max = 1.543179
|ψ|nf,max = 1.342420
(a)
Y
X
Y
1
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1
X
–0.45
–0.4
–0.35
–0.3
–0.25
–0.2
–0.15
–0.1
–0.05
0
0.5
1.0
0.15
0.2
0.25
0.3
0.35
0.4
0.45
T
Y
Z
X
|ψ|f,max = 7.509157
|ψ|nf,max = 7.458925
(b)
Y
X
Y
1
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1
X
–0.45
–0.4
–0.35
–0.3
–0.25
–0.2
–0.15
–0.1
–0.05
0
0.5
1.0
0.15
0.2
0.25
0.3
0.35
0.4
0.45
T
Y
Z
X
|ψ|f,max = 18.09838
|ψ|nf,max = 18.75187
(c)
Figure 5: Streamlines and isotherms in the cavity for nanofluid (Al2O3) and pure water, length d � 0.5 and ϕ � 0.04 in plane Z � 1/2. (a)
Ra � 104
. (b) Ra � 105
. (c) Ra � 106
.

Y
X
|ψ|f,max = 7,509157
|ψ|nf,max = 7,458925
(a)
Y
X
|ψ|f,max = 7,509157
|ψ|nf,max = 7,569393
(b)
Y
X
|ψ|f,max = 7,509157
|ψ|nf,max = 7,410663
(c)
1
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1
Y
X
(d)
1
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1
Y
X
(e)
1
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1
Y
X
(f)
Y
X
|ψ|f,max = 7,509157
|ψ|nf,max = 7,458925
(g)
Y
X
|ψ|f,max = 7,509157
|ψ|nf,max = 7,569393
(h)
Y
X
|ψ|f,max = 7,509157
|ψ|nf,max = 7,410663
(i)
1
0.8
0.6
0.4
0.2
0
Y
0 0.2 0.4 0.6 0.8 1
X
(j)
1
0.8
0.6
0.4
0.2
0
Y
0 0.2 0.4 0.6 0.8 1
X
(k)
1
0.8
0.6
0.4
0.2
0
Y
0 0.2 0.4 0.6 0.8 1
X
(l)
Y
X
|ψ|f,max = 18,09838
|ψ|nf,max = 18,75187
(m)
Y
X
|ψ|f,max = 18,09838
|ψ|nf,max = 18,75187
(n)
Y
X
|ψ|f,max = 18,09838
|ψ|nf,max = 18,57277
(o)
Figure 6: Continued.

1
0.8
0.6
0.4
0.2
0
Y
0 0.2 0.4 0.6 0.8 1
X
(p)
1
0.8
0.6
0.4
0.2
0
Y
0 0.2 0.4 0.6 0.8 1
X
(q)
1
0.8
0.6
0.4
0.2
0
Y
0 0.2 0.4 0.6 0.8 1
X
(r)
Figure 6: Streamlines (top: (a, b, c), (g, h, i) and (m, n, o) respectively for Ra � 103
, Ra � 105
and Ra � 106
) and isotherms (bottom: (d, e, f), (j,
k, l) and (p, q, r) respectively for Ra � 103
, Ra � 105
and Ra � 106
) for pure water (—) and for different nanofluids (----) (Al2O3 (a, d, g, j, m
and p), Cu (b, e, h, k, n and q) and TiO2 (c, f, i, l, o and r)) in the plane Z � 1/2.
Ra = 104 Ra = 103
Ra = 105
Eau
Al2O3
Ra = 106
Eau
Ra = 103
Al2O3
V
(X)
1
2
3
4
0
–6
–5
–4
–3
–2
–1
–7
0.0 0.2 0.4 0.6
X
0.8 1.0
0.2 0.4 0.6 0.8 1.0
0.0
X
–180
–160
–140
–120
–100
–80
–60
–40
–20
0
20
40
V
(X)
(a)
Eau
Al2O3
Ra = 104
Ra = 103
Ra = 105
Ra = 106
Eau
Al2O3
Ra = 10
3
V
(X)
1.0
0.5
–1.0
–1.5
–0.5
0.0
0.0 0.2 0.4 0.6
X
0.8 1.0
0.2 0.4 0.6 0.8 1.0
0.0
X
–300
–200
–100
0
100
200
300
V
(X)
(b)
Eau
Al2O3
Ra = 104
Ra = 103
Ra = 105
Ra = 106
Eau
Al2O3
V
(X)
10
5
0
–5
–10
0.0 0.2 0.4 0.6
X 0.8 1.0
Ra = 103
Ra = 10
4
0.2 0.4 0.6 0.8 1.0
0.0
X
–300
–200
–100
0
100
200
300
V
(X)
(c)
Eau
Al2O3
Ra = 104
Ra = 103
Ra = 105
Eau
Al2O3
Ra = 106
V
(X)
10
15
5
0
–5
–10
–15
0.0 0.2 0.4 0.6
X 0.8 1.0
0.2 0.4 0.6 0.8 1.0
0.0
X
–300
–200
–100
0
100
200
300
V
(X)
(d)
Figure 7: Profiles of the vertical component of the velocity (V) along the horizontal median of the enclosure for water-Al2O3 and different
lengths. (a) d � 0.25. (b) d � 0.5. (c) d � 0.75. (d) d � 1.

isotherms are nearly parallel to the vertical walls, indicating
that, for small values of the Rayleigh number, the heat
transfer is practically conductive. For Ra � 105
, the isotherms
are slightly deformed with a small variation following y.
Beyond this value of Ra, the deformation of the isotherms
becomes more evident and horizontal stratification begins to
settle in the center of the cavity. For Ra � 106
, we notice that
the vertical gradient of the temperature becomes almost
constant at the heart of the cavity and subsequently a
horizontal stratification settles there. The values of ψ (the
function of the current) increase with the increasing of
Rayleigh number. Thus, it is shown that the flow of the pure
fluid is stronger than that of the nanofluid for Ra � 103
. The
streamlines become tighter close to the sidewalls, and the
shape of the cells changes completely for Ra ≥ 105
. The flow
of the nanofluids becomes stronger. According to this figure,
the water and the different mixtures have the same dynamic
and thermal behavior. Furthermore, Figure 6 confirms that
the copper nanoparticles allow a better heat exchange by
comparing with Alumina and Titanium and the type of
nanoparticles is a main factor for the improvement of the
heat transfer.
4.3.Velocity. In order to justify more the flow of the fluid in
the cavity, Figure 7 shows the profiles of the vertical
component of the velocity (V) along the horizontal median
of the enclosure, for different lengths and Rayleigh num-
bers; it increases under the effect of the buoyancy force with
the increase of Ra. We observe a decrease in the intensity of
the fluid flow at the cold side walls and an increase in the
heated walls. This rise increases with Rayleigh number and
reaches its maximum at the center of the heated walls,
where the temperature is maximum. This is due to the
increase in the intensity of the thermal thrust forces and
therefore to the predominance of convective heat transfer.
In addition, it is useful to mention that the flow becomes
stagnant in the 0.35 ≤ X ≤ 0.65 zone where the heating
partition is located, and this is for all lengths d ≥ 0.5. It is
also observed that the behavior of the dynamic field is
symmetrical with respect to the plane X � 0.5. Therefore,
the fluid is heated significantly and relaxes with a decrease
in density. It follows a rise of the fluid upwards and the
speed of the flow becomes important.
Figure 8 shows the vertical component velocity pro-
files at the center line for different Rayleigh number values
(Ra � 103
, 104
, 105
, and 106
) and different nanofluids.
From this figure, we notice that the vertical velocity (V)
reaches a maximum value near the adiabatic sidewalls, for
all types of nanoparticles, where the fluid changes di-
rection and V decreases towards negative values. The
difference between the three types of nanofluids is small
and the vertical component of the velocity is not signif-
icantly affected by the type of nanoparticles. For Ra � 103
,
we observe that the values of the speed are higher in the
case of base fluid, and the addition of nanoparticles
contributes to slowing down of the fluid in the vertical
direction. Decreasing speed results in a decrease in
convective heat transfer. However, since much of the heat
exchange is conducted by conduction, the decrease in the
speed of the nanofluids has no visible influence on the
overall heat transfer, which remains better than that of the
water due to the high value of the thermal conductivity of
the nanofluid. In the case of dominant convection
(Ra � 106
), we observe that the velocities reached by
nanofluids are greater than those corresponding to base
fluid. Thus, the use of nanofluids modifies the velocity
profiles and, as a result, the temperatures and the heat
transfer are increased. When comparing the velocity
profiles between the nanofluid and the base fluid, only
Water
Al2O3
Cu
TiO3
0.4 0.6
X
30
40
50
60
70
V
(X)
Cu
Eau
Al2O3
TiO3
–80
–60
–40
–20
0
20
40
60
80
V
(X)
0.2 0.4 0.6 0.8 1.0
0.0
X
(a)
Water
Al2O3
Cu
TiO3
0.4
0.00 0.04 0.04 0.06 0.08
0.6
X
X
60
90
120
180
150
V
(X)
–210
–180
–120
–150
V
(X)
Cu
Eau
Al2
O3
TiO3
Cu
Eau
Al2
O3
TiO3
–300
–200
–100
0
100
200
V
(X)
0.2 0.4 0.6 0.8 1.0
0.0
X
(b)
Figure 8: Vertical component of the velocity along the horizontal median plane for pure water and different nanofluid. (a) Ra� 105
. (b) Ra� 106
.

small differences are observed. It is concluded that the V
(y) components of the velocity are not sensitive to the type
of nanofluid.
4.4. Average Nusselt Number. The results presented in this
part are obtained for Rayleigh numbers Ra between 103
and
106
, different volume fractions of the nanoparticles, and
length of the partition. Subsequently, we will focus on the
effect of these parameters on the average Nusselt number.
The variation of the average Nusselt number as a
function of the length of the heated sources for different
Rayleigh numbers is shown in Figure 9. It is noted that,
regardless of length and form of the heated sources, the
average Nusselt number increases with the increase of the
volume fraction. This increase is due to the improvement of
the thermal conductivity of the nanofluid. The effect of the
nanoparticles is greater at low Ra values, so that the 6%
increase in the volume fraction for a length of 0.5 leads to a
relative increase in the average Nusselt number of about 9%
for Ra � 106
and about 26% for Ra � 103
. The figure also
shows that, for a given volume fraction, the average Nusselt
increases with the increase in Ra and the length of the
block.
Figure 10 shows the variation of the average Nusselt
number for different values of Ra, as a function of the
volume fraction of the Al2O3, Cu, and TiO2. We note that
Nuav increases with Ra and the volume fraction of the
nanoparticles. This is due to the improvement of the knf,
when the volume fraction of the nanoparticles increases.
We observe, in a general way, that the intensification of
the thermal thrust forces, through the increase of the
number of Rayleigh, favors the thermal exchanges within
the cavity and this is because of the increase of the speed
Ra = 103
Ra = 104
Ra = 105
Ra = 106
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(a)
Ra = 103
Ra = 104
Ra = 105
Ra = 106
2
3
4
5
6
7
8
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(b)
Ra = 103
Ra = 104
Ra = 105
Ra = 106
2
3
4
5
6
7
8
9
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(c)
Ra = 103
Ra = 104
Ra = 105
Ra = 106
2
3
4
5
6
7
8
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(d)
Figure 9: The variation of the average Nusselt number as a function of the Rayleigh numbers, and ϕ for water-Al2O3. (a) d � 0.25. (b) d � 0.5.
(c) d � 0.75. (d) d � 0.1.

(Figure 7) following the intensification of convection
currents. This figure also shows the effect of the nano-
particles type on the variation of the average Nusselt
number. This heat transfer rate is maximum in the case of
water-Cu. A large number of Nusselt, that is to say, a
stronger exchange, is favored by a higher conductivity to
evacuate heat, a lower heat capacity to reduce storage, and
a higher density to promote convection (force Archi-
medes). These parameters play in favor of copper in
comparison with Al2O3 and TiO2 nanoparticles. Indeed,
the latter nanoparticles have very close heat capacities and
densities (difference not exceeding 12%), but the thermal
conductivity of Al2O3 is greater than that of TiO2 by about
34.6%, according to the results of Glades [42]. As a result,
Al2O3 nanoparticles provide better heat transfer than
TiO2 nanoparticles.
5. Conclusion
The study presented in this article deals with the natural
laminar convection of nanofluids within a three-dimensional
cavity containing two heating portions located on the left wall.
In the first part of this paper, we analyzed the effect of various
parameters on heat transfer, namely, the volume fraction, the
Rayleigh number, and the height of the heat source. This
allowed us to highlight the effect generated by each parameter
and to identify the optimal situations favoring heat transfer.
In the second part, we made a comparison between three
Al2O3
TiO3
Cu
1.9
2.0
2.1
2.2
2.3
2.4
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(a)
Al2O3
TiO3
Cu
3.4
3.5
3.6
3.7
3.8
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(b)
Al2O3
TiO3
Cu
6.2
6.4
6.6
6.8
7.0
Nu
moy
0.02 0.04 0.06 0.08 0.10
0.00
ϕ
(c)
Figure 10: Variation of the average Nusselt number as a function of the volume fraction and nanofluid type for different Ra. (a) Ra � 104
. (b)
Ra � 105
. (c) Ra � 106
.

types of nanoparticles (copper (Cu), aluminum oxide (Al2O3),
and titanium oxide (TiO2)).
The main results can be summarized as follows:
(i) The effect of the nanofluid on natural convection is
particularly apparent at high Rayleigh numbers.
(ii) The increase in the volume fraction causes the in-
tensification of the flow and an increase of the heat
exchange.
(iii) The heat transfer in the presence of copper nano-
particles is greater than that of Al2O3 and TiO2
nanoparticles; therefore, the type of nanoparticles is
a main factor in improving heat transfer.
(iv) The geometry parameters (dimensionless length
and width of the heat source) are used to improve
heat transfer. As the length of the heat source in-
creases, the average Nusselt number increases.
(v) As the Rayleigh number increases, the buoyancy
force becomes important and therefore the maxi-
mum flow function of the pure fluid and the
nanofluid increases. The flow in the presence of
nanofluid is more intense than that of the pure fluid
for Ra greater than 104
.
(vi) From all the previous investigations, we can say that
the case which improves the thermal and dynamic
transfers is the one where the length of the partition
is d � 1. We get the best heat transfer for this case, so
we can say that it is the optimal choice.
This configuration is the optimal choice to obtain the
best heat transfer, because the thermal and dynamic
transfers are influenced by the heat exchange surface and the
value of the buoyancy force which is favourable in the case of
the length being d � 1.
Nomenclature
d: Dimensionless heat source length
E: Dimensionless heat source height
e: Dimensionless heat source width
C P: Specific heat (J·kg−1
·K−1
)
g: Gravitational acceleration (m·s−2
)
k: Thermal conductivity (W·m−1
·K−1
)
Nuav: Local Nusselt number along the heat source
Ra: Rayleigh number
Pr: Prandtl number
H: Height of the cavity
B: Length of the cavity
B: Width of the cavity
u, v, w: Dimensional velocities (m/s)
U, V, W: Dimensionless velocities
P: Dimensionless pressure
x, y, z: Dimensional coordinates (m)
X, Y, Z: Dimensionless coordinates
Subscripts
np: Nanoparticle
f: Fluid (pure water)
nf: Nanofluid
Greek symbols
α: Thermal diffusivity (m2
·S−1
)
β: Thermal expansion coefficient (K−1
)
υ: Kinematic viscosity (m2
/s)
ρ: Density (kg/m3
)
k: Thermal conductivities (W·m1
·K1
)
ϕ: Solid volume fraction
Ɵ: Dimensionless temperature
μ: Dynamic viscosity (kg·m−1
·s−1
)
H: Hot
C: Cold.
Data Availability
The data are available upon request to the corresponding
author.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
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