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Efficacy of a novel liquid block working with a nanofluid containing graphene nanoplatelets decorated with silver nanoparticles compared with conventional CPU coolers
Applied Thermal Engineering 127 (2017) 1233–1245
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Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Research Paper
Efficacy of a novel liquid block working with a nanofluid containing graphene nanoplatelets decorated with silver nanoparticles compared with conventional CPU coolers Mehdi Bahiraei ⇑, Saeed Heshmatian Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran
h i g h l i g h t s A novel distributor liquid block is applied for CPU cooling compared to usual ones. A nanofluid containing graphene decorated with Ag nanoparticles is used as coolant. Novel liquid block has a superior efficacy and lower irreversibility than others. Possibility of hot spot formation on CPU surface is very low for this liquid block. The merit of using the nanofluid in the liquid blocks is greater than pure water.
a r t i c l e
i n f o
Article history: Received 12 July 2017 Revised 14 August 2017 Accepted 28 August 2017 Available online 31 August 2017 Keywords: Hybrid nanofluid Novel liquid block CPU cooling Graphene Entropy generation Variable properties
a b s t r a c t This research attempts to investigate the efficacy and entropy generation of a hybrid nanofluid containing graphene nanoplatelets decorated with silver nanoparticles in three different liquid blocks for CPU cooling. A novel distributor liquid block is evaluated compared with two conventional liquid blocks including serpentine and parallel geometries. Comparisons of the flow distribution uniformity, maximum CPU temperature, average CPU temperature, temperature uniformity on CPU surface, and pumping power consumption of liquid blocks with novel and conventional configurations are made. The results show that the novel distributor liquid block has a superior efficacy based on both thermal performance and irreversibility rates. In addition, the merit of using the nanofluid in the liquid blocks is greater than pure water, and this hybrid nanofluid shows a promising view for cooling improvement in electronics. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Cooling and thermal management of temperature-sensitive devices such as electronic processors are very important because the lifetime and energy efficiency of these devices are significantly affected by the temperature values. Cooling of Central Processor Units (CPUs) is still one of the important challenges in computer systems and microelectronics technology. At present, air-based heat sinks are the most utilized devices to cool electronic processors. So far, many research papers about airbased CPU coolers have been published, and substantial improvements for their development have been presented based on both numerical solutions and experimental studies [1,2]. Air cooling techniques, however, are not sufficient for cooling of high heat flux ⇑ Corresponding author. E-mail address: [email protected] (M. Bahiraei). http://dx.doi.org/10.1016/j.applthermaleng.2017.08.136 1359-4311/Ó 2017 Elsevier Ltd. All rights reserved.
electronic chips. Therefore, electronics cooling has witnessed a shift away from air cooling towards some alternative methods such as liquid cooling [3], two-phase cooling [4], phase change materials [5] and nanofluids [6]. Liquids generally possess a greater heat capacity and thermal conductivity than gases and consequently, can considerably enhance heat exchange rate, and reduce maximum temperature on a processor. Hence, liquid cooling has been recognized as an operative and practical technique to cool high heat flux electronic devices. After the innovative survey of Tuckerman and Pease [7], several academics have investigated electronics cooling with liquids including examinations on manifold heat sinks [3], traditional microchannel heat sinks [8], jet and spray cooling [9]. Due to the high potential of liquid cooling, it is becoming progressively popular. In this approach, a liquid is employed inside a convective system to absorb the generated heat from an electronic processor and dissipate it to the environment.
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These systems can be developed for various applications, spaces and configurations. Recently, nanofluids have been introduced as an excellent choice for the future of innovative heat transfer liquids. It has been proved that by adding the solid nanoparticles inside a conventional liquid, thermal conductivity of the base fluid is improved significantly [10–16] and consequently, the convective heat transfer is also augmented. Hence, some studies have been performed in which nanofluids have been employed as coolants in liquid blocks for electronics cooling. Sarafraz et al. [17] evaluated the thermal performance of a liquid block working with gallium, CuO-water nanofluid and water. The CPU used was assessed at three states of standby, normal and overload working modes. The CuO–water nanofluid presented a higher thermal performance in comparison with water, while had lower pressure drop offering a trade-off situation against the gallium. Sohel et al. [18] investigated the cooling performance of Al2O3-water nanofluid as an alternative for a conventional coolant. The nanofluid was passed through the copper minichannel heat sink which was attached with an electronic heat source. The experimental results showed that the nanofluid decreases heat sink temperature compared to the conventional coolant. Rafati et al. [19] investigated cooling of a computer microchip using silica, alumina and titania nanoparticles in a base fluid (mixture of water and ethylene glycol). The largest decrease in CPU temperature from 49.4 °C to 43.9 °C was obtained for 1% concentration of alumina. Many types of nanomaterials have been employed by researchers to synthetize different nanofluids. In addition to metals and metal oxide nanoparticles, carbon based materials such as Carbon Nanotubes (CNTs), graphene oxide and Graphene Nanoplatelets (GNPs) have also been utilized in nanofluids. New studies show that nanofluids containing graphene can present a greater thermal conductivity compared to other nanofluids. Indeed, graphene possesses a superior thermal conductivity, which introduces it as an excellent selection for use in nanofluids. Furthermore, producing graphene nanoparticles is comparatively simple and cost effective. Akhavan-Zanjani et al. [20] evaluated convective heat transfer of graphene–water nanofluid through a circular pipe with uniform wall heat flux. Results revealed that addition of low amounts (up to 0.02% volume fraction) of graphene nanoparticles to water considerably increases thermal conductivity and convective heat transfer coefficient. Khajeh Arzani et al. [21] synthesized graphene-water nanofluids in presence of covalent and noncovalent functionalization. Thermal conductivity, density, viscosity and specific heat capacity were investigated and employed as raw data for numerical simulation. The increase in GNP concentration resulted in an increase in thermal conductivity and friction factor. Vakili et al. [22] developed a model to predict viscosity of the nanofluid containing graphene nanoplatelets via neural network. The nanofluid viscosity augmented by increasing weight concentration in each measured temperature. Many studies about liquid cooling using heat sinks have been performed, however, these contributions paid inadequate attention to the maldistribution of a flow to several channels on a plate [23]. Heat sinks often have several parallel flow channels, such that these small channels provide a large heat exchange surface for heat transfer enhancement. However, the role of inlet and outlet manifolds is also vital because they distribute the flow between the channels. Indeed, a non-uniform distribution of the flow decreases liquid block efficiency since it causes high local temperatures, and intensifies pressure drop which consequently increases pumping power required for heat sink operation. In fact, high temperature gradients intensify thermal stresses in electronic chips and consequently, decrease reliability. Therefore, some researchers have attempted to recognize the flow maldistribution concerning conventional flow configurations such as Z-type and U-type manifold
connections to parallel channels. Ramos-Alvarado et al. [24] applied a new flow distributor designed with T-shaped symmetric divisions of flow between the channels for pure water as the coolant. This outline can provide a more uniform flow distribution than other designs. To develop the application of this novel idea, this pattern is employed in the present research for nanofluids aimed at energy efficiency improvement and uniform cooling in heat sinks. Several methods have also been suggested in the past to reach a uniform cooling on chips by applying innovative approaches including use of thermoelectric cooling [25] and electrowetting [26]. These techniques, however, are limited due to poor device performance and low heat flux pumping capability. However, nanofluids can be employed as a passive method for this purpose owing to their superior thermal conductivity. In the present contribution, the efficacy of a novel liquid block with T-shaped symmetric flow distribution configuration operated with a new nanofluid is evaluated and is compared with two other configurations including traditional serpentine and parallel channels. A hybrid nanofluid containing graphene nanoplatelets decorated with silver nanoparticles is applied. In fact, this study employs two approaches simultaneously to improve CPU reliability and energy efficiency, i.e. use of an efficient nanofluid along with applying a novel liquid block. To the best knowledge of the authors, no study has been performed on the performance of hybrid nanofluids containing graphene for electronics cooling.
2. Definition of the liquid blocks and nanofluid Fig. 1 illustrates the liquid blocks under study. The liquid blocks presented in Fig. 1a and b are conventional ones while that displayed in Fig. 1c is a novel liquid block with T-shaped distributors. The material for all liquid blocks is aluminum. The serpentine flow channel configuration is presented in Fig. 1a. This outline has only one continuous channel, and it is basically a conventional configuration. The parallel flow channel configuration is displayed in Fig. 1b, which has the manifold for better distribution of the flow between the parallel channels. Fig. 1c shows the novel flow field configuration in which parallel flow channels and flow distributors are noticed. In this pattern, the flow is distributed between the channels in four levels (i.e. four stages) before reaching main parallel channels. This leads to a uniform distribution of the flow between the channels, which can decrease pressure drop and improve uniformity of cooling for an electronic chip. In the current study, this configuration is called distributor liquid block. In addition, Fig. 1d displays outside of the liquid blocks, in which the locations of the inlet and outlet are illustrated. The geometric details of the liquid blocks are summarized in Table 1. For the distributor liquid block, as is seen in Fig. 1c, after entering from the inlet, flow arrives a preliminary channel and then divides at four levels before reaching 20 parallel channels. The length and width of the channels in each level are presented in Table 2 for this configuration. The investigation is carried out on a water based hybrid nanofluid containing a nanocomposite powder. This powder has been produced by decorating silver nanoparticles on graphene nanoplatelets. The method to synthetize this new nanofluid has been fully introduced in [27], and is summarized in Fig. 2. As can be found from this figure, there are two stages for synthesis of GNP–silver nanocomposite. In the first stage, functionalization of GNPs is carried out via acid treatment and then, functionalized GNPs are decorated with silver nanoparticles by the use of Ag (NH3)2OH solution. It is noteworthy that functional groups reduce the great theoretical surface area (2630 m2 g1) of graphene, which decreases its excellent attributes in practice. Hence, applying the
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Fig. 1. The liquid blocks under study: (a) serpentine, (b) parallel flow, (c) distributor, (d) outside of liquid blocks.
Table 1 Geometric details of the liquid blocks. Characteristic
Value
Width of liquid blocks Inlet diameter Outlet diameter Number of channels Width of each channel Length of each channel Thickness of bottom surface
53.89 mm 1.90 mm 1.90 mm 20 1.20 mm 29.23 mm 1 mm
Table 2 Dimensions of the channels for the distributor liquid block. Division level
Length (mm)
Width (mm)
Preliminary channel First level Second level Third level Fourth level Main parallel channels (20)
5.73 3.47 3.11 2.69 2.30 29.23
2.84 2.55 2.06 1.77 1.49 1.20
silver nanoparticles as a guest material in GNPs can be efficient and useful in improvement of thermal conductivity of the nanocomposite. The method for synthesis of the GNP–silver nanofluid is briefly described below. Graphene nanoplatelets are naturally hydrophobic and therefore, they cannot be suspended in polar liquids such as distilled
water. In order to disperse GNPs inside the water, they are functionalized by acid treatment and therefore, become hydrophilic. By this technique, functional groups such as carboxyl and hydroxyl are introduced on the surface of GNPs. Acid treatment procedure was performed by adding GNPs to a 1:3 ratio of HNO3 and H2SO4 solution under ultrasonication. In the next step, the functionalized GNPs were decorated with silver nanoparticles by means of a chemical reaction. For this purpose, Ag (NH3)2OH solution was combined with functionalized GNP solution at a weight ratio of 1:6. After synthesis of nanocomposite powder, the nanofluid was prepared by adding distilled water [27]. The TEM image of silver coated GNPs is shown in Fig. 3. According to this figure, the silver nanoparticles have been decorated on GNPs properly, which indicates an excellent acid treatment bringing about functional groups reduction. 3. Governing equations The equations of continuity, momentum and energy are solved by employing variable effective properties in order to evaluate the performance of the hybrid nanofluid in the liquid blocks. The nanofluid is assumed Newtonian and incompressible. The flow regime is laminar, and the gravity effect is neglected. Moreover, the viscous dissipation is considered to be negligible. Conservation of mass:
r ðqv Þ ¼ 0
ð1Þ
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Fig. 2. Schematic of molecular structure of GNP–Ag nanocomposite [27]. Reprinted with permission from Elsevier.
Fig. 3. TEM images of GNP–Ag nanocomposite; (a) low and (b) high magnifications [27]. Reprinted with permission from Elsevier.
Conservation of momentum:
r ðqvvÞ ¼ rP þ r ðlrv Þ
ð2Þ
Conservation of energy:
r ðqv cp TÞ ¼ r ðkrTÞ
ð3Þ
where q is density, k is thermal conductivity, l denotes viscosity, and cp represents specific heat. Moreover, v , T, and P represent velocity, temperature, and pressure, respectively.
where subscripts f, p and nf refer to the base fluid, particles and nanofluid, respectively. Moreover, u denotes the volume concentration. In addition, based on the experimental data measured by Yarmand et al. [27], the empirical temperature-dependent models are developed here to evaluate the thermal conductivity, viscosity, and density of this novel hybrid nanofluid. The models obtained are presented in Table 3 for the base fluid (u = 0) and the nanofluid (u = 0.1%). It should be noted that in these models, the temperature is in Celsius.
3.1. Nanofluid properties The following equation is used to calculate the specific heat of the nanofluid:
cp;nf ¼ ð1 uÞcp;f þ ucp;p
ð4Þ
3.2. Boundary conditions The liquid blocks under study are utilized for cooling of a CPU. They are placed on the CPU and therefore, heat flux is applied from
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Nanofluid (u = 0.1%)
q ¼ 0:2968T þ 1004
q ¼ 0:302T þ 1005:2
k ¼ 0:002T þ 0:5488
k ¼ 2:928 106 T 2:815 þ 0:6752
l ¼ 0:001 ð0:0003631T 0:04121T þ 1:777Þ
l ¼ 0:001 ð19:19T 1:13 þ 0:6251Þ
2
their bottom surface. Uniform profiles for temperature and velocity are employed at the inlet of the liquid blocks. Moreover, zero relative pressure is applied at the outlet of them. The no slip condition is used on the walls. Also, all surfaces of the liquid blocks are insulated except the bottom part under heat flux. 4. Entropy generation Second law analysis with calculation of entropy generation is a practical and suitable approach to investigate performance of thermal devices. In fact, entropy generation is a criterion to evaluate irreversibilities in a process. It means that a system with lower irreversibilities (i.e. lower entropy generation rates) is more optimal and has a better performance. Local entropy generation per unit volume originates from two causes; entropy generation due to heat transfer and entropy generation owing to friction:
_ 000 _ 000 S_ 000 g;t ¼ Sg;h þ Sg;f
ð5Þ
_ 000 _ 000 where S_ 000 g;t , Sg;h and Sg;f are total entropy generation rate, thermal entropy generation rate and frictional entropy generation rate, respectively.
S_ 000 g;h
" 2 2 # 2 k @T @T @T ¼ 2 þ þ @x @y @z T
S_ 000 g;f dV;
S_ g;h ¼
Z
S_ 000 g;h dV;
S_ g;t ¼
Serpentine Parallel Distributor
ð7Þ
Z
S_ 000 g;t dV
ð8Þ
5. Numerical method and validation The control volume method is applied for numerical simulation. The approach for pressure-velocity coupling is SIMPLE, and second-order upwind scheme is utilized for solving the mass, momentum, and energy equations. The convergence criterion for all variables is that the normalized residuals are less than 105. In order to verify the mesh independency, the different grids are examined with various meshes. With evaluating different grids, the optimal grids for the serpentine liquid block, parallel flow liquid block and distributor liquid block possess respectively 178,241, 432,973 and 571,033 cells. It is noteworthy that further simulations in this survey are carried out based on the mentioned grids. For validation of the numerical approach, the results obtained from the current study are compared with the data presented by Ramos-Alvarado et al. [24] for pure water at q00 = 1000 W/m2 and Re = 300. For this comparison, pressure drop and temperature rise
DTi-o (K)
DPi-o (Pa)
Present study
[24]
Present study
[24]
1.681 1.661 1.651
1.691 1.691 1.691
15,184 61.26 136.94
15,082 65 137
of the fluid (pure water) from inlet to outlet of three liquid blocks are considered. The results of the comparison are presented in Table 4 and as can be observed, the results show a proper agreement and consequently, the numerical method is valid. 6. Data reduction In order to evaluate the cooling performance of the liquid blocks quantitatively, the difference between the maximum and minimum temperatures of the CPU surface should be examined. The ratio of this temperature difference against the heat flux employed (q00 ) is used as a criterion for uniformity of the cooling:
ð6Þ
The global entropy generation rates (i.e., entropy generation rates in entire domain) are evaluated by the integration of the local entropy generation rates as below:
Z
Liquid block
h¼
( " 2 2 2 # 2 l @v x @v y @v z @v x @v y þ ¼ þ þ 2 þ S_ 000 g;f T @x @y @z @y @x 2 2 ) @v x @v z @v y @v z þ þ þ þ @z @x @z @y
S_ g;f ¼
Table 4 Results obtained from current study compared to those of Ramos-Alvarado et al. [24].
min T max CPU T CPU 00 q
ð9Þ
min where T max CPU and T CPU are respectively maximum temperature and minimum temperature of CPU surface. A smaller h shows a more uniform temperature distribution. To judge the merit of a liquid block, however, the temperature uniformity is not sufficient. A low average temperature for CPU surface also is vital and should be taken into account. Therefore, the following parameter is employed for this purpose:
R¼
T CPU T in q00
ð10Þ
where T CPU is average temperature of CPU surface, and T in represents fluid temperature in the inlet of liquid blocks. In reality, R in Eq. (10) is a thermal resistance. A better cooling in liquid blocks leads to a smaller average CPU temperature and so a lower thermal resistance R. The convective heat transfer coefficient for the liquid blocks is evaluated by Eq. (11) [17]:
h¼
q00 T CPU T m
ð11Þ
out where T m ¼ T in þT 2 where T out denotes fluid temperature in the outlet of liquid blocks.
To examine the performance of liquid blocks, pumping power required for fluid flow should also be evaluated. Pumping power _ is obtained by the following equation: (W)
_ ¼ V_ DP W
ð12Þ
where DP represents the pressure drop, and V_ denotes the volumetric flow rate.
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7. Results and discussion Numerical simulations are performed in order to investigate hydrothermal characteristics and entropy generation of the hybrid nanofluid containing GNPs decorated with silver nanoparticles in three liquid blocks for CPU cooling. The liquid blocks are placed on the CPU and so, heat flux is applied from the bottom surface. The analyses are conducted for both pure water and the nanofluid with concentration of 0.1%, and are carried out in terms of both Reynolds number and pumping power. At the first step, efficacy and hydrothermal characteristics of the liquid blocks are assessed and then, irreversibilities due to friction and heat transfer are examined based on the second law of thermodynamics. The movement path of the fluid in the serpentine liquid block is spiral and continuous. However, in the parallel flow and distributor liquid blocks, division of flow occurs and the pathlines are drawn for them in Fig. 4 in which fluid movement direction is from left to right. It is noteworthy that the color of the pathlines is based on flow velocity. It is clear from Table 3 that thermal conductivity, viscosity, and density are considered temperature dependent. In Fig. 5, the contours of temperature, thermal conductivity, viscosity, and density of the nanofluid in the distributor liquid block are shown for cross section passing through the center of the liquid block. It is observed that with flowing the nanofluid from left to right, due to the nanofluid temperature increment, thermal conductivity enhances, whereas viscosity and density decrease. Figs. 6 and 7 illustrate temperature contours for all three liquid blocks in cross section passing through their center at different Reynolds numbers for pure water and the nanofluid, respectively. It is clear that the change of temperature for serpentine liquid block is from right to left, and for two other liquid blocks, is from top to bottom. This is because the fluid flows through a continuous spiral path in the serpentine liquid block, whereas in two other geometries, flow is divided between channels and a parallel flow is established at different channels (see Fig. 1). As is seen in Figs. 6 and 7, with increasing Reynolds number, temperature of the walls decreases and temperature distribution uniformity is also improved. In addition, by comparing Figs. 6 and 7, it is found that for all three liquid blocks, the nanofluid has much better cooling than pure water at a constant Reynolds number. Moreover, use of the nanofluid leads to more uniform distribution of temperature. As previously mentioned, average temperature of CPU surface is a very important parameter in evaluating the performance of a liquid block. Fig. 8 depicts average temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water. As can be observed, with the addition of nanoparticles, the average temperature of CPU surface significantly
decreases, and the temperature reduction is more prominent at lower Reynolds numbers. The greatest decrease is 5.81 K, which occurs in the distributer liquid block for Re = 500. In addition, comparing different liquid blocks indicates that at a constant Reynolds number, the serpentine liquid block has the best cooling and the parallel flow liquid block has the worst cooling either for water or for the nanofluid. Moreover, it is noticed in the figure that the effect of changing the type of liquid block is more prominent at higher Reynolds numbers. In addition to average temperature of CPU surface, its maximum temperature is also of considerable importance. In fact, formation of hot spots can greatly reduce the reliability and performance that should be avoided. Fig. 9 presents maximum temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water. It is seen that maximum temperature decreases with increase in Reynolds number. Moreover, compared with water, the nanofluid decreases the maximum temperature of CPU surface, and the temperature reduction is more significant at lower Reynolds numbers, such that maximum temperature reduces 7.08 K in the distributor liquid block for Re = 500 in the case of using the nanofluid instead of water. By comparing three liquid blocks in Fig. 9, it is understood that the serpentine liquid block has a better performance and maximum temperature for this liquid block is lower than the two other ones. In addition, maximum temperature for the distributor liquid block is lower than that for the parallel liquid block. Fig. 10 shows the parameter h (based on Eq. (9)) for the three liquid blocks at different Reynolds numbers for the cases of using the nanofluid and water. As previously mentioned, lower value for h demonstrates more uniform temperature distribution. It is observed that using the nanofluid instead of water leads to significant reduction in h for all the liquid blocks. Therefore, the nanofluid improves uniformity of temperature distribution on the CPU surface significantly. In addition, by comparing three liquid blocks, it is found that the serpentine liquid block presents the lowest h, whereas the parallel liquid block has the highest h. Moreover, it is evident from Fig. 10 that the increase in Reynolds number leads to more uniform temperature distribution. A remarkable point about the serpentine liquid block is that at a constant Reynolds number, even using pure water in this liquid block causes more uniform temperature distribution compared to using the nanofluid in other liquid blocks. It was observed in Fig. 10 that at a given Reynolds number, the distributor liquid block offers more uniform temperature distribution compared with the parallel liquid block (because h value for it is smaller). This is due to the presence of flow divider channels in the distributor liquid block (Fig. 1c), which leads to more uniform distribution of the flow between the main channels. Fig. 11
Fig. 4. Pathlines for nanofluid flow at Re = 750 for: (a) distributor liquid block, (b) parallel liquid block.
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Fig. 5. Contours of (a) temperature, (b) thermal conductivity, (c) viscosity, and (d) density of nanofluid in the distributor liquid block at Re = 500.
Re=500
Re=750
Re=1000
Serpentine
Parallel
Distributor
Fig. 6. Temperature contours for three liquid blocks at different Reynolds numbers for case of using pure water.
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Re=500
Re=750
Re=1000
Serpentine
Parallel
Distributor
Fig. 7. Temperature contours for three liquid blocks at different Reynolds numbers for case of using nanofluid.
Fig. 8. Average temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water.
Fig. 9. Maximum temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water.
displays the nanofluid flow velocity in twenty channels for these two liquid blocks. It is obvious that for the parallel liquid block, flow has a higher velocity in the middle channels, which is the result of the entry of greater mass flow to them. For the distributor liquid block, however, flow has almost divided uniformly between the channels. Uniform division of flow between the channels in the distributor liquid block can cause more uniform cooling at CPU surface, which this fact is clearly seen in Fig. 12 in which temperature contours of CPU surface for these two liquid blocks are illustrated.
According to Fig. 12a, because of higher velocity in the middle channels of the parallel liquid block, a stronger cooling occurs in this region compared with marginal regions, which reduces the uniformity of temperature distribution in CPU surface for this liquid block. In the distributor liquid block (Fig. 12b), however, more uniform temperature distribution happens in CPU surface. In liquid blocks, in addition to thermal considerations, energy consumption must be taken into consideration as well. As the results showed, the serpentine liquid block offers better thermal
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Fig. 10. Parameter h for three liquid blocks at different Reynolds numbers for cases of using nanofluid and water.
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performance than two other liquid blocks. Nevertheless, the superiority of a liquid block should not be measured solely based on thermal performance, but the amount of energy consumption should also be considered. In Fig. 13, pumping power required to the fluid movement in different liquid blocks is depicted in terms of Reynolds number for the nanofluid and water. As can be seen, the serpentine liquid block has a much higher pumping power compared with two other liquid blocks. This is because in this liquid block, all mass flow passes through a continuous conduit and also the flow experiences successive path changes along its route. Therefore, pressure drop is very high in this liquid block, which leads to a considerable increase in pumping power. In the two other liquid blocks, however, mass flow is divided between different channels and hence, relatively low mass flow passes through each channel, and also the flow does not experience severe path changes throughout the route. As a result, pressure drop in the parallel and distributor liquid blocks is much lower than that in the serpentine liquid block, which reduces pumping power required to move the fluid. Thus, although the serpentine liquid block at a constant Reynolds number has a better thermal performance than two other liquid blocks, pumping power is much higher for it. For example, in the case of using the nanofluid at Re = 1000, pumping
Fig. 11. Nanofluid flow velocity in twenty channels at Re = 750 for: (a) parallel liquid block, (b) distributor liquid block.
Fig. 12. Temperature contours of CPU surface at Re = 750 for: (a) parallel liquid block, (b) distributor liquid block.
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Fig. 13. Pumping power in different liquid blocks in terms of Reynolds number for nanofluid and water.
power of the serpentine liquid block is about 138 times as much as pumping power of the parallel liquid block, and about 65 times as much as pumping power of the distributor liquid block that is a very important weakness for this liquid block. Furthermore, the distributor liquid block requires a higher pumping power than the parallel liquid block at a given Reynolds number that is due to the existence of more divider channels. Moreover, according to Fig. 13, the nanofluid intensifies pumping power compared to water that is due to the viscosity increment and increase in the
nanofluid flow velocity compared with water. The reason for higher velocity of the nanofluid compared with water is that analysis is carried out here at a constant Reynolds number and as the viscosity of nanofluid is higher than that of water, flow velocity should be increased to keep Reynolds number constant. The above results reveal that although the serpentine liquid block has a better thermal performance at a given Reynolds number, its energy consumption is much higher than the two other liquid blocks. Thus, for better comparison of the liquid blocks, their thermal performance is assessed at constant pumping powers (i.e. same energy consumption) in the following. The comparison between the serpentine and distributor liquid blocks based on average temperature of CPU surface, maximum temperature of CPU surface, h, thermal resistance, and convective heat transfer coefficient in case of using the nanofluid for different pumping powers at q00 = 41,500 W/m2 is presented in Table 5. Based on all the parameters presented in this table at a given pumping power, the distributor liquid block presents much better thermal performance than the serpentine liquid block. In addition to creating much lower maximum and average temperatures, the distributor liquid block improves uniformity of temperature distribution significantly, and the value of h for it is much lower than that for the serpentine liquid block. This can clearly be noticed in Fig. 14 that shows contour of CPU surface temperature for two liq_ = 0.02 W. uid blocks in case of using the nanofluid at W The above results show that at a given pumping power, the serpentine liquid block has a much weaker performance than the distributor liquid block. In the following, the distributor and parallel liquid blocks are compared at constant pumping powers. It is noteworthy that in the following figures, the results related to the pure water in the distributor liquid block are also presented because at a given pumping power, the nanofluid has a lower velocity than
Table 5 Comparison between serpentine and distributor liquid blocks in case of using nanofluid for different pumping powers at q00 = 41,500 W/m2. Parameter
TCPU (K) T max CPU ðKÞ h (m2 W1 K) R (m2 W1 K) h (W/m2 K)
Serpentine liquid block
Distributor liquid block
_ = 0.015 W W
_ = 0.02 W W
_ = 0.08 W W
_ = 0.015 W W
_ = 0.02 W W
_ = 0.08 W W
335.87 349.43 0.00068 0.00091 2701
331.43 343.89 0.00062 0.00080 3090
317.80 326.11 0.00040 0.00047 4911
309.76 314.94 0.00025 0.00028 6144
308.99 313.81 0.00023 0.00026 6603
306.73 310.53 0.00017 0.00021 7398
_ = 0.02 W in case of using nanofluid for: (a) distributor liquid block, (b) serpentine liquid block. Fig. 14. Contour of CPU surface temperature at W
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water and hence, its efficacy may be lower than water, which should be examined. Fig. 15 illustrates average temperature of CPU surface in terms of pumping power for two liquid blocks at q00 = 138,000 W/m2. As seen, the distributor liquid block decreases average temperature of CPU surface more. Moreover, the advantage of using the nanofluid compared with water at a constant pumping power is obvious in the figure. Fig. 16 presents maximum temperature of surface CPU in different pumping powers for two liquid blocks at q00 = 138,000 W/m2. According to the figure, the distributor liquid block reduces maximum temperature much more compared to the parallel liquid block. Superiority of this liquid block is more evident at higher pumping powers, such that maximum surface temperature in case
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_ = 0.15 W for the distributor liquid of using the nanofluid at W block is about 7.6 K lower than that for the parallel flow liquid block. Due to uniform division of the flow in the distributor liquid block, the possibility of formation of hot spots is very low, such that according to the figure, even for case of using water in this liquid block, maximum surface temperature has a lower value than case of using the nanofluid in the parallel liquid block. Furthermore, in the distributor liquid block, the nanofluid presents a lower maximum temperature compared with water, which shows higher merit of the nanofluid compared with water at a constant pumping power. Fig. 17 illustrates temperature contour at cross section passing through the center of the liquid blocks for the case of using the nanofluid in the parallel liquid block and states of using water _ = 0.1 W and the nanofluid in the distributor liquid block for W and q00 = 138,000 W/m2. It is obvious that the distributor liquid block creates both lower maximum temperature and more uniform cooling in comparison with the parallel flow liquid block. Thereby, according to the figure, even applying water in it (Fig. 17a) is preferable to use of the nanofluid in the parallel flow _ = 0.1 W, maximum temliquid block (Fig. 17c). For example for W
Fig. 15. Average temperature of CPU surface in terms of pumping power at q00 = 138,000 W/m2.
Fig. 16. Maximum temperature of CPU surface in terms of pumping power at q00 = 138,000 W/m2.
perature of CPU surface is 343.20 K for the parallel flow liquid block with the nanofluid flow, while it is 340.01 K and 337.81 K respectively for flow of water and the nanofluid in the distributor liquid block. Moreover, it is clear that at a given pumping power, the nanofluid has a better performance compared with water despite its lower velocity than water. In the analysis conducted based on Reynolds number, it was seen that the distributor liquid block has a better thermal performance than the parallel flow liquid block, whereas its pumping power was also higher according to Fig. 13 that made its superiority doubtful. However, in analysis performed based on pumping power above, it was found that this liquid block has a better performance than the parallel flow liquid block at constant pumping power (i.e. same energy consumption) as well. Although the main purpose in thermal devices is the amount of heat transfer, the entropy may be generated with a high rate, which can reduce second law efficiency. Analysis based on first law of thermodynamics is not sufficient to clarify the energy efficiency of thermal systems. Therefore, it is essential to perform evaluations based on second law of thermodynamics as well. Optimization of thermal equipment based on the second law ensures that the most efficient utilization of available energy is being achieved. In fact, lower entropy generation in a thermal device means lower energy dissipation. In the following, irreversibilities caused by friction and heat transfer are investigated through calculating entropy generation rates via the second law of thermodynamics. Table 6 presents thermal, frictional, and total entropy generation rates for the distributor and serpentine liquid blocks at different pumping powers. As observed, both thermal and frictional entropy generation rates for the distributor liquid block are lower than those for the serpentine liquid block and therefore, lower irreversibility occurs in this geometry. In fact, temperature distribution is more uniform and temperature gradient is smaller in the distributor liquid block and thus, lower thermal entropy is generated in it. On the other hand, in the serpentine liquid block, because all mass flow passes through only one channel and there is no flow division in it, velocity gradients are great and hence, higher frictional entropy is produced in this liquid block. Meanwhile, with increase in pumping power according to Table 6, thermal entropy generation reduces and frictional entropy generation increases, and due to the dominance of thermal term, total entropy generation decreases.
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_ = 0.1 W and q00 = 138,000 W/m2 for cases of using: (a) water in distributor liquid block, (b) nanofluid in distributor liquid block, (c) Fig. 17. Temperature contour at W nanofluid in parallel liquid block.
Table 6 Thermal, frictional, and total entropy generation rates for distributor and serpentine liquid blocks at different pumping powers. Parameter
S_ g;f ðW=KÞ S_ g;h ðW=KÞ S_ g;t ðW=KÞ
Serpentine liquid block
Distributor liquid block
_ = 0.015 W W
_ = 0.02 W W
_ = 0.08 W W
_ = 0.015 W W
_ = 0.02 W W
0.000026
0.000034
0.000123
0.000014
0.000019
0.000054
0.01707
0.01505
0.00971
0.00707
0.00685
0.00636
0.01710
0.01508
0.00983
0.00709
0.00687
0.00641
Fig. 18 shows frictional and total entropy generation rates for the parallel and distributor liquid blocks at different pumping powers. It is noticed that frictional entropy generation in the distributor liquid block is greater than that in the parallel liquid block that is due to the presence of more divider channels in it. However, it is found from Fig. 18b that total entropy generation in the distributor liquid block is lower than that in the parallel liquid block, which is owing to lower production of thermal entropy in it due to the smaller temperature gradient. Thus, despite the fact that in the distributor liquid block, greater frictional entropy generation occurs compared with the parallel liquid block, due to its lower
_ = 0.08 W W
thermal entropy generation, overall, irreversibility in this liquid block is lower. As a result, it is more appropriate geometry from the viewpoint of the second law of thermodynamics. Given that the distributor liquid block also had better thermal performance according to Figs. 14–17, this geometry has both better cooling and lower irreversibility compared to other liquid blocks. In addition, in the distributor liquid block, the nanofluid flow has higher frictional entropy generation compared with water flow according to Fig. 18a, which is caused by higher viscosity of the nanofluid. However, in this liquid block, total entropy generation for the nanofluid flow is smaller than that for the water flow according
Fig. 18. Entropy generation rates for parallel and distributor liquid blocks at different pumping powers: (a) frictional, (b) total.
M. Bahiraei, S. Heshmatian / Applied Thermal Engineering 127 (2017) 1233–1245
to Fig. 18b. This is because thermal conductivity of the nanofluid is greater than that of water and thus, temperature gradient for the nanofluid is smaller, which reduces thermal entropy generation and finally, decreases total entropy generation for the nanofluid. Therefore, the use of nanofluid instead of water causes lower irreversibility. Although the performance of a nanofluid containing graphene for CPU cooling was evaluated in the present study, much more studies are needed for better characterizing these nanofluids for use in electronics cooling. In fact, because of the unique characteristics of graphene, this material can be considered more in the future for cooling of electronic components. 8. Conclusion In this paper, the efficacy and entropy generation of a hybrid nanofluid containing graphene nanoplatelets decorated with silver nanoparticles are evaluated in a novel liquid block compared with two other conventional ones. The most important results achieved are as follows: – With increase in Reynolds number, average and maximum temperatures of CPU surface decrease and the uniformity of temperature distribution improves as well. – At a constant Reynolds number, the nanofluid has much better cooling than pure water, reduction of surface temperature due to adding the nanoparticles is more prominent at lower Reynolds numbers, and applying the nanofluid leads to more uniform distribution of temperature. – At a constant Reynolds number, the serpentine liquid block has the best cooling for either water or the nanofluid, such that maximum and average temperatures of CPU surface for it are lower than those of two other liquid blocks. Moreover, temperature distribution in this liquid block is more uniform. – Although the serpentine liquid block has better thermal performance than two other liquid blocks at a constant Reynolds number, pumping power for it is much higher. – At a constant pumping power, the distributor liquid block has a significant superiority over other liquid blocks, such that average and maximum temperatures of CPU for it are much lower. Moreover, the superiority of this liquid block becomes more prominent at higher pumping powers. – Due to the uniform division of the flow in the distributor liquid block, the possibility of formation of hot spots is very low, such that even in case of using water in this liquid block, maximum surface temperature has a lower value than case of applying the nanofluid in other liquid blocks. Surface cooling is much more uniform in it too. – At a constant pumping power, although velocity of the nanofluid is lower than that of water, the nanofluid has higher merit compared with water for use in the liquid blocks. – In addition to better thermal performance, the distributor liquid block has less irreversibility than other liquid blocks, and is more appropriate geometry from the viewpoint of the second law of thermodynamics as well. – The nanofluid causes lower total entropy generation in the liquid blocks compared with water.
References [1] P. Naphon, S. Klangchart, S. Wongwises, Numerical investigation on the heat transfer and flow in the mini-fin heat sink for CPU, Int. Commun. Heat Mass Transf. 36 (2009) 834–840.
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[2] E. Walsh, R. Grimes, Low profile fan and heat sink thermal management solution for portable applications, Int. J. Therm. Sci. 46 (2007) 1182–1190. [3] C.S. Sharma, M.K. Tiwari, S. Zimmermann, T. Brunschwiler, G. Schlottig, B. Michel, D. Poulikakos, Energy efficient hotspot-targeted embedded liquid cooling of electronics, Appl. Energy 138 (2015) 414–422. [4] J.B. Marcinichen, D. Wu, S. Paredes, J.R. Thome, B. Michel, Dynamic flow control and performance comparison of different concepts of two-phase on-chip cooling cycles, Appl. Energy 114 (2014) 179–191. [5] C. Kinkelin, S. Lips, U. Soupremanien, V. Remondiere, J. Dijon, H.L. Poche, E. Ollier, M. Zegaoui, N. Rolland, P.A. Rolland, Theoretical and experimental study of a thermal damper based on a CNT/PCM composite structure for transient electronic cooling, Energy Convers. Manage. 142 (2017) 257–271. [6] M. Bahiraei, S. Heshmatian, Application of a novel biological nanofluid in a liquid block heat sink for cooling of an electronic processor: thermal performance and irreversibility considerations, Energy Convers. Manage. 149 (2017) 155–167. [7] D.B. Tuckerman, R.F.W. Pease, High-performance heat sinking for VLSI, IEEE Electron Dev. Lett. 2 (1981) 126–129. [8] A. Ebrahimi, F. Rikhtegar, A. Sabaghan, E. Roohi, Heat transfer and entropy generation in a microchannel with longitudinal vortex generators using nanofluids, Energy 101 (2016) 190–201. [9] S.G. Kandlikar, A.V. Bapat, Evaluation of jet impingement, spray and microchannel chip cooling options for high heat flux removal, Heat Transf. Eng. 28 (2007) 911–923. [10] M. Bahiraei, Studying nanoparticle distribution in nanofluids considering the effective factors on particle migration and determination of phenomenological constants by Eulerian-Lagrangian simulation, Adv. Powder Technol. 26 (2015) 802–810. [11] O. Mahian, A. Kianifar, S. Zeinali Heris, S. Wongwises, Natural convection of silica nanofluids in square and triangular enclosures: theoretical and experimental study, Int. J. Heat Mass Transf. 99 (2016) 792–804. [12] M. Bahiraei, M. Hangi, Automatic cooling by means of thermomagnetic phenomenon of magnetic nanofluid in a toroidal loop, Appl. Therm. Eng. 107 (2016) 700–708. [13] A. Malvandi, D.D. Ganji, I. Pop, Laminar filmwise condensation of nanofluids over a vertical plate considering nanoparticles migration, Appl. Therm. Eng. 100 (2016) 979–986. [14] M. Bahiraei, R. Khosravi, S. Heshmatian, Assessment and optimization of hydrothermal characteristics for a non-Newtonian nanofluid flow within miniaturized concentric-tube heat exchanger considering designer’s viewpoint, Appl. Therm. Eng. 123 (2017) 266–276. [15] M. Bahiraei, Particle migration in nanofluids: a critical review, Int. J. Therm. Sci. 109 (2016) 90–113. [16] M. Sheikholeslami, T. Hayat, A. Alsaedi, S. Abelman, Numerical analysis of EHD nanofluid force convective heat transfer considering electric field dependent viscosity, Int. J. Heat Mass Transf. 108 (Part B) (2017) 2558–2565. [17] M.M. Sarafraz, A. Arya, F. Hormozi, V. Nikkhah, On the convective thermal performance of a CPU cooler working with liquid gallium and CuO/water nanofluid: a comparative study, Appl. Therm. Eng. 112 (2017) 1373–1381. [18] M.R. Sohel, R. Saidur, S.S. Khaleduzzaman, T.A. Ibrahim, Cooling performance investigation of electronics cooling system using Al2O3–H2O nanofluid, Int. Commun. Heat Mass Transf. 65 (2015) 89–93. [19] M. Rafati, A.A. Hamidi, M. Shariati Niaser, Application of nanofluids in computer cooling systems, heat transfer performance of nanofluids, Appl. Therm. Eng. 45–46 (2012) 9–14. [20] H. Akhavan-Zanjani, M. Saffar-Avval, M. Mansourkiaei, F. Sharif, M. Ahadi, Experimental investigation of laminar forced convective heat transfer of graphene–water nanofluid inside a circular tube, Int. J. Therm. Sci. 100 (2016) 316–323. [21] H. Khajeh Arzani, A. Amiri, S.N. Kazi, B.T. Chew, A. Badarudin, Int. Commun. Heat Mass Transf. 68 (2015) 267–275. [22] M. Vakili, S. Khosrojerdi, P. Aghajannezhad, M. Yahyaei, Int. Commun. Heat Mass Transf. 82 (2017) 40–48. [23] S.G. Kandlikar, Z. Lua, W.E. Domigana, A.D. Whitea, M.W. Benedicta, Measurement of flow maldistribution in parallel channels and its application to ex-situ and in-situ experiments in PEMFC water management studies, Int. J. Heat Mass Transf. 52 (2009) 1741–1752. [24] B. Ramos-Alvarado, P. Li, H. Liu, A. Hernandez-Guerrero, CFD study of liquidcooled heat sinks with microchannel flow field configurations for electronics, fuel cells, and concentrated solar cells, Appl. Therm. Eng. 31 (2011) 2494– 2507. [25] I. Chowdhury, R. Prasher, K. Lofgreen, G. Chrysler, S. Narasimhan, R. Mahajan, et al., On-chip cooling by superlattice-based thin-film thermoelectrics, Nat. Nano 4 (2009) 235–238. [26] P.Y. Paik, V.K. Pamula, K. Chakrabarty, Adaptive cooling of integrated circuits using digital microfluidics, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 16 (2008) 432–443. [27] H. Yarmand, S. Gharehkhani, G. Ahmadi, S.F. Seyed Shirazi, S. Baradaran, E. Montazer, M.N.M. Zubir, M.S. Alehashem, S.N. Kazi, M. Dahari, Graphene nanoplatelets–silver hybrid nanofluids for enhanced heat transfer, Energy Convers. Manage. 100 (2015) 419–428.
Contents lists available at ScienceDirect
Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Research Paper
Efficacy of a novel liquid block working with a nanofluid containing graphene nanoplatelets decorated with silver nanoparticles compared with conventional CPU coolers Mehdi Bahiraei ⇑, Saeed Heshmatian Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran
h i g h l i g h t s A novel distributor liquid block is applied for CPU cooling compared to usual ones. A nanofluid containing graphene decorated with Ag nanoparticles is used as coolant. Novel liquid block has a superior efficacy and lower irreversibility than others. Possibility of hot spot formation on CPU surface is very low for this liquid block. The merit of using the nanofluid in the liquid blocks is greater than pure water.
a r t i c l e
i n f o
Article history: Received 12 July 2017 Revised 14 August 2017 Accepted 28 August 2017 Available online 31 August 2017 Keywords: Hybrid nanofluid Novel liquid block CPU cooling Graphene Entropy generation Variable properties
a b s t r a c t This research attempts to investigate the efficacy and entropy generation of a hybrid nanofluid containing graphene nanoplatelets decorated with silver nanoparticles in three different liquid blocks for CPU cooling. A novel distributor liquid block is evaluated compared with two conventional liquid blocks including serpentine and parallel geometries. Comparisons of the flow distribution uniformity, maximum CPU temperature, average CPU temperature, temperature uniformity on CPU surface, and pumping power consumption of liquid blocks with novel and conventional configurations are made. The results show that the novel distributor liquid block has a superior efficacy based on both thermal performance and irreversibility rates. In addition, the merit of using the nanofluid in the liquid blocks is greater than pure water, and this hybrid nanofluid shows a promising view for cooling improvement in electronics. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Cooling and thermal management of temperature-sensitive devices such as electronic processors are very important because the lifetime and energy efficiency of these devices are significantly affected by the temperature values. Cooling of Central Processor Units (CPUs) is still one of the important challenges in computer systems and microelectronics technology. At present, air-based heat sinks are the most utilized devices to cool electronic processors. So far, many research papers about airbased CPU coolers have been published, and substantial improvements for their development have been presented based on both numerical solutions and experimental studies [1,2]. Air cooling techniques, however, are not sufficient for cooling of high heat flux ⇑ Corresponding author. E-mail address: [email protected] (M. Bahiraei). http://dx.doi.org/10.1016/j.applthermaleng.2017.08.136 1359-4311/Ó 2017 Elsevier Ltd. All rights reserved.
electronic chips. Therefore, electronics cooling has witnessed a shift away from air cooling towards some alternative methods such as liquid cooling [3], two-phase cooling [4], phase change materials [5] and nanofluids [6]. Liquids generally possess a greater heat capacity and thermal conductivity than gases and consequently, can considerably enhance heat exchange rate, and reduce maximum temperature on a processor. Hence, liquid cooling has been recognized as an operative and practical technique to cool high heat flux electronic devices. After the innovative survey of Tuckerman and Pease [7], several academics have investigated electronics cooling with liquids including examinations on manifold heat sinks [3], traditional microchannel heat sinks [8], jet and spray cooling [9]. Due to the high potential of liquid cooling, it is becoming progressively popular. In this approach, a liquid is employed inside a convective system to absorb the generated heat from an electronic processor and dissipate it to the environment.
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These systems can be developed for various applications, spaces and configurations. Recently, nanofluids have been introduced as an excellent choice for the future of innovative heat transfer liquids. It has been proved that by adding the solid nanoparticles inside a conventional liquid, thermal conductivity of the base fluid is improved significantly [10–16] and consequently, the convective heat transfer is also augmented. Hence, some studies have been performed in which nanofluids have been employed as coolants in liquid blocks for electronics cooling. Sarafraz et al. [17] evaluated the thermal performance of a liquid block working with gallium, CuO-water nanofluid and water. The CPU used was assessed at three states of standby, normal and overload working modes. The CuO–water nanofluid presented a higher thermal performance in comparison with water, while had lower pressure drop offering a trade-off situation against the gallium. Sohel et al. [18] investigated the cooling performance of Al2O3-water nanofluid as an alternative for a conventional coolant. The nanofluid was passed through the copper minichannel heat sink which was attached with an electronic heat source. The experimental results showed that the nanofluid decreases heat sink temperature compared to the conventional coolant. Rafati et al. [19] investigated cooling of a computer microchip using silica, alumina and titania nanoparticles in a base fluid (mixture of water and ethylene glycol). The largest decrease in CPU temperature from 49.4 °C to 43.9 °C was obtained for 1% concentration of alumina. Many types of nanomaterials have been employed by researchers to synthetize different nanofluids. In addition to metals and metal oxide nanoparticles, carbon based materials such as Carbon Nanotubes (CNTs), graphene oxide and Graphene Nanoplatelets (GNPs) have also been utilized in nanofluids. New studies show that nanofluids containing graphene can present a greater thermal conductivity compared to other nanofluids. Indeed, graphene possesses a superior thermal conductivity, which introduces it as an excellent selection for use in nanofluids. Furthermore, producing graphene nanoparticles is comparatively simple and cost effective. Akhavan-Zanjani et al. [20] evaluated convective heat transfer of graphene–water nanofluid through a circular pipe with uniform wall heat flux. Results revealed that addition of low amounts (up to 0.02% volume fraction) of graphene nanoparticles to water considerably increases thermal conductivity and convective heat transfer coefficient. Khajeh Arzani et al. [21] synthesized graphene-water nanofluids in presence of covalent and noncovalent functionalization. Thermal conductivity, density, viscosity and specific heat capacity were investigated and employed as raw data for numerical simulation. The increase in GNP concentration resulted in an increase in thermal conductivity and friction factor. Vakili et al. [22] developed a model to predict viscosity of the nanofluid containing graphene nanoplatelets via neural network. The nanofluid viscosity augmented by increasing weight concentration in each measured temperature. Many studies about liquid cooling using heat sinks have been performed, however, these contributions paid inadequate attention to the maldistribution of a flow to several channels on a plate [23]. Heat sinks often have several parallel flow channels, such that these small channels provide a large heat exchange surface for heat transfer enhancement. However, the role of inlet and outlet manifolds is also vital because they distribute the flow between the channels. Indeed, a non-uniform distribution of the flow decreases liquid block efficiency since it causes high local temperatures, and intensifies pressure drop which consequently increases pumping power required for heat sink operation. In fact, high temperature gradients intensify thermal stresses in electronic chips and consequently, decrease reliability. Therefore, some researchers have attempted to recognize the flow maldistribution concerning conventional flow configurations such as Z-type and U-type manifold
connections to parallel channels. Ramos-Alvarado et al. [24] applied a new flow distributor designed with T-shaped symmetric divisions of flow between the channels for pure water as the coolant. This outline can provide a more uniform flow distribution than other designs. To develop the application of this novel idea, this pattern is employed in the present research for nanofluids aimed at energy efficiency improvement and uniform cooling in heat sinks. Several methods have also been suggested in the past to reach a uniform cooling on chips by applying innovative approaches including use of thermoelectric cooling [25] and electrowetting [26]. These techniques, however, are limited due to poor device performance and low heat flux pumping capability. However, nanofluids can be employed as a passive method for this purpose owing to their superior thermal conductivity. In the present contribution, the efficacy of a novel liquid block with T-shaped symmetric flow distribution configuration operated with a new nanofluid is evaluated and is compared with two other configurations including traditional serpentine and parallel channels. A hybrid nanofluid containing graphene nanoplatelets decorated with silver nanoparticles is applied. In fact, this study employs two approaches simultaneously to improve CPU reliability and energy efficiency, i.e. use of an efficient nanofluid along with applying a novel liquid block. To the best knowledge of the authors, no study has been performed on the performance of hybrid nanofluids containing graphene for electronics cooling.
2. Definition of the liquid blocks and nanofluid Fig. 1 illustrates the liquid blocks under study. The liquid blocks presented in Fig. 1a and b are conventional ones while that displayed in Fig. 1c is a novel liquid block with T-shaped distributors. The material for all liquid blocks is aluminum. The serpentine flow channel configuration is presented in Fig. 1a. This outline has only one continuous channel, and it is basically a conventional configuration. The parallel flow channel configuration is displayed in Fig. 1b, which has the manifold for better distribution of the flow between the parallel channels. Fig. 1c shows the novel flow field configuration in which parallel flow channels and flow distributors are noticed. In this pattern, the flow is distributed between the channels in four levels (i.e. four stages) before reaching main parallel channels. This leads to a uniform distribution of the flow between the channels, which can decrease pressure drop and improve uniformity of cooling for an electronic chip. In the current study, this configuration is called distributor liquid block. In addition, Fig. 1d displays outside of the liquid blocks, in which the locations of the inlet and outlet are illustrated. The geometric details of the liquid blocks are summarized in Table 1. For the distributor liquid block, as is seen in Fig. 1c, after entering from the inlet, flow arrives a preliminary channel and then divides at four levels before reaching 20 parallel channels. The length and width of the channels in each level are presented in Table 2 for this configuration. The investigation is carried out on a water based hybrid nanofluid containing a nanocomposite powder. This powder has been produced by decorating silver nanoparticles on graphene nanoplatelets. The method to synthetize this new nanofluid has been fully introduced in [27], and is summarized in Fig. 2. As can be found from this figure, there are two stages for synthesis of GNP–silver nanocomposite. In the first stage, functionalization of GNPs is carried out via acid treatment and then, functionalized GNPs are decorated with silver nanoparticles by the use of Ag (NH3)2OH solution. It is noteworthy that functional groups reduce the great theoretical surface area (2630 m2 g1) of graphene, which decreases its excellent attributes in practice. Hence, applying the
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Fig. 1. The liquid blocks under study: (a) serpentine, (b) parallel flow, (c) distributor, (d) outside of liquid blocks.
Table 1 Geometric details of the liquid blocks. Characteristic
Value
Width of liquid blocks Inlet diameter Outlet diameter Number of channels Width of each channel Length of each channel Thickness of bottom surface
53.89 mm 1.90 mm 1.90 mm 20 1.20 mm 29.23 mm 1 mm
Table 2 Dimensions of the channels for the distributor liquid block. Division level
Length (mm)
Width (mm)
Preliminary channel First level Second level Third level Fourth level Main parallel channels (20)
5.73 3.47 3.11 2.69 2.30 29.23
2.84 2.55 2.06 1.77 1.49 1.20
silver nanoparticles as a guest material in GNPs can be efficient and useful in improvement of thermal conductivity of the nanocomposite. The method for synthesis of the GNP–silver nanofluid is briefly described below. Graphene nanoplatelets are naturally hydrophobic and therefore, they cannot be suspended in polar liquids such as distilled
water. In order to disperse GNPs inside the water, they are functionalized by acid treatment and therefore, become hydrophilic. By this technique, functional groups such as carboxyl and hydroxyl are introduced on the surface of GNPs. Acid treatment procedure was performed by adding GNPs to a 1:3 ratio of HNO3 and H2SO4 solution under ultrasonication. In the next step, the functionalized GNPs were decorated with silver nanoparticles by means of a chemical reaction. For this purpose, Ag (NH3)2OH solution was combined with functionalized GNP solution at a weight ratio of 1:6. After synthesis of nanocomposite powder, the nanofluid was prepared by adding distilled water [27]. The TEM image of silver coated GNPs is shown in Fig. 3. According to this figure, the silver nanoparticles have been decorated on GNPs properly, which indicates an excellent acid treatment bringing about functional groups reduction. 3. Governing equations The equations of continuity, momentum and energy are solved by employing variable effective properties in order to evaluate the performance of the hybrid nanofluid in the liquid blocks. The nanofluid is assumed Newtonian and incompressible. The flow regime is laminar, and the gravity effect is neglected. Moreover, the viscous dissipation is considered to be negligible. Conservation of mass:
r ðqv Þ ¼ 0
ð1Þ
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Fig. 2. Schematic of molecular structure of GNP–Ag nanocomposite [27]. Reprinted with permission from Elsevier.
Fig. 3. TEM images of GNP–Ag nanocomposite; (a) low and (b) high magnifications [27]. Reprinted with permission from Elsevier.
Conservation of momentum:
r ðqvvÞ ¼ rP þ r ðlrv Þ
ð2Þ
Conservation of energy:
r ðqv cp TÞ ¼ r ðkrTÞ
ð3Þ
where q is density, k is thermal conductivity, l denotes viscosity, and cp represents specific heat. Moreover, v , T, and P represent velocity, temperature, and pressure, respectively.
where subscripts f, p and nf refer to the base fluid, particles and nanofluid, respectively. Moreover, u denotes the volume concentration. In addition, based on the experimental data measured by Yarmand et al. [27], the empirical temperature-dependent models are developed here to evaluate the thermal conductivity, viscosity, and density of this novel hybrid nanofluid. The models obtained are presented in Table 3 for the base fluid (u = 0) and the nanofluid (u = 0.1%). It should be noted that in these models, the temperature is in Celsius.
3.1. Nanofluid properties The following equation is used to calculate the specific heat of the nanofluid:
cp;nf ¼ ð1 uÞcp;f þ ucp;p
ð4Þ
3.2. Boundary conditions The liquid blocks under study are utilized for cooling of a CPU. They are placed on the CPU and therefore, heat flux is applied from
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Nanofluid (u = 0.1%)
q ¼ 0:2968T þ 1004
q ¼ 0:302T þ 1005:2
k ¼ 0:002T þ 0:5488
k ¼ 2:928 106 T 2:815 þ 0:6752
l ¼ 0:001 ð0:0003631T 0:04121T þ 1:777Þ
l ¼ 0:001 ð19:19T 1:13 þ 0:6251Þ
2
their bottom surface. Uniform profiles for temperature and velocity are employed at the inlet of the liquid blocks. Moreover, zero relative pressure is applied at the outlet of them. The no slip condition is used on the walls. Also, all surfaces of the liquid blocks are insulated except the bottom part under heat flux. 4. Entropy generation Second law analysis with calculation of entropy generation is a practical and suitable approach to investigate performance of thermal devices. In fact, entropy generation is a criterion to evaluate irreversibilities in a process. It means that a system with lower irreversibilities (i.e. lower entropy generation rates) is more optimal and has a better performance. Local entropy generation per unit volume originates from two causes; entropy generation due to heat transfer and entropy generation owing to friction:
_ 000 _ 000 S_ 000 g;t ¼ Sg;h þ Sg;f
ð5Þ
_ 000 _ 000 where S_ 000 g;t , Sg;h and Sg;f are total entropy generation rate, thermal entropy generation rate and frictional entropy generation rate, respectively.
S_ 000 g;h
" 2 2 # 2 k @T @T @T ¼ 2 þ þ @x @y @z T
S_ 000 g;f dV;
S_ g;h ¼
Z
S_ 000 g;h dV;
S_ g;t ¼
Serpentine Parallel Distributor
ð7Þ
Z
S_ 000 g;t dV
ð8Þ
5. Numerical method and validation The control volume method is applied for numerical simulation. The approach for pressure-velocity coupling is SIMPLE, and second-order upwind scheme is utilized for solving the mass, momentum, and energy equations. The convergence criterion for all variables is that the normalized residuals are less than 105. In order to verify the mesh independency, the different grids are examined with various meshes. With evaluating different grids, the optimal grids for the serpentine liquid block, parallel flow liquid block and distributor liquid block possess respectively 178,241, 432,973 and 571,033 cells. It is noteworthy that further simulations in this survey are carried out based on the mentioned grids. For validation of the numerical approach, the results obtained from the current study are compared with the data presented by Ramos-Alvarado et al. [24] for pure water at q00 = 1000 W/m2 and Re = 300. For this comparison, pressure drop and temperature rise
DTi-o (K)
DPi-o (Pa)
Present study
[24]
Present study
[24]
1.681 1.661 1.651
1.691 1.691 1.691
15,184 61.26 136.94
15,082 65 137
of the fluid (pure water) from inlet to outlet of three liquid blocks are considered. The results of the comparison are presented in Table 4 and as can be observed, the results show a proper agreement and consequently, the numerical method is valid. 6. Data reduction In order to evaluate the cooling performance of the liquid blocks quantitatively, the difference between the maximum and minimum temperatures of the CPU surface should be examined. The ratio of this temperature difference against the heat flux employed (q00 ) is used as a criterion for uniformity of the cooling:
ð6Þ
The global entropy generation rates (i.e., entropy generation rates in entire domain) are evaluated by the integration of the local entropy generation rates as below:
Z
Liquid block
h¼
( " 2 2 2 # 2 l @v x @v y @v z @v x @v y þ ¼ þ þ 2 þ S_ 000 g;f T @x @y @z @y @x 2 2 ) @v x @v z @v y @v z þ þ þ þ @z @x @z @y
S_ g;f ¼
Table 4 Results obtained from current study compared to those of Ramos-Alvarado et al. [24].
min T max CPU T CPU 00 q
ð9Þ
min where T max CPU and T CPU are respectively maximum temperature and minimum temperature of CPU surface. A smaller h shows a more uniform temperature distribution. To judge the merit of a liquid block, however, the temperature uniformity is not sufficient. A low average temperature for CPU surface also is vital and should be taken into account. Therefore, the following parameter is employed for this purpose:
R¼
T CPU T in q00
ð10Þ
where T CPU is average temperature of CPU surface, and T in represents fluid temperature in the inlet of liquid blocks. In reality, R in Eq. (10) is a thermal resistance. A better cooling in liquid blocks leads to a smaller average CPU temperature and so a lower thermal resistance R. The convective heat transfer coefficient for the liquid blocks is evaluated by Eq. (11) [17]:
h¼
q00 T CPU T m
ð11Þ
out where T m ¼ T in þT 2 where T out denotes fluid temperature in the outlet of liquid blocks.
To examine the performance of liquid blocks, pumping power required for fluid flow should also be evaluated. Pumping power _ is obtained by the following equation: (W)
_ ¼ V_ DP W
ð12Þ
where DP represents the pressure drop, and V_ denotes the volumetric flow rate.
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7. Results and discussion Numerical simulations are performed in order to investigate hydrothermal characteristics and entropy generation of the hybrid nanofluid containing GNPs decorated with silver nanoparticles in three liquid blocks for CPU cooling. The liquid blocks are placed on the CPU and so, heat flux is applied from the bottom surface. The analyses are conducted for both pure water and the nanofluid with concentration of 0.1%, and are carried out in terms of both Reynolds number and pumping power. At the first step, efficacy and hydrothermal characteristics of the liquid blocks are assessed and then, irreversibilities due to friction and heat transfer are examined based on the second law of thermodynamics. The movement path of the fluid in the serpentine liquid block is spiral and continuous. However, in the parallel flow and distributor liquid blocks, division of flow occurs and the pathlines are drawn for them in Fig. 4 in which fluid movement direction is from left to right. It is noteworthy that the color of the pathlines is based on flow velocity. It is clear from Table 3 that thermal conductivity, viscosity, and density are considered temperature dependent. In Fig. 5, the contours of temperature, thermal conductivity, viscosity, and density of the nanofluid in the distributor liquid block are shown for cross section passing through the center of the liquid block. It is observed that with flowing the nanofluid from left to right, due to the nanofluid temperature increment, thermal conductivity enhances, whereas viscosity and density decrease. Figs. 6 and 7 illustrate temperature contours for all three liquid blocks in cross section passing through their center at different Reynolds numbers for pure water and the nanofluid, respectively. It is clear that the change of temperature for serpentine liquid block is from right to left, and for two other liquid blocks, is from top to bottom. This is because the fluid flows through a continuous spiral path in the serpentine liquid block, whereas in two other geometries, flow is divided between channels and a parallel flow is established at different channels (see Fig. 1). As is seen in Figs. 6 and 7, with increasing Reynolds number, temperature of the walls decreases and temperature distribution uniformity is also improved. In addition, by comparing Figs. 6 and 7, it is found that for all three liquid blocks, the nanofluid has much better cooling than pure water at a constant Reynolds number. Moreover, use of the nanofluid leads to more uniform distribution of temperature. As previously mentioned, average temperature of CPU surface is a very important parameter in evaluating the performance of a liquid block. Fig. 8 depicts average temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water. As can be observed, with the addition of nanoparticles, the average temperature of CPU surface significantly
decreases, and the temperature reduction is more prominent at lower Reynolds numbers. The greatest decrease is 5.81 K, which occurs in the distributer liquid block for Re = 500. In addition, comparing different liquid blocks indicates that at a constant Reynolds number, the serpentine liquid block has the best cooling and the parallel flow liquid block has the worst cooling either for water or for the nanofluid. Moreover, it is noticed in the figure that the effect of changing the type of liquid block is more prominent at higher Reynolds numbers. In addition to average temperature of CPU surface, its maximum temperature is also of considerable importance. In fact, formation of hot spots can greatly reduce the reliability and performance that should be avoided. Fig. 9 presents maximum temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water. It is seen that maximum temperature decreases with increase in Reynolds number. Moreover, compared with water, the nanofluid decreases the maximum temperature of CPU surface, and the temperature reduction is more significant at lower Reynolds numbers, such that maximum temperature reduces 7.08 K in the distributor liquid block for Re = 500 in the case of using the nanofluid instead of water. By comparing three liquid blocks in Fig. 9, it is understood that the serpentine liquid block has a better performance and maximum temperature for this liquid block is lower than the two other ones. In addition, maximum temperature for the distributor liquid block is lower than that for the parallel liquid block. Fig. 10 shows the parameter h (based on Eq. (9)) for the three liquid blocks at different Reynolds numbers for the cases of using the nanofluid and water. As previously mentioned, lower value for h demonstrates more uniform temperature distribution. It is observed that using the nanofluid instead of water leads to significant reduction in h for all the liquid blocks. Therefore, the nanofluid improves uniformity of temperature distribution on the CPU surface significantly. In addition, by comparing three liquid blocks, it is found that the serpentine liquid block presents the lowest h, whereas the parallel liquid block has the highest h. Moreover, it is evident from Fig. 10 that the increase in Reynolds number leads to more uniform temperature distribution. A remarkable point about the serpentine liquid block is that at a constant Reynolds number, even using pure water in this liquid block causes more uniform temperature distribution compared to using the nanofluid in other liquid blocks. It was observed in Fig. 10 that at a given Reynolds number, the distributor liquid block offers more uniform temperature distribution compared with the parallel liquid block (because h value for it is smaller). This is due to the presence of flow divider channels in the distributor liquid block (Fig. 1c), which leads to more uniform distribution of the flow between the main channels. Fig. 11
Fig. 4. Pathlines for nanofluid flow at Re = 750 for: (a) distributor liquid block, (b) parallel liquid block.
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Fig. 5. Contours of (a) temperature, (b) thermal conductivity, (c) viscosity, and (d) density of nanofluid in the distributor liquid block at Re = 500.
Re=500
Re=750
Re=1000
Serpentine
Parallel
Distributor
Fig. 6. Temperature contours for three liquid blocks at different Reynolds numbers for case of using pure water.
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Re=500
Re=750
Re=1000
Serpentine
Parallel
Distributor
Fig. 7. Temperature contours for three liquid blocks at different Reynolds numbers for case of using nanofluid.
Fig. 8. Average temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water.
Fig. 9. Maximum temperature of CPU surface in terms of Reynolds number in different liquid blocks for the nanofluid and water.
displays the nanofluid flow velocity in twenty channels for these two liquid blocks. It is obvious that for the parallel liquid block, flow has a higher velocity in the middle channels, which is the result of the entry of greater mass flow to them. For the distributor liquid block, however, flow has almost divided uniformly between the channels. Uniform division of flow between the channels in the distributor liquid block can cause more uniform cooling at CPU surface, which this fact is clearly seen in Fig. 12 in which temperature contours of CPU surface for these two liquid blocks are illustrated.
According to Fig. 12a, because of higher velocity in the middle channels of the parallel liquid block, a stronger cooling occurs in this region compared with marginal regions, which reduces the uniformity of temperature distribution in CPU surface for this liquid block. In the distributor liquid block (Fig. 12b), however, more uniform temperature distribution happens in CPU surface. In liquid blocks, in addition to thermal considerations, energy consumption must be taken into consideration as well. As the results showed, the serpentine liquid block offers better thermal
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Fig. 10. Parameter h for three liquid blocks at different Reynolds numbers for cases of using nanofluid and water.
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performance than two other liquid blocks. Nevertheless, the superiority of a liquid block should not be measured solely based on thermal performance, but the amount of energy consumption should also be considered. In Fig. 13, pumping power required to the fluid movement in different liquid blocks is depicted in terms of Reynolds number for the nanofluid and water. As can be seen, the serpentine liquid block has a much higher pumping power compared with two other liquid blocks. This is because in this liquid block, all mass flow passes through a continuous conduit and also the flow experiences successive path changes along its route. Therefore, pressure drop is very high in this liquid block, which leads to a considerable increase in pumping power. In the two other liquid blocks, however, mass flow is divided between different channels and hence, relatively low mass flow passes through each channel, and also the flow does not experience severe path changes throughout the route. As a result, pressure drop in the parallel and distributor liquid blocks is much lower than that in the serpentine liquid block, which reduces pumping power required to move the fluid. Thus, although the serpentine liquid block at a constant Reynolds number has a better thermal performance than two other liquid blocks, pumping power is much higher for it. For example, in the case of using the nanofluid at Re = 1000, pumping
Fig. 11. Nanofluid flow velocity in twenty channels at Re = 750 for: (a) parallel liquid block, (b) distributor liquid block.
Fig. 12. Temperature contours of CPU surface at Re = 750 for: (a) parallel liquid block, (b) distributor liquid block.
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Fig. 13. Pumping power in different liquid blocks in terms of Reynolds number for nanofluid and water.
power of the serpentine liquid block is about 138 times as much as pumping power of the parallel liquid block, and about 65 times as much as pumping power of the distributor liquid block that is a very important weakness for this liquid block. Furthermore, the distributor liquid block requires a higher pumping power than the parallel liquid block at a given Reynolds number that is due to the existence of more divider channels. Moreover, according to Fig. 13, the nanofluid intensifies pumping power compared to water that is due to the viscosity increment and increase in the
nanofluid flow velocity compared with water. The reason for higher velocity of the nanofluid compared with water is that analysis is carried out here at a constant Reynolds number and as the viscosity of nanofluid is higher than that of water, flow velocity should be increased to keep Reynolds number constant. The above results reveal that although the serpentine liquid block has a better thermal performance at a given Reynolds number, its energy consumption is much higher than the two other liquid blocks. Thus, for better comparison of the liquid blocks, their thermal performance is assessed at constant pumping powers (i.e. same energy consumption) in the following. The comparison between the serpentine and distributor liquid blocks based on average temperature of CPU surface, maximum temperature of CPU surface, h, thermal resistance, and convective heat transfer coefficient in case of using the nanofluid for different pumping powers at q00 = 41,500 W/m2 is presented in Table 5. Based on all the parameters presented in this table at a given pumping power, the distributor liquid block presents much better thermal performance than the serpentine liquid block. In addition to creating much lower maximum and average temperatures, the distributor liquid block improves uniformity of temperature distribution significantly, and the value of h for it is much lower than that for the serpentine liquid block. This can clearly be noticed in Fig. 14 that shows contour of CPU surface temperature for two liq_ = 0.02 W. uid blocks in case of using the nanofluid at W The above results show that at a given pumping power, the serpentine liquid block has a much weaker performance than the distributor liquid block. In the following, the distributor and parallel liquid blocks are compared at constant pumping powers. It is noteworthy that in the following figures, the results related to the pure water in the distributor liquid block are also presented because at a given pumping power, the nanofluid has a lower velocity than
Table 5 Comparison between serpentine and distributor liquid blocks in case of using nanofluid for different pumping powers at q00 = 41,500 W/m2. Parameter
TCPU (K) T max CPU ðKÞ h (m2 W1 K) R (m2 W1 K) h (W/m2 K)
Serpentine liquid block
Distributor liquid block
_ = 0.015 W W
_ = 0.02 W W
_ = 0.08 W W
_ = 0.015 W W
_ = 0.02 W W
_ = 0.08 W W
335.87 349.43 0.00068 0.00091 2701
331.43 343.89 0.00062 0.00080 3090
317.80 326.11 0.00040 0.00047 4911
309.76 314.94 0.00025 0.00028 6144
308.99 313.81 0.00023 0.00026 6603
306.73 310.53 0.00017 0.00021 7398
_ = 0.02 W in case of using nanofluid for: (a) distributor liquid block, (b) serpentine liquid block. Fig. 14. Contour of CPU surface temperature at W
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water and hence, its efficacy may be lower than water, which should be examined. Fig. 15 illustrates average temperature of CPU surface in terms of pumping power for two liquid blocks at q00 = 138,000 W/m2. As seen, the distributor liquid block decreases average temperature of CPU surface more. Moreover, the advantage of using the nanofluid compared with water at a constant pumping power is obvious in the figure. Fig. 16 presents maximum temperature of surface CPU in different pumping powers for two liquid blocks at q00 = 138,000 W/m2. According to the figure, the distributor liquid block reduces maximum temperature much more compared to the parallel liquid block. Superiority of this liquid block is more evident at higher pumping powers, such that maximum surface temperature in case
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_ = 0.15 W for the distributor liquid of using the nanofluid at W block is about 7.6 K lower than that for the parallel flow liquid block. Due to uniform division of the flow in the distributor liquid block, the possibility of formation of hot spots is very low, such that according to the figure, even for case of using water in this liquid block, maximum surface temperature has a lower value than case of using the nanofluid in the parallel liquid block. Furthermore, in the distributor liquid block, the nanofluid presents a lower maximum temperature compared with water, which shows higher merit of the nanofluid compared with water at a constant pumping power. Fig. 17 illustrates temperature contour at cross section passing through the center of the liquid blocks for the case of using the nanofluid in the parallel liquid block and states of using water _ = 0.1 W and the nanofluid in the distributor liquid block for W and q00 = 138,000 W/m2. It is obvious that the distributor liquid block creates both lower maximum temperature and more uniform cooling in comparison with the parallel flow liquid block. Thereby, according to the figure, even applying water in it (Fig. 17a) is preferable to use of the nanofluid in the parallel flow _ = 0.1 W, maximum temliquid block (Fig. 17c). For example for W
Fig. 15. Average temperature of CPU surface in terms of pumping power at q00 = 138,000 W/m2.
Fig. 16. Maximum temperature of CPU surface in terms of pumping power at q00 = 138,000 W/m2.
perature of CPU surface is 343.20 K for the parallel flow liquid block with the nanofluid flow, while it is 340.01 K and 337.81 K respectively for flow of water and the nanofluid in the distributor liquid block. Moreover, it is clear that at a given pumping power, the nanofluid has a better performance compared with water despite its lower velocity than water. In the analysis conducted based on Reynolds number, it was seen that the distributor liquid block has a better thermal performance than the parallel flow liquid block, whereas its pumping power was also higher according to Fig. 13 that made its superiority doubtful. However, in analysis performed based on pumping power above, it was found that this liquid block has a better performance than the parallel flow liquid block at constant pumping power (i.e. same energy consumption) as well. Although the main purpose in thermal devices is the amount of heat transfer, the entropy may be generated with a high rate, which can reduce second law efficiency. Analysis based on first law of thermodynamics is not sufficient to clarify the energy efficiency of thermal systems. Therefore, it is essential to perform evaluations based on second law of thermodynamics as well. Optimization of thermal equipment based on the second law ensures that the most efficient utilization of available energy is being achieved. In fact, lower entropy generation in a thermal device means lower energy dissipation. In the following, irreversibilities caused by friction and heat transfer are investigated through calculating entropy generation rates via the second law of thermodynamics. Table 6 presents thermal, frictional, and total entropy generation rates for the distributor and serpentine liquid blocks at different pumping powers. As observed, both thermal and frictional entropy generation rates for the distributor liquid block are lower than those for the serpentine liquid block and therefore, lower irreversibility occurs in this geometry. In fact, temperature distribution is more uniform and temperature gradient is smaller in the distributor liquid block and thus, lower thermal entropy is generated in it. On the other hand, in the serpentine liquid block, because all mass flow passes through only one channel and there is no flow division in it, velocity gradients are great and hence, higher frictional entropy is produced in this liquid block. Meanwhile, with increase in pumping power according to Table 6, thermal entropy generation reduces and frictional entropy generation increases, and due to the dominance of thermal term, total entropy generation decreases.
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_ = 0.1 W and q00 = 138,000 W/m2 for cases of using: (a) water in distributor liquid block, (b) nanofluid in distributor liquid block, (c) Fig. 17. Temperature contour at W nanofluid in parallel liquid block.
Table 6 Thermal, frictional, and total entropy generation rates for distributor and serpentine liquid blocks at different pumping powers. Parameter
S_ g;f ðW=KÞ S_ g;h ðW=KÞ S_ g;t ðW=KÞ
Serpentine liquid block
Distributor liquid block
_ = 0.015 W W
_ = 0.02 W W
_ = 0.08 W W
_ = 0.015 W W
_ = 0.02 W W
0.000026
0.000034
0.000123
0.000014
0.000019
0.000054
0.01707
0.01505
0.00971
0.00707
0.00685
0.00636
0.01710
0.01508
0.00983
0.00709
0.00687
0.00641
Fig. 18 shows frictional and total entropy generation rates for the parallel and distributor liquid blocks at different pumping powers. It is noticed that frictional entropy generation in the distributor liquid block is greater than that in the parallel liquid block that is due to the presence of more divider channels in it. However, it is found from Fig. 18b that total entropy generation in the distributor liquid block is lower than that in the parallel liquid block, which is owing to lower production of thermal entropy in it due to the smaller temperature gradient. Thus, despite the fact that in the distributor liquid block, greater frictional entropy generation occurs compared with the parallel liquid block, due to its lower
_ = 0.08 W W
thermal entropy generation, overall, irreversibility in this liquid block is lower. As a result, it is more appropriate geometry from the viewpoint of the second law of thermodynamics. Given that the distributor liquid block also had better thermal performance according to Figs. 14–17, this geometry has both better cooling and lower irreversibility compared to other liquid blocks. In addition, in the distributor liquid block, the nanofluid flow has higher frictional entropy generation compared with water flow according to Fig. 18a, which is caused by higher viscosity of the nanofluid. However, in this liquid block, total entropy generation for the nanofluid flow is smaller than that for the water flow according
Fig. 18. Entropy generation rates for parallel and distributor liquid blocks at different pumping powers: (a) frictional, (b) total.
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to Fig. 18b. This is because thermal conductivity of the nanofluid is greater than that of water and thus, temperature gradient for the nanofluid is smaller, which reduces thermal entropy generation and finally, decreases total entropy generation for the nanofluid. Therefore, the use of nanofluid instead of water causes lower irreversibility. Although the performance of a nanofluid containing graphene for CPU cooling was evaluated in the present study, much more studies are needed for better characterizing these nanofluids for use in electronics cooling. In fact, because of the unique characteristics of graphene, this material can be considered more in the future for cooling of electronic components. 8. Conclusion In this paper, the efficacy and entropy generation of a hybrid nanofluid containing graphene nanoplatelets decorated with silver nanoparticles are evaluated in a novel liquid block compared with two other conventional ones. The most important results achieved are as follows: – With increase in Reynolds number, average and maximum temperatures of CPU surface decrease and the uniformity of temperature distribution improves as well. – At a constant Reynolds number, the nanofluid has much better cooling than pure water, reduction of surface temperature due to adding the nanoparticles is more prominent at lower Reynolds numbers, and applying the nanofluid leads to more uniform distribution of temperature. – At a constant Reynolds number, the serpentine liquid block has the best cooling for either water or the nanofluid, such that maximum and average temperatures of CPU surface for it are lower than those of two other liquid blocks. Moreover, temperature distribution in this liquid block is more uniform. – Although the serpentine liquid block has better thermal performance than two other liquid blocks at a constant Reynolds number, pumping power for it is much higher. – At a constant pumping power, the distributor liquid block has a significant superiority over other liquid blocks, such that average and maximum temperatures of CPU for it are much lower. Moreover, the superiority of this liquid block becomes more prominent at higher pumping powers. – Due to the uniform division of the flow in the distributor liquid block, the possibility of formation of hot spots is very low, such that even in case of using water in this liquid block, maximum surface temperature has a lower value than case of applying the nanofluid in other liquid blocks. Surface cooling is much more uniform in it too. – At a constant pumping power, although velocity of the nanofluid is lower than that of water, the nanofluid has higher merit compared with water for use in the liquid blocks. – In addition to better thermal performance, the distributor liquid block has less irreversibility than other liquid blocks, and is more appropriate geometry from the viewpoint of the second law of thermodynamics as well. – The nanofluid causes lower total entropy generation in the liquid blocks compared with water.
References [1] P. Naphon, S. Klangchart, S. Wongwises, Numerical investigation on the heat transfer and flow in the mini-fin heat sink for CPU, Int. Commun. Heat Mass Transf. 36 (2009) 834–840.
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[2] E. Walsh, R. Grimes, Low profile fan and heat sink thermal management solution for portable applications, Int. J. Therm. Sci. 46 (2007) 1182–1190. [3] C.S. Sharma, M.K. Tiwari, S. Zimmermann, T. Brunschwiler, G. Schlottig, B. Michel, D. Poulikakos, Energy efficient hotspot-targeted embedded liquid cooling of electronics, Appl. Energy 138 (2015) 414–422. [4] J.B. Marcinichen, D. Wu, S. Paredes, J.R. Thome, B. Michel, Dynamic flow control and performance comparison of different concepts of two-phase on-chip cooling cycles, Appl. Energy 114 (2014) 179–191. [5] C. Kinkelin, S. Lips, U. Soupremanien, V. Remondiere, J. Dijon, H.L. Poche, E. Ollier, M. Zegaoui, N. Rolland, P.A. Rolland, Theoretical and experimental study of a thermal damper based on a CNT/PCM composite structure for transient electronic cooling, Energy Convers. Manage. 142 (2017) 257–271. [6] M. Bahiraei, S. Heshmatian, Application of a novel biological nanofluid in a liquid block heat sink for cooling of an electronic processor: thermal performance and irreversibility considerations, Energy Convers. Manage. 149 (2017) 155–167. [7] D.B. Tuckerman, R.F.W. Pease, High-performance heat sinking for VLSI, IEEE Electron Dev. Lett. 2 (1981) 126–129. [8] A. Ebrahimi, F. Rikhtegar, A. Sabaghan, E. Roohi, Heat transfer and entropy generation in a microchannel with longitudinal vortex generators using nanofluids, Energy 101 (2016) 190–201. [9] S.G. Kandlikar, A.V. Bapat, Evaluation of jet impingement, spray and microchannel chip cooling options for high heat flux removal, Heat Transf. Eng. 28 (2007) 911–923. [10] M. Bahiraei, Studying nanoparticle distribution in nanofluids considering the effective factors on particle migration and determination of phenomenological constants by Eulerian-Lagrangian simulation, Adv. Powder Technol. 26 (2015) 802–810. [11] O. Mahian, A. Kianifar, S. Zeinali Heris, S. Wongwises, Natural convection of silica nanofluids in square and triangular enclosures: theoretical and experimental study, Int. J. Heat Mass Transf. 99 (2016) 792–804. [12] M. Bahiraei, M. Hangi, Automatic cooling by means of thermomagnetic phenomenon of magnetic nanofluid in a toroidal loop, Appl. Therm. Eng. 107 (2016) 700–708. [13] A. Malvandi, D.D. Ganji, I. Pop, Laminar filmwise condensation of nanofluids over a vertical plate considering nanoparticles migration, Appl. Therm. Eng. 100 (2016) 979–986. [14] M. Bahiraei, R. Khosravi, S. Heshmatian, Assessment and optimization of hydrothermal characteristics for a non-Newtonian nanofluid flow within miniaturized concentric-tube heat exchanger considering designer’s viewpoint, Appl. Therm. Eng. 123 (2017) 266–276. [15] M. Bahiraei, Particle migration in nanofluids: a critical review, Int. J. Therm. Sci. 109 (2016) 90–113. [16] M. Sheikholeslami, T. Hayat, A. Alsaedi, S. Abelman, Numerical analysis of EHD nanofluid force convective heat transfer considering electric field dependent viscosity, Int. J. Heat Mass Transf. 108 (Part B) (2017) 2558–2565. [17] M.M. Sarafraz, A. Arya, F. Hormozi, V. Nikkhah, On the convective thermal performance of a CPU cooler working with liquid gallium and CuO/water nanofluid: a comparative study, Appl. Therm. Eng. 112 (2017) 1373–1381. [18] M.R. Sohel, R. Saidur, S.S. Khaleduzzaman, T.A. Ibrahim, Cooling performance investigation of electronics cooling system using Al2O3–H2O nanofluid, Int. Commun. Heat Mass Transf. 65 (2015) 89–93. [19] M. Rafati, A.A. Hamidi, M. Shariati Niaser, Application of nanofluids in computer cooling systems, heat transfer performance of nanofluids, Appl. Therm. Eng. 45–46 (2012) 9–14. [20] H. Akhavan-Zanjani, M. Saffar-Avval, M. Mansourkiaei, F. Sharif, M. Ahadi, Experimental investigation of laminar forced convective heat transfer of graphene–water nanofluid inside a circular tube, Int. J. Therm. Sci. 100 (2016) 316–323. [21] H. Khajeh Arzani, A. Amiri, S.N. Kazi, B.T. Chew, A. Badarudin, Int. Commun. Heat Mass Transf. 68 (2015) 267–275. [22] M. Vakili, S. Khosrojerdi, P. Aghajannezhad, M. Yahyaei, Int. Commun. Heat Mass Transf. 82 (2017) 40–48. [23] S.G. Kandlikar, Z. Lua, W.E. Domigana, A.D. Whitea, M.W. Benedicta, Measurement of flow maldistribution in parallel channels and its application to ex-situ and in-situ experiments in PEMFC water management studies, Int. J. Heat Mass Transf. 52 (2009) 1741–1752. [24] B. Ramos-Alvarado, P. Li, H. Liu, A. Hernandez-Guerrero, CFD study of liquidcooled heat sinks with microchannel flow field configurations for electronics, fuel cells, and concentrated solar cells, Appl. Therm. Eng. 31 (2011) 2494– 2507. [25] I. Chowdhury, R. Prasher, K. Lofgreen, G. Chrysler, S. Narasimhan, R. Mahajan, et al., On-chip cooling by superlattice-based thin-film thermoelectrics, Nat. Nano 4 (2009) 235–238. [26] P.Y. Paik, V.K. Pamula, K. Chakrabarty, Adaptive cooling of integrated circuits using digital microfluidics, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 16 (2008) 432–443. [27] H. Yarmand, S. Gharehkhani, G. Ahmadi, S.F. Seyed Shirazi, S. Baradaran, E. Montazer, M.N.M. Zubir, M.S. Alehashem, S.N. Kazi, M. Dahari, Graphene nanoplatelets–silver hybrid nanofluids for enhanced heat transfer, Energy Convers. Manage. 100 (2015) 419–428.