“…Based on the concepts of mixing theory for ideal gas mixtures and classical and statistical mechanisms, the density of the nanofluid ( nf ) is respectively given by Equations (3) and (4), which are related to volume fraction ( ), bulk fluid density ( bf ), and nanoparticle density ( p ) [13,15,28,29]:…”
Section: Related Theories and Calculationsmentioning
confidence: 99%
“…Nanofluid is a solidliquid mixture, and therefore the concepts of mixing theory for ideal gas mixtures, and classical and statistical mechanism are useful to estimate the nanofluid density. The density of nanoparticles is usually greater than that of the bulk liquid; therefore, according to the concepts of mixing theory for ideal gas mixtures [14][15][16][17][18][19][20] and classical and statistical mechanisms [20][21][22][23][24][25][26][27][28][29], adding nanoparticles to the bulk liquid increases the density of the bulk liquid. Similarly, estimating the specific heat of nanofluid often requires the concepts of mixing theory for ideal gas mixtures and classical and statistical mechanism.…”
This study analyses the density and specific heat of alumina (Al 2 O 3 )/water nanofluid to determine the feasibility of relative calculations. The Al 2 O 3 /water nanofluid was produced by the directsynthesis method with cationic chitosan dispersant served as the experimental sample, and was dispersed into three concentrations of 0.5, 1.0 and 1.5 wt.%. This experiment measures the density and specific heat of nanofluid with weight fractions and sample temperatures with a liquid density meter and a differential scanning calorimeter (DSC). To assess the availability of these equations, it then compares the experimental data with the calculated results according to the concepts of mixing theory and statistical mechanism. Comparing the calculated results of density and specific heat with the experimental data, the deviation of density fell within the range of À1.50% to 0.06% and 0.25% to 2.53%, whereas the deviation of specific heat fell within the range of À0.07% to 5.88% and À0.35% to 4.94%, respectively. Calculated results of density and specific heat show a trend of greater deviation with an increased concentration of nanofluid. However, two kinds of density and specific heat of the calculated results fall within an acceptable deviation range in this study.
“…Based on the concepts of mixing theory for ideal gas mixtures and classical and statistical mechanisms, the density of the nanofluid ( nf ) is respectively given by Equations (3) and (4), which are related to volume fraction ( ), bulk fluid density ( bf ), and nanoparticle density ( p ) [13,15,28,29]:…”
Section: Related Theories and Calculationsmentioning
confidence: 99%
“…Nanofluid is a solidliquid mixture, and therefore the concepts of mixing theory for ideal gas mixtures, and classical and statistical mechanism are useful to estimate the nanofluid density. The density of nanoparticles is usually greater than that of the bulk liquid; therefore, according to the concepts of mixing theory for ideal gas mixtures [14][15][16][17][18][19][20] and classical and statistical mechanisms [20][21][22][23][24][25][26][27][28][29], adding nanoparticles to the bulk liquid increases the density of the bulk liquid. Similarly, estimating the specific heat of nanofluid often requires the concepts of mixing theory for ideal gas mixtures and classical and statistical mechanism.…”
This study analyses the density and specific heat of alumina (Al 2 O 3 )/water nanofluid to determine the feasibility of relative calculations. The Al 2 O 3 /water nanofluid was produced by the directsynthesis method with cationic chitosan dispersant served as the experimental sample, and was dispersed into three concentrations of 0.5, 1.0 and 1.5 wt.%. This experiment measures the density and specific heat of nanofluid with weight fractions and sample temperatures with a liquid density meter and a differential scanning calorimeter (DSC). To assess the availability of these equations, it then compares the experimental data with the calculated results according to the concepts of mixing theory and statistical mechanism. Comparing the calculated results of density and specific heat with the experimental data, the deviation of density fell within the range of À1.50% to 0.06% and 0.25% to 2.53%, whereas the deviation of specific heat fell within the range of À0.07% to 5.88% and À0.35% to 4.94%, respectively. Calculated results of density and specific heat show a trend of greater deviation with an increased concentration of nanofluid. However, two kinds of density and specific heat of the calculated results fall within an acceptable deviation range in this study.
“…There are some reports on enhancement in convective heat transfer by nanofluids. [7,[17][18][19][20][21][22][23][24][25][26][27][28][29] However, there are few studies showing inconsistent results as reported by Pak and Cho, [25] Chein and Chuang, [26] Ding et al, [27] Lee and Mudawar [28] and Nelson et al, [29] and also studies showing a decrease in heat transfer coefficient by the addition of nanoparticles to the base fluids. [27,30] The experiments were usually carried out in a pipe or channel flows with constant heat flux.…”
Nanofluids with their enhanced thermal conductivity are believed to be a promising coolant in heat transfer applications. In this study, carbon nanotube (CNT) nanofluids of 0.01 wt%, stabilised by 1.0 wt% gum arabic were used as a cooling liquid in a concentric tube laminar flow heat exchanger. The flow rate of cold fluid varied from 10 to 50 g/s. Both experimental and numerical simulations were carried out to determine the heat transfer enhancement using CNT nanofluids. Computational fluid dynamics (CFD) simulations were carried out using Fluent v 6.3 by assuming single-phase approximation. Thermal conductivity, density and rheology of the nanofluid were also measured as a function of temperature. The results showed thermal conductivity enhancement from 4% to 125% and nearly 70% enhancement in heat transfer with increase in flow rate. Numerical results exhibited good agreement with the experimental results with a deviation of AE3:0%. CNT nanofluids at 0.01 wt% CNTs showed Newtonian behaviour with no significant increase in the density.
“…16 and 17 show the thermal conductivity ratio of the two types of nanoparticles Cu and TiO2 with three types of the base fluids DW, EG, EG + DW. As well as the measure values for Cp compared with the two models of Cp [23,24] which are shown in Figs. 18 and 19.…”
Section: Measurement Of the Nanofluid Thermal Propertiesmentioning
confidence: 99%
“…The improvement of the fluid thermal conductivity is due to disperse in fluid of the conventional heat transfer and occurs without problems as plug and deposition and sedimentation and clogging. Pak and Cho [1], investigated experimentally the turbulent friction and heat transfer behaviors of dispersed fluids (Al2O3 and TiO2 particles suspended in water) in a circular pipe. Lee et al [2], observed enhancement of thermal conductivity of nanofluids using CuO and Al2O3 nanoparticles with water and ethylene glycol compared to base fluids.…”
Nanofluid ethylene glycol enhancement metallic nano metallic
A R T I C L E I N F O
A B S T R A C TThis experimental investigation was performed to improve heat transfer in the heat exchanger (tube of shell and helically coiled (using nanoparticles for turbulent parallel flow and counter flow of distilled water (Dw) and ethylene glycol (EG) fluids. Six types of nanofluids have been used namely: copper -distilled water, copper -distilled water and ethylene glycol, copper -ethylene glycol, titanium oxide -distilled water, titanium oxide -distilled water and ethylene glycol, titanium oxide -ethylene glycol with 0.5%,1%,2%,3% and 5% volume concentration as well as the range of Reynolds number are 4000 -15000. The experimental results reveal that an increase in coefficient of heat transfer of 50.2 % to Cu -Dw, 41.5% to Cu -( EG + Dw ), 32.12 % for Cu -EG , 36.5% for TiO2 -Dw, 30.2 % to TiO2 -( EG + Dw) and 25.5%, to TiO2 -EG . The strong nanoconvection currents and good mixing caused by the presence of Cu and TiO2 nanoparticles. The metal nanofluids give more improvement than oxide nanofluids. The shear stress of nanofluids increases with concentration of nanoparticles in the case of parallel and counter flow. The effect of flow direction is insignificant on coefficient of overall heat transfer and the nanofluids behave as the Newtonian fluid for 0.5%,1%,2%,3% and 5%. Good assent between the practical data and analytical prediction to nanofluids friction factor which means the nanofluid endure pump power with no penalty. This study reveals that the thermal performance from nanofluid Cu -Dw is higher than Cu -(EG + Dw) and Cu -EG due to higher thermal conductivity for the copper and distilled water compared with ethylene glycol.
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