The document describes a study that analyzed the performance of a pseudo-single row radiator operated with different nanofluids. A pseudo-single row radiator design was created using 3D modeling software to address limitations in standard multi-row radiator designs by increasing surface area and heat conduction. Computational fluid dynamics was used to analyze the radiator's performance with four nanofluids - aluminum oxide, silicon dioxide, copper oxide, and ethylene glycol - at three volume fractions. The results showed that copper oxide nanofluid provided the best performance compared to the other nanofluids.
2. Performance Study of a Pseudo Single Row Radiator Operated with Different Nanofluids
http://www.iaeme.com/IJMET/index.asp 305 editor@iaeme.com
Cite this Article: Ahmed Mohmad Aliywy, Ahmed Shany Khusheef, Ashham m and
Raad Farhood Chisab, Performance Study of a Pseudo Single Row Radiator Operated
with Different Nanofluids, International Journal of Mechanical Engineering and
Technology, 10(5), 2019, pp. 304-313.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=5
1. INTRODUCTION
In modern automotive industries, the demand for high-efficiency engines has been increased.
Those engines are not only evaluated based on their performances but also on their fuel
economy and on their emissions [2]. Those requirements are strongly dependent on the engine's
heat transfer system [3]. There is also a need for reducing the weight and dimensions of the
radiator that represents the heat transfer system. There are different types of radiator's tube
supplements that use various fin types and micro-channels show the amount of the efforts that
has been made to increase the cooling rate of radiators [2, 4]. Recently, some researchers have
found another way to increase the radiator's performance by changing the radiator's working
fluid. The typical heat transfer fluids (e.g. water and engine oil) have relatively poor heat
performances [5]. Therefore, there is a requirement to attain the required heat transfer [6]. One
way to improve the "heat transfer performances of radiator's fluids" is by adding solid particles,
which are metallic or metallic oxide and they are very small in the range of Nano and always
having cylindrical or spherical shapes, to the base fluid [7]. Nanoparticles change the thermal
transport properties and characteristics of base fluids because the Nanoparticles' heat
conductivity is higher than ordinary fluids [8]. These types of fluids are called Nanofluids.
Many researchers have investigated of using Nanofluids [5]. For instance, Zeinali Heris et
al. [9] investigated the use of Al2O3-water and CuO-water Nanofluids' performance
characteristics and the pressure drop under constant heat flux. The mentioned study confirmed
that in the triangular duct the Al2O3-water Nanofluid within volume fractions of 1.5% and 2%
is not helpful. It also approved that adding the same Nanoparticles' volume fraction of the CuO
is less beneficial than Al2O3 Nanoparticles based on the performance index. Another study was
performed by Hojjat et al. [10] who investigated experimentally the non-Newtonian
Nanofluids' frictional pressure drop through a horizontal circular tube. The authors stated that
"dimensionless pressure drop for Nanofluids" in both the turbulent and the laminar flow rules
as a function of the "Reynolds number" follows the same trend of the "pressure drop" observed
with the base fluid. Sharma et al. [1] presented a new design of radiators that can be used in
place of the current design. The "multi row radiator tubes" were separated by spaces to provide
adequate ligament to connect the tubes with the head. The ligament's average size in the
commercial application was between (5 and 10) mm. However, these radiators involved some
limitations since there was no contact surface among the tube rows; therefore, the heat
transmission was delayed. Consequently, the heat transfer rate to the fins and through the tube
rows declined. Bhogare et al. [3] executed the experiments on automobile radiator to study the
effects of adding Al2O3 to the base fluid. The authors claimed that improving the engine's
thermal transmission leads to raise the performance of engine, drop the pollution emissions and
decrease the fuel consumption. The experimental results proved that Nusselt number and heat
transfer coefficient grew when Nanoparticle volume fraction, air Reynolds number and mass
flow rate of coolant flowing through radiator were increasing. Godley et al. [11] executed an
experimental study on an automobile radiator to find the thermal behavior of the single phase
flow. The author in that research tried to examine the thermal transfer characteristics of the
radiator using Nanofluid as coolant that consists of water and CuO. Then the thermal
performance of the radiator that was operated with Nanofluid was compared with the radiator
that used conventional coolants.
3. Ahmed Mohmad Aliywy, Ahmed Shany Khusheef, Ashham m and Raad Farhood Chisab
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The main objectives of this paper are to design a pseudo–single row radiator. Then, the
performance of the designed radiator is analyzed using different Nanofluids that include Al2O3,
SiO2, CuO, and Ethylene Glycol with three volume fraction values (0.2, 0.3 and 0.4). The
Computational Fluid Dynamics (CFD) in ANSYS is used to calculate the pressure drop, inlet
velocity, coefficient of heat transfer and heat transfer rate of Nanofluids. The paper firstly gives
the fundamental information about the designed radiator. Then, the design of the 2D and 3D
models of the pseudo single row radiator will be presented. The Nanofluid properties will be
then theoretically calculated and explained. This is followed by analyses and discussions of the
CFD analysis results; and finally, the conclusion of this work is demonstrated.
2. RADIATOR DESIGN
In this section, two types of radiators will be discussed. The first is the standard radiator that
comprises two header tanks that are located on top and bottom. Those tanks are linked by a
passage of flattened tubes that form the main part of heat exchange [1]. This type of system,
which is typically made of aluminum, copper or brass soldered the metallic headers, is usually
named a multi row radiator core (see Figure 1). One of the main drawbacks of this design is
that it has no contact surfaces between the tube rows and therefore the heat conduction rate
through the tube rows and to the fins is hindered [1].
Figure 1 Multi row radiator
In order to solve the above-mentioned problem, a new design will be considered and it is
called a Pseudo–single row radiator in which the space between the tubes is filled up with some
material that has "light weight and good heat conduction" [1]. The filled material will increase
the surface area of the heat radiation and the heat flow through the tubes and to the fins;
therefore, the structure would be more efficient. Figure 2 shows the Pseudo–single row radiator
design.
Figure 2 The “Pseudo-single row” radiator design
3. MODIFIED MODEL
Figures 3(A and B) show the 2D and 3D models of the pseudo single row radiator that was
designed.
4. Performance Study of a Pseudo Single Row Radiator Operated with Different Nanofluids
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Figure 3 The pseudo single row radiator A) 2D drawing (mm) and B) 3D Model
4. NANOFLUID PROPERTIES
The Nanofluid properties need to be precisely identified before studying the thermal transfer
performance. This is achieved by assuming that the nanoparticles are well dispersed in the fluid
and also their concentration is considered uniform throughout the tube. This assumption is a
useful tool in order to estimate the Nanofluids' physical properties [12]. The density of the
Nanofluid is determined by "the mixing theory"
𝜌 𝑛𝑓 = 𝜙 ∗ 𝜌𝑠 + (1 − 𝜙) 𝜌 𝑤 (1)
in which 𝜌 𝑛𝑓, 𝜌𝑠, 𝜌 𝑤, 𝜙 are the "density of Nanofluid" (kg/m3
), the density of solid material
(kg/m3
), the "density of base fluid" material (water) (kg/m3
), and the volume fraction,
respectively. The Nanofluids' specific heat capacity (Cpnf) will be found using the thermal
equilibrium model as following
Cpnf =
ϕ∗ρs∗Cps+(1−ϕ)ρw∗Cpw
𝜌 𝑛𝑓
(2)
where Cpw and Cpsthe specific heat of the base fluid material (water) (j/kg-k) and the
specific heat of solid material (j/kg-k), respectively. The Nanofluid's effective dynamic
viscosity will be also determined based on Einstein's equation [13] for a viscous fluid having a
dilute suspension (𝜑 ≤ 2%) of small, rigid, spherical particles [14].
µ 𝑛𝑓 = µ 𝑤 (1 + 2.5 𝜙) (3)
in which µ 𝑤 and µ 𝑛𝑓 are the viscosity of base fluid (water) (kg/m-s) and the viscosity of
Nanofluid (kg/m-s), respectively. In order to determine the Nanofluids' effective thermal
conductivity, Yu and Choi's [15] equation is used.
𝐾 𝑛𝑓 = [
𝐾𝑠+2𝐾 𝑤+2(𝐾𝑠−𝐾 𝑤)(1+𝛽)³∗𝜙
𝐾𝑠+2𝐾 𝑤−(𝐾𝑠−𝐾 𝑤)(1+𝛽)3∗𝜙
] 𝑘 𝑤 (4)
where 𝛽 is the Nano-layer thickness's ratio to the radius of the "original particle" and it was
set to at 0.1 as in [15]. Kw and Ks are the thermal conductivity of base fluid material (water)
(W/m-k) and the thermal conductivity of solid material (W/m-k), respectively. Note that the
"transport properties" are functions of temperature and therefore the properties were measured
by using "the mean fluid temperature between the inlet and outlet" as in [12]. Table 1 shows
(A) (B)
5. Ahmed Mohmad Aliywy, Ahmed Shany Khusheef, Ashham m and Raad Farhood Chisab
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the thermo physical properties of base fluid and Nanoparticles while Table 2 presents the
physical properties of Nanofluids used in this study.
Table 1 Thermo physical properties of base fluid and Nanoparticles
Material
Thermal
conductivity (W/m-
k)
Specific heat
(j/kg-k)
Density (kg/m3
) Viscosity (kg/m-s)
Water [16] 0.605 4195 997.1 0.001003
Al2O3[10] 31.922 873.336 3950 ----------
Silicon Dioxide [17] 1.300 680 2650 ----------
Copper Oxide [16] 400 385 8933 ----------
Ethylene Glycol 0.2580 837 1110 ----------
Table 2 The physical properties of Nanofluids
Fluid
Volume
Fraction 𝝓
Thermal conductivity
𝑲 𝒏𝒇 (W/m-k)
Specific heat
𝐂 𝐩𝐧𝐟 (j/kg-k)
Density 𝝆 𝒏𝒇
(kg/m3
)
Viscosity µ 𝒏𝒇
(kg/m-s)
Aluminum
Oxide
0.2 1.031229 2542.202 1587.68 0.001505
0.3 1.37354 2104.594 1882.97 0.001755
0.4 1.889237 1785.632 2178.26 0.002006
Silicon
Dioxide
0.2 0.705878 2791.838 1327.68 0.001505
0.3 0.762587 2323.278 1492.97 0.001755
0.4 0.824199 1948.127 1658.26 0.002006
Copper
Oxide
0.2 1.062062 1561.018 2584.28 0.001505
0.3 1.441362 1172.261 3377.87 0.001755
0.4 2.034516 931.4203 4171.46 0.002006
Ethylene
Glycol
0.2 0.529953 3463.912 1019.68 0.001505
0.3 0.495665 3110.377 1030.97 0.001755
0.4 0.463294 2764.501 1042.26 0.002006
5. HEAT TRANSFER COEFFICIENT
The techniques in [12] are used to determine heat transfer coefficient. According to Newton’s
law of cooling
𝑄 = ℎ𝐴𝛥𝑇 = ℎ𝐴(𝑇𝑏 − 𝑇 𝑤) (5)
where 𝑄, ℎ, 𝐴, 𝑇𝑏 and 𝑇 𝑤 are the "thermal energy" in joules, the heat transfer
coefficient (assumed independent of T) (W/(m2
K)), the peripheral area of radiator tubes(m2
),
the bulk temperature "assumed to be the average values of inlet and outlet temperature of the
fluid moving through the radiator", and the tube wall temperature (the mean value by two
surfaces of the thermocouples), respectively. The "heat transfer rate" can be determined as
follows:
𝑄 = 𝑚𝐶 𝑝 𝛥𝑇 = 𝑚𝐶 𝑝(𝑇𝑖𝑛 − 𝑇𝑜𝑢𝑡) (6)
in which m, 𝐶 𝑝, 𝑇𝑖𝑛 and 𝑇𝑜𝑢𝑡 are the mass flow rate (the product of density and volume flow
rate of fluid), the fluid specific heat capacity, inlet, and outlet temperatures, respectively.
Regarding the equality of Q in the above equations:
𝑁𝑢 =
ℎ𝑑ℎ𝑦
𝑘
=
𝑚𝐶 𝑝(𝑇𝑖𝑛−𝑇𝑜𝑢𝑡)
𝐴(𝑇 𝑏−𝑇 𝑤)
(7)
6. Performance Study of a Pseudo Single Row Radiator Operated with Different Nanofluids
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where Nu, 𝑘, ℎ 𝑒𝑥𝑝, and 𝑑ℎ𝑦 are the average Nusselt number for the entire radiator, the fluid
thermal conductivity and the hydraulic diameter of the tube, respectively. Note that all the
physical properties were determined at fluid bulk temperature.
6. COMPUTATIONAL FLUID DYNAMICS (CFD) ANALYSIS
As mentioned above, four types of Nanofluids were used at three volume fractions as shown in
Tables 1 and Table 2. In order to study the thermal performance of the radiator, the mass rate
flow was 2Kg/s and the inlet temperature was 353K. For each Nanofluid, experiments were
conducted for three volume fractions. As an example in this paper, Figures 4 to 7 show the
computational fluid dynamics (CFD) analysis of the radiator by using Al2O3 Nanofluid at
volume fraction 0.3. Table (4) demonstrates the CFD analysis results for all Nanofluids at three
volume fractions (0.2, 0.3 and 0.4). Figure 8 shows the plot of the pressure against Nanofluid
types at different volume fractions. As can be seen in Figure 8, the value of pressure increased
dramatically when CuO was used at volume fraction 0.3 in comparisons with other Nanofluids.
The lowest pressure was recorded when Ethylene Glycol was used at volume fraction 0.4.
In order to test the effect of Nanofluid types on the outlet velocity of the radiator, the figure
of radiator outlet velocity as a function of Nanofluid types is given in Figure 9. As can be seen,
the highest value was recorded within Ethylene Glycol at volume fraction 0.2 while the smallest
value was documented within CuO fluid at volume fraction 0.4. This might be because the
density of the Ethylene Glycol Nanofluid has the smallest value at 0.2 volume fraction while
the density of CuO Nanofluid has the greatest value (see Tables 1 and 2 for comparison). This
will affect of the movements of the fluids inside the radiator. Figure 10 presents the heat transfer
coefficient as a function of Nanofluids at different volume fractions. The highest value was
recorded when Al2O3 was used at volume fraction 0.2 while the smallest value was
documented when SiO2 was used at volume fraction 0.3.
The effect of Nano fluids types on the heat transfer rate of the radiator was also studied as
shown in Figure 11. As can be seen, adding CuO nanoparticles to the base fluid increased
radiator heat transfer rate in comparison with other nanoparticles. It may be because the CuO
Nanofluid has greatest thermal conductivity compared to other Nanofluid types (see Tables 1
and 2). It may be also because the CuO Nanofluid had the lowest values of outlet velocity;
therefor, the fluid had sufficient time for contacting with air so the heat transfer rate increased.
It should be confirmed that increasing the heat transfer rate for any cooling system will indicate
to better thermal performance of the cooling system. Overall, it can be said that CuO Nanofluid
showed the best performance and Al2O3 Nanofluid was the second best in comparison with
other Nanofluids.
Figure 4 static pressure
7. Ahmed Mohmad Aliywy, Ahmed Shany Khusheef, Ashham m and Raad Farhood Chisab
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Figure 5 static temperature
Figure 6 velocity magnitude
Figure 7 heat transfer coefficient
Table 4 CFD analysis results at volume fraction 0.2, 0.3 and 0.4, the highest and lowest values are shown
in red and blue fonts, respectively.
Name of
fluid
Volume
Fraction
Pressure
(Pa)
Inlet
Temperature
(k)
Velocity
(m/s)
Heat Transfer
Coefficient
(W/m2
-k)
Mass Flow
Rate (Kg/s)
Heat
Transfer
Rate (W)
Aluminum
Oxide
0.2 0.0195 353 0.003230 84.024 0.0033913 236.8281
0.3 0.0161 353 0.002640 67.100 0.0038900 300.345
0.4 0.0141 353 0.002288 77.900 0.0028200 192.3620
Silicon
Dioxide
0.2 0.0224 353 0.003700 43.0295 0.0033913 217.4531
0.3 0.0201 353 0.003292 36.824 0.0038900 179.9843
0.4 0.0183 353 0.002967 51.600 0.0028200 248.4290
0.2 0.0115 353 0.001900 78.3442 0.0034658 310.0293
8. Performance Study of a Pseudo Single Row Radiator Operated with Different Nanofluids
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Name of
fluid
Volume
Fraction
Pressure
(Pa)
Inlet
Temperature
(k)
Velocity
(m/s)
Heat Transfer
Coefficient
(W/m2
-k)
Mass Flow
Rate (Kg/s)
Heat
Transfer
Rate (W)
Copper
Oxide
0.3 0.0910 353 0.001770 66.800 0.0038900 551.2265
0.4 0.0294 353 0.001619 77.300 0.0028200 439.9370
Ethylene
Glycol
0.2 0.0290 353 0.004790 66.669 0.0034657 93.9687
0.3 0.0089 353 0.0044555 61.853 0.0038900 121.1960
0.4 0.0073 353 0.0041790 55.00 0.0028200 137.5460
Figure 8 Pressure vs. Nanofluids at different volume fractions
Figure 9 Velocity against Nanofluids at different volume fractions
Figure 10 Heat Transfer Coefficient vs. Nanofluids at different volume fractions
9. Ahmed Mohmad Aliywy, Ahmed Shany Khusheef, Ashham m and Raad Farhood Chisab
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Figure 11 Heat Transfer Rate against Nanofluids at different volume fractions
7. CONCLUSION
Computational Fluid Dynamics (CFD) analysis was done on the pseudo single row radiator for
four types of Nanofluids (Al2O3, SiO2, CuO and Ethylene Glycol) at three volume fractions
(0.2, 0.3 and 0.4). It can be concluded that
• The value of pressure is more when CuO was used at volume fraction 0.3 in comparisons
with other Nano fluids.
• The highest value of the radiator outlet velocity was recorded within Ethylene Glycol at
volume fraction 0.2.
• The highest values of the heat transfer coefficient was recorded when Al2O3 and CuO
were used.
• The heat transfer rate was more when CuO nanoparticles were added to the base fluid
in comparison with other nanoparticles.
• The high value of heat transfer rate for any cooling system indicated to better thermal
performance of the cooling system.
Overall, it can be said that CuO Nanofluid shows the best performance in comparison with
other Nanofluids.
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