Previous experiments have indicated a substantially higher feature in thermal conductivity with the use of nanofluid in comparison to base fluids. These nanofluid are composed of suspended nanoparticles that change the thermal and transport properties the referenced base fluids. Numerous research literature has depicted almost similar characteristics in some of the nanoparticles to be used. The nanofluid include; copper in water and alumina in water being the major technology inventions. Adding solid particles into media that transfers heat has been known for a long time as a strategy used in heat transfer enhancement. The main consideration in using this technique is the use of suspended micrometre- or multimeter particles having the high potential of causing severe problems. The problems include; high drop in pressure, clogging, abrasion and particle sedimentation. Such properties hinder sedimentation during flow thereby preventing clogging. Taking note of these points, there are studies that were performed research on the transfer of heat in suspended nanoparticles such as Al 2 O 3 dispersed nanoparticles in flowing water in microprocessor cooling system. The 36nm nanoparticles had the greater coefficient of heat transfer than 47nm nanofluid particles (Mahmood, 2011).
Nanofluid preparation
There exist 2 major methods used in nanofluid preparation. They include;
Thermal Conductivity
This parameter is very important during enhancement of transfer of heat in the base fluid performance. The solid metals’ thermal conductivity tends to be very high, solid metals conduct thermal heat faster than fluids. This, therefore, brings in the need to suspend metals in fluids to increase their thermal conductivity and relatively increasing their performance in heat transfer. Experiments have reportedly been done on nanofluid thermal conductivity using various methods. Some of these methods that were focused on are; parallel plate steady-state method, oscillating temperature method and hot-wire transient methods. The most used method for thermal conductivity determination is the hot-wired transient method. The alumina thermal conductivity has been experimented by many researchers (Gui, 2015). A summarized result from these experiments has revealed that there is an increase in thermal conductivity as the nanoparticle volume fraction is increased. Also, the decrease in particle size would increase the thermal conductivity. Other influences include the nanoparticle’s shape that affects thermal conductivity as well as nanoparticle’s Brownian motion and temperature. Additives and interfacial layer in the nanofluid also influence thermal conductivity (Mahmood, 2011)
Purpose Statement
This study involves an analysis of alumina nanofluid preparation as well as its thermal conductivity evaluation in regards to double pipe heat exchangers for possible applications in the various fields requiring cooling of devices.
Research Question
In the study, there are various components of the used nanofluid that affect its operation and will be experimented on. They include volume fraction concentration, temperature, nanoparticle size and shape as well as their comparison to available correlated information.
Experimental setup
The apparatus in this study include; a heat exchanger test section, 2 gear pumps that are magnetic and two tanks. Including a pump that would transport nanofluid, that is hot and one more pump transporting cold water is important. The section of the test consists of a double pipe countercurrent heat exchanger of length 120 cm. the sued nanofluid flows in the exchanger into the designated pipe while the cold water gets into the pipe’s annular space. The pipe is inside is of soft steel having a 6 mm inner diameter, 8 mm outer diameter and a 2 mm thickness. The steel-tubed outside pipe would have a 14 mm inner diameter, 16 mm outer diameter and a 2 mm thickness. Reduction of heat loss along the axis involves the bottom and top of the test be insulated using plastic tubes. Measuring the outlet and inlet nanofluid temperatures together with the temperature of the cold water outlet and inlet section required the use of 4 RTD thermometer. It is prudent to get the temperature reading of six positions at the test section’s outer surface so as to get the Nusselt number that is average. All these six temperature probes that are to be evaluated are to be connected to sets of data logger (Bradshaw, 2016). Pressure drops are measured using manometers with an inclined u-tube all-round the test. The stainless steel tanks of volume 15-litres are for the storage of the cold water and the nanofluid. For the maintenance of fluid temperature, a thermostat and a cooling tank are used. A thermostat and an electric heater are installed on these to maintain the nanofluids temperature. The error of the measured Nusselt number is influenced by temperature measurement and the nanofluid and cold water flow. In the test, the test section’s wall temperature, nanofluids outlet and inlet temperature, cold water’s temperature and the mass’s flow rate are measured (Davood & Amir, 2016).
The experiment uses a 99.0+% aluminium oxide that is pure and dispersed in water having an average size of its particle at 20 nm. Deionized water was mixed with the nanofluid. A preparation of the experiment’s concentration involves the nanofluid having its nanoparticles less than 4% for stability that would last over a week. No necessary intermediate mixing was considered necessary (Kumar, 2009).
The data from the experiment were used in calculating the overall coefficient of heat transfer, the coefficient of heat transfer convective as well as the nanofluids’ Nusselt number using various concentration volume of particles and Peclet numbers. Inflow of fluids in heat exchange having concentric tubes, the transferring rate of heat in the inner tube’s hot Al 2 O 3 nanofluid had the expression below;
(Hot Nanofluid) Q = (Tout – T in ) m o (hot nanofluid) C p (hot nanofluid) (Chhabra & Richardson, 2011)
Mo = flow rate of hot nanofluid’s mass
T out = outlet temperature of hot nanofluid.
T in = hot nanofluid’s inlet temperature.
The transfer of heat in the outer tube’s cold water fluid is as below;
Q ( Coldwater fluid) = m o( cold water fluid) C p ( cold water fluid) ( T in – T out) (Mahmood, 2011)
M O = mass’s cold water fluid flow rate
T in = inlet temperature
T out = outlet temperature
The nanofluid’s effective density is as below;
ρ nf = ( 1 – ?v) ρf + ?vρp (Mourad, et al., 2016)
subscripts p, f and nf reference nanoparticle, base fluid and nanofluid respectively.
Φv represents volume concentration of the nanoparticle.
C pnf represents the nanofluid’s specific heat
(ρCp)nf = (1 − φV)(ρCp)f + φV(ρC)p (Mourad, et al., 2016)
The coefficient of the test fluid’s heat transfer h i , is calculated as;
1Ui=1hi+DiLn(Do/Di)2kw+DiDo+1ho, (Mourad, et al., 2016)
D o and D i are the outer and inner diameters respectively. U i represents the overall coefficient of heat transfer of the inner tube’s area. h o and h i are coefficients of heat transfer of the outside and inside tubes respectively. k w represents tube wall’s thermal conductivity. U i is calculated as;
Q = UiAiΔTlm, (Mourad, et al., 2016)
A i = πD i L (Mourad, et al., 2016)
ΔT lm = logarithmic mean difference in temperature.
Bell’s procedure obtains the coefficient of outside heat transfer.
Convection of heat transfer is obtained in th test section is calculated as;
Q(convection)=hiAi(T~w−Tb),Tb=Tout(nano fluid(hot fluid))+Tin(nano fluid(hot fluid))2,(T~w=∑Tw6) (Davood & Amir, 2016)
T w = inner tube’s outer wall temperature of the local surface.
T w ~ = T w 6 points lined the test tube’s exit and inlet.
The coefficient of heat transfer h i and Nu the Nusselt number is as below;
hi=m?(nano fluid(hot fluid))Cp(nano fluid(hot fluid))(Tout−Tin)Ai(T~w−Tb),Nunf=hidiknf (Davood & Amir, 2016)
k nf = effective thermal conductivity calculated using Maxwell’s model below.
knf=kfkp+2kf−2φV(kf−kp)kp+2kf+φV(kf−kp) (Davood & Amir, 2016)
Measurements accuracy was calculated by testing the experimental setup using distilled water before determining the convective transfer of nanofluid heat depicting a comparison measurement and prediction. h i is turbulent flow in the tube evaluated using Gneilinski correlation.
Nu = 0.012(Re0.87 − 280)Pr?0.4 (Davood & Amir, 2016)
The comparison is as below;
Nanofluid’s Convective Heat Transfer
The figure below shows the overall nanofluid coefficient heat transfer of aluminium oxide and water in Reynold’s number at various volume concentration indicating an increase in coefficient heat transfer overall with the nanofluid’s temperature and Reynold’s number.
The coefficient also increases with increased concentration having constant Reynold’s number (Mohab, et al., 2016). The overall coefficient of heat transfer is highest in nanofluid of aluminium oxide with 0.3 concentration and 27000 Reynold’s number is to increase to 9.2% 400C base fluid temperature comparison. In water, the coefficient is to be 6.82 percent for 400C base fluid comparison (concentration of 0.1 and Reynold’s number 27000). The increase in coefficient would also be observed as shown below;
Possible reasons for increased coefficient of heat transfer overall with Reynold’s number increase would be due to (Chhabra & Richardson, 2011);
Experimental results are compared with the Li and Xuan correlation, Nunf=0.0059(1+7.6286φ0.6886VPe0.001p) Re0.9238nfPr0.4nf producing the graph below;
This study is to investigate the effects of flowing nanofluid temperature, Reynold’s number and concentration of nanoparticle on the transfer of heat (Donald & Adrian, 2017). The results would then be consistent with the correlation available in that Nusselt number would increase with Reynold’s number. Possible graph depiction as;
Alumina Nanofluid Applications
The cooling effect of nanofluid is very useful and could be applied in cooling automobile engines as well as welding equipment. Any device exhibiting heat flux like heavily powered microwave tubes and heavily powered arrays of laser diodes. The coolant composed of nanofluid can be put to flow into the spaces in MEMs cooling it and improving their performance. Measuring CHF nanofluid has also proven to be key in applications in nuclear. An improvement of efficiency in chilling by 1% would mean saving about 320billions of kWh electricity. Another area of application is deep drilling. This fluid is possible to be used in increasing a transformer’s oil life and dielectric strength through dispersion of particles. Such cooling features could prove to be important in Navy field applications in instances of heavy power generation with a reduced size of the transformer and its weight. Increase in electricity demand has led to an increase in electricity reduction, thereby, an upgrade or replacement of transformers is important without raising its cost. In the process of avoiding such high cost, the oil could be improved by increasing its thermal conductivity property with the addition of nanoparticles in it.
Conclusion
There has been a focus of attention on the improvement of efficiency of heat exchangers with the addition of solid articles. Investigations have been done on various nanoparticles for thermophysical and hydrodynamic properties but lacked evaluation of turbulent flow of nanofluid effect on the heat transfer (Gui, 2015). This experiment focuses on enhancing heat transfer using nanoparticles of aluminium oxide together with water in turbulent flow in the double-pipe heat exchanger. 20 nm aluminium oxide particles are to be suspended at 0.1 to 0.3% volume concentration in water (Vincenzo, et al., 2015). The result was an increase in average coefficient of heat transfer with the increase of nanoparticles in the nanofluid. The alumina nanofluid has proven to be important in its numerous heat transfer capabilities. This brings in the various research that has been focusing on stable nanofluid preparation with the use of pH optimization, various surfactants, different nanofluid temperatures and surface nanoparticle modification. The observed thermal conductivity in alumina, however, is not that consistent due to various conditions of experiments performed. There is some nanofluid that has been prepared to have basic and acidic media thereby making them inapplicable in this study. The temperature effect is the key in thermal conductivity in constant volume concentration, However, an extensive research is required on this study to obtain more relations of the heat transfer features (Mourad, et al., 2016)
References
Bradshaw, P., 2016. Experimental Fluid Mechanics: Thermodynamics and Fluid Mechanics Division. 2 ed. Chester: Elsevier.
Chhabra, P. & Richardson, J., 2011. Non-Newtonian Flow and Applied Rheology: Engineering Applications. 2, revised ed. Leeds: Elsevier.
Davood, D. & Amir, M., 2016. Heat Transfer Enhancement Using Nanofluid Flow in Microchannels: Simulation of Heat and Mass Transfer. 1 ed. Durham: Elsevier Science.
Donald, A. & Adrian, B., 2017. Convection in Porous Media. 5, illustrated ed. Leeds: Springer.
Gui, L., 2015. Dynamic Wetting by Nanofluids. illustrated ed. Nottingham: Springer.
Kumar, S., 2009. Thermal Science And Engineering. 1 ed. Durham: S. K. Kataria & Sons.
Mahmood, A., 2011. Nanocoatings: Size Effect in Nanostructured Films. illustrated ed. Bath: Springer Science & Business Media.
Mohab, A., Ghada, A., Wesam, S. & Mona, E., 2016. Nanovate: Commercializing Disruptive Nanotechnologies. illustrated ed. Gloucester: Springer.
Mourad, R., Sadik, K. & Renato, M., 2016. Microscale and Nanoscale Heat Transfer: Analysis, Design, and Application. illustrated ed. Liverpool: CRC Press.
Vincenzo, B., Oronzio, M., Sergio, N. & Kambiz, V., 2015. Heat Transfer Enhancement with Nanofluids. illustrated ed. Nottingham: CRC Press.
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