Discuss about the Heat Exchangers High-Density Fluids and CNT.
In order to cope with the environmental challenges and get better and efficient methods of heat dissipation, it is necessary to adopt methods that can enable us to dissipate heat efficiently. The introduction of the heat exchangers and other energy recovery devices has then proven to be the unavoidable choice to reduce consumption of energy. Carbon nanotubes total heat exchangers is the new focus due to its rare advantages. The heat transfer performance of the carbon nanotube (CNT) directly and positively affects the performance of the total heat exchanger. Recent research and studies show that CNT carries better thermal conductivity as well as faster moisture transferability. Thus, studying heat transfer characteristics of carbon nanotube shall offer a primary data for the application of CNT based materials in total heat exchangers.
The total heat exchanger is the core component of any fresh air energy recovery system. Currently, there exist three main types including the wheel type, the heat-pipe type, and the fixed-plate type. The wheel type is the most broadly used type of heat recovery device. However, it is prone to leaking and requires a lot of maintenance. In the recent past, energy recovery systems that are membrane-based have caught the attention of many people due to zero air leakage, simple system and characteristics of anti-icing (Chen et al, 2016).
This study of the heat exchanger concentrates on improving the rate of saving energy by the ventilation system, studying the parameters affecting the system as well as examining the processes of heat and moisture transfer. From the experiments conducted below, the energy saving performance of the CNT membrane-based heat exchanger system was calculated. The results show a 58% energy saving rate of the CNT membrane-based heat recovery equipment (Hosseini et al, 2017). Further studies by other scholars show that the system’s performance depends on the complex function of outdoor humidity and temperature. Further studies of the performance of a novel quasi-countercurrent membrane-based total heat exchanger system in cold weather established a model for analysis at a lower temperature to analyse the transfer of heat of the heat exchanger device (Aghabozorg et al, 2016).
The studies of membrane-based CNT materials in heat exchangers concentrates on improving moisture and heat transfer ability of the heat recovery devices, as well as enhancing the effect of the barrier of carbon dioxide. Since the inception of CNT in 1991, it has attracted much attention in several realms. This is due to its unique thermal, electrical and other physical properties. CNT has its applications in sensors, water filtration, rechargeable batteries, high strength materials and many more (Xing et al, 2017). It is a fact that there are few reports on the application of CNT in heat exchanger systems (Halelfadl et al, 2014). Therefore, this report will investigate the performance of CNT membrane-based heat recovery devices and provide a first-hand information and data.
The membrane materials applied in this experiment include the carbon nanotube membrane (M1) comprising of the carbon nanotube (M5) as well as the hydrophilic substrate
(M2), commercial carbon nanotube (M3), and a commercial total heat exchange membrane
(M4). Figure 1 below shows the set up of the experiment.
The test chamber has two layers, that is, the lower layer and the upper layer. Air is passed through each layer to the outlet of the test chamber from the inlet of the test rig. Both the inlet and the outlet of the test chambers have humidity and temperature sensors placed in them. The membranes are positioned between the two layers’ intermediate partition. For the area of a
membrane to be effective it has to be 34.21cm2 and each layer’s cross-sectional area is 12.4cm2. A gas heating device regulates the temperature of the hot air and the flow meter regulates the rate of flow of inlet air.
Consideration of the chamber’s heat dissipation is crucial because of the difference in temperature of the air inside the chamber and the air outside the chamber (Khattak et al, 2016). Thus, calculation of the heat dissipation coefficient goes as follows.
The dissipation of heat of the chamber to the environment is equivalent to the sensible difference of heat of the air through the chamber, and is as illustrated in the following equation:
Where Cpa is the specific heat;
ma is the mass flow rate;
????a is the density;
Qa is the volume flow rate.
The ambient temperature of the set-up is 22 degrees Celsius and the flow rates of the two layers of the chamber are 30L/min each. The coefficient of heat dissipation of the whole chamber is 1.84.
This section deals with the comparison of heat transfer characteristics under the same temperature and flow rate of the outlet and the inlet air temperatures of the hot air are given by t1 double prime and t1 prime respectively. Thus the quantity of heat transfer is illustrated below
The ambient temperature of the chamber is regulated at 21 degrees Celsius. The passage of the hot air is the upper layer and the temperature of the inlet hot air is 41 degrees Celsius. The ambient air passage is offered by the lower layer. The two layers both have a flow rate of
30L/min. Table 1 shows the results of various heat transfer membranes.
It is evident from the results that CNT (M1) has a better performance of heat transfer than M3 and M4. It is also evident that the thermal resistance of M5 is 0.009, which is a tenth of the thermal resistance of M1. For the sake of further studies of CNT heat transfer characteristics, inlet temperature’s effect on the performance of the heat transfer of CNT is also evident.
|
Membrane |
H/ W·m-2·K-1 |
H-1/W-1·m2·K |
|
M1 |
10.53 |
0.095 |
|
M2 |
11.65 |
0.086 |
|
M3 |
7.00 |
0.142 |
|
M4 |
5.85 |
0.171 |
This section further discusses the relationship between the temperature at the inlet part of the chamber and the heat transfer characteristics of CNTs M1, M2, and M5 at different temperatures of the incoming hot air at the inlet part of the chamber. The passage of the hot air is provided by the upper layer and has an inlet temperature of 32.6 degrees Celsius, 42.2 degrees Celsius, and 50.4 degrees Celsius. The two layers both have a flow rate of 30 L/min.
With respect to equation 3 and equation 4, the total coefficient of heat transfer of M2 and M1 can be obtained by calculation and then calculate the thermal resistance. Figure 2 shows the thermal resistances of M5, M1, and M2. It is evident from figure 2 that thermal resistances of M1 and that of M2 are not significantly affected by the variation in the inlet temperature. Similarly, the thermal resistance of M5 stagnated even with the inlet temperature change.
Coefficients of heat transfer of M5 at various temperatures are demonstrated in figure 3 below. The results show that the coefficient of heat transfer of the CNT is not significantly affected by the variation of the inlet temperature. The average coefficient of heat transfer of CNT is 49.11.
In this study, experimental system of the test is embraced for the investigation of the characteristics of heat transfer of various membranes such as commercial carbon nanotube membrane, carbon nanotube composite membrane as well as the commercial total heat recovery membrane. The effect of the inlet temperature on the CNT membrane comparing the three membranes was investigated and the results were:
There has been a recent advancement in the field of CNT nanofluids leading to a corresponding increase in their scope of application. The applications are heat dissipation, engine transmission fluids, control reactivity amongst others (Esfe et al, 2014). This study focusses on the heat transfer of CNT nanofluids in a power transformer. A significant amount of heat is generated in a transformer during the normal operation as a result of transformer core losses and copper losses. This heat needs to be dissipated out of the transformer so as to increase its efficiency and reduce its workload. The transformer oil that is currently in use has a lower thermal conductivity. The inclusion of nanoparticles with a higher thermal conductivity can improve the overall heat conductivity of the transformer oil. Many research papers record that addition of engineered nanoparticles enhances the thermal performance of the conventional transformer oil (Huang et al, 2016).
There are limited research studies on the use of nanofluids in the transformer world. Studies show that mass flow rate fluctuation in vertical channels occurs as a result of flow in the horizontal channels (Goodarzi et al, 2015). This concludes that coefficient of heat transfer depends on the Reynolds number and Grashof number. Studies of the behavior of heat in a 3-phase transformer using a 3-D finite volume method show that forced convection by water has better heat transfer characteristics than natural air convection for cases dry-type (Huminic & Huminic (2016)).
CFD simulations were performed using a sliced model to study the thermal performance of an (ONAN) distributor transformer. Velocity and temperature profiles were investigated at various conditions and the pattern of fluid flow was realised to be similar in all scenarios (Knowles et al, 2015). In another study where such parameters as temperature change distribution, electrophoresis, and velocity distribution were examined using silicon carbide nanofluids, it was found out that the heat performance of the transformer oil greatly and significantly improved. This was due to the addition of the nanofluids (Ellahi et al, 2015).
Numerical simulations are carried out in a model of a distributor transformer filled with oil for investigating effects of transport and flow of fluid at different concentrations of nanoparticles. 3-D CFD simulations on two different nanofluid systems to examine heat transfer by natural convection. The overall effect on heat transfer performance is approximated and a comparison made with a base fluid which is the transformer oil. Nusselt number, overall coefficient of heat transfer and Rayleigh number are approximated at various particles loading to examine the nanoparticle concentration effect on the performance of heat transfer in the model (Wu et al, 2016).
The study of the improvement of heat dissipation in a distributor transformer is investigated using two kinds of nanofluids. A base fluid which is the transformer oil is chosen along with single-wall carbon nanotubes with graphite. The carbon nanotube is chosen due to its good thermal conductivity (Megatif et al, 2016). Figure 1 below shows the slice model of the distributor transformer under investigation, while table 1 below shows the dimensions of the considered geometry of the slice model under investigation.
All the internal surfaces of the cores and windings are considered to have a uniform heat flux. The coefficient of heat transfer is set on the outer surface of the fin that shows the outside air’s effect of cooling. For turbulent flow effects, a density-based solver is used. Steady-state simulations are conducted to investigate the heat transfer behavior of natural convection. K-epsilon RNG turbulence model is triggered to swirl effects then there is an application of a pseudo transient method for a steady state solution. Similarly, a method of the first-order solution is preserved for turbulent kinetic energy, momentum, pressure, and rate of turbulent dissipation under the SIMPLE scheme.
In addition, fluid thermal properties are taken as Boussinesq, a polynomial of the first order, and the polynomial of second order for density, specific heat, and viscosity in that order. The effective coefficient of thermal expansion and the effective thermal conductivity are taken to be constant. The convergence criteria are set by the calibrated residuals for velocity, energy, and continuity.
Simulations were conducted for the base fluid and the nanofluids at the various particle loading
(0-2%). The overall coefficient of heat transfer is investigated, estimated and compared as shown in figure 5. Below. Calculation of the overall coefficient of heat transfer is done based on the average coefficient of heat transfer for each heating surface area (Sarafraz & Hormozi, 2016). It is noticed that transformer oil containing nanoparticles exhibit improvement in coefficient of heat transfer compared to transformer oil without the nanoparticles. Further, an increase in nanoparticles leads to a corresponding improvement of thermal properties. CNT based nanofluids show a higher efficiency heat transfer performance based on the overall coefficient of heat transfer as compared to other materials (Zing & Mahjoob, 2017). Figure 6 below, shows the enhancement percentage of heat transfer. The calculations are done on the basis of an overall coefficient of heat transfer of transformer oil and the nanofluid illustrated by equation 1. At critical concentration where density dominates, natural convection improvement vanishes. A transformer oil having 2% CNT exhibits a 50.35% improvement in heat transfer.
The characteristics of fluid flow and heat transfer of CNT and graphite based nanofluids are investigated at various particles loading in a transformer oil. The natural convection inside the model causes buoyancy that creates a movement of the nanofluids and the transformer oil (Ebrahimnia-Bajestan et al, 2016). Figure 2 gives a comparison of the temperature distributions in transformer oil with nanoparticles and the one without nanoparticles. Figure 3 shows the
contours of the velocity distribution.
The simulations reveal that CNT nanofluids exhibit high rates of velocity compared to transformer oil and graphite based nanofluids. Figure 4 below shows the velocity vectors of the fluids used in the simulation. Their velocity vectors show a similar direction of flow.
Calculation of Nusselt number is based on the overall coefficient of heat transfer for an individual fluid with a total characteristic heating surface length and thermal conductivity average values. Calculation of Rayleigh number for an individual heating surface is based on surface and fluid average temperatures. Calculation of area-weighted average Rayleigh number is based on all the surfaces of heating (Estellé et al, 2017). Figure 7 below shows the relationship between area-weighted Rayleigh number and Nusselt number.
Conclusion
The studies conducted above show that density and thermal conductivity significantly affect the heat transfer characteristics. Nanofluids have better heat dissipation properties than the transformer oil. Thus, adding nanoparticles to the transformer oil can improve its heat transfer characteristics. During the increment process, the heat performance starts to drop when the density becomes more than the thermal conductivity. A transformer oil having 2% CNT exhibits a 50.35% improvement in heat transfer and is deemed to be the best nanofluid for heat transfer performance. Thus, if the nanofluids are properly applied in the transformer industry a new design of small-sized transformers can be realized.
References
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