Thermal Conductivity Of Ethylene Glycol Research Articles

Investigation on liquid thermal conductivity of ethylene glycol (EG)/water mixtures: A comparative experimental and molecular dynamics simulation study

Ethylene glycol (EG) and water mixtures can be used as the antifreeze, refrigerant and coolant due to their good environmental, thermodynamic and transport performance. The mixture’s accurate thermal conductivity is indispensable for the optimal design of the relevant heat exchangers. However, thermal conductivity of EG/water mixtures is lacking and is seen to limit the further industrial application. In this paper, the transient hot wire method (THW) was used to measure the thermal conductivities of five EG/water mixtures (with ethylene glycol mass fraction wEG = 0, 0.25, 0.50, 0.75, 1.00) in the temperature range from 253.15 K to 353.15 K and pressures up to 8.0 MPa. A comparison was made between the present experimental data and literature values, yielding a good agreement. The AADs are within 2.65% for EG/water mixture systems. For convenience of engineering calculations, the present experimental data were correlated as a function of temperature and pressure. The average absolute deviations (AADs) between the experimental thermal conductivities and the calculations are 0.50%, 0.10%, 0.60%, 0.40% and 0.32% for mixture systems with ethylene glycol mass fraction wEG = 0.00, 0.25, 0.50, 0.75, 1.00, respectively. In addition, non-equilibrium molecular dynamics was applied to study the thermal conductivities of ethylene glycol/water binary mixtures based on PCFF (polymer consistent force field) and SPC/E (extended simple point charge) model, showing a good agreement with the experimental data. This study can provide fundamental thermal conductivity data for the heat transfer application of ethylene glycol aqueous solution in the industrial field.

Thermal conductivity of ethylene glycol and propylene glycol nanofluids with boron nitride nano-barbs.

This study investigates the potential of composite allotrope boron nitride nanobarbs (BNNBs) as nanoparticles for enhancing the thermal conductivity of nanofluids based on mixtures of ethylene glycol and propylene glycol with water. BNNBs are allotrope composites composed of boron nitride nanotube cores with walls decorated with attached hexagonal boron nitride crystals, creating a jagged morphology that facilitates the formation of a connected network and contributes to the enhancement of thermal conductivity in nanofluids. BNNBs exhibit high thermal conductivity due to efficient phonon transfer and they are electrical insulators owing to their wide bandgap. The effect of BNNB concentration in carrier fluids on nanofluid thermal conductivity was investigated by introducing BNNBs into ethylene glycol-water and propylene glycol-water mixtures at 0-10 wt%. The results showed that BNNBs enhanced thermal conductivity of carrier fluids up to 45%, and the enhancement was proportional to the concentration of BNNBs in the carrier fluid. The study also investigated the dispersion stability of BNNBs in different solvents using Hansen Solubility Parameters, revealing that propylene glycol mixtures demonstrated better long-term stability compared to ethylene glycol mixtures. The findings suggest that BNNBs have great potential for use as thermally conductive nanoparticles in nanofluids for various heat transfer applications. Future research should focus on enhancing the dispersion stability of BNNB nanofluids and exploring the influence of BNNB morphology on the thermal conductivity and other thermophysical properties of nanofluids.

Thermal conductivity of ethylene glycol and water binary mixtures at evaluated temperature and pressure

Binary mixtures of ethylene glycol and water are widely used as working fluids in many areas, and the study of thermal conductivity is crucial in the determination of the heat transfer properties of related systems. In this paper, the thermal conductivity of these binary mixtures at a series of ethylene glycol concentrations (0 to 100 wt%) is measured experimentally using a transient hot-wire system at evaluated temperature and pressure range from 293–533 K and 0.1–15.0 MPa. Moreover, the experimental data are fitted into an equation as a function of temperature, pressure, and concentration, the average absolute deviation and maximum deviation between fitted values and experimental data are 0.29 % and 1.05 %, respectively, which shows that this equation describes our data very well. Furthermore, available experimental data in the literature are collected and the comparisons between literature data and our data are analyzed, our data agree well with most literature data. This work is expected to promote the use of binary mixtures of ethylene glycol and water as heat transfer working fluids in industries.

Thermal conductivity of ethylene glycol based nanofluids containing hybrid nanoparticles of SWCNT and Fe3O4 and its price-performance analysis for energy management

In the present study, the thermal conductivity (knf) of hybrid nanofluids containing SWCNT and Fe3O4 nanoparticles with a ratio of 40%–60% in ethylene glycol base fluid was investigated in the laboratory and performance analysis. The knf of hybrid prepared nanofluids was measured using KD2 device with the error of 5%. Then, the price-performance analysis of the nanofluid was performed to evaluate the performance and efficiency of the present nanofluids and mono nanofluids (SWCNT/ethylene glycol). Then, using artificial neural network (ANN), experimental data are modeled and estimated in terms of temperature and volume fraction of nanoparticles (ϕ). Sensitivity analysis of experimental data was also performed to slope the knf changes to determine the best temperature and ϕ for nanofluid operation. Experimental data show that nanofluids with ϕ=1% show more than 40% increase in knf. Price-performance analysis that presented to provide capability in energy management shows that the use of hybrid nanofluids can be more economical than the cost of mono nanofluids.

ANFIS modelling of effective thermal conductivity of ethylene glycol and water nanofluids for low temperature heat transfer application

Nanofluids have been proven to be promising coolants for heat transfer application due to their superior thermal properties. In the present work, experimental studies were carried out to investigate the thermal conductivity of ethylene glycol (EG) and water mixture (35:65 v/v in ratio) based CuO, Al2O3 and TiO2 nanofluids. The study has considered nanofluid containing nanoparticles in the concentration range of 0.2 to 2 wt%. The effect of nanoparticle concentration on thermal conductivity was investigated in terms of effective thermal conductivity (ratio of thermal conductivity of nanofluid to that of base fluid) at low temperatures (5 to 25 °C). Results indicated significant improvement in thermal conductivity at lower temperature of nanofluids. Enhancement of 6.34%, 4.87% and 3.59% in thermal conductivity was obtained for 2 wt% of concentration at 5 °C for CuO, Al2O3 and TiO2 nanoparticles respectively compared to EG:Water. Correlations for effective thermal conductivity were developed by regression considering two input (temperature, concentration) and three input (temperature, concentration and nanoparticles thermal conductivity) parameters using experimental results. An intelligent model, adaptive neuro-fuzzy inference system (ANFIS) approach was used to model the effective thermal conductivity of nanofluids. ANFIS model performed better than the correlations developed.

Comprehensive study concerned graphene nano-sheets dispersed in ethylene glycol: Experimental study and theoretical prediction of thermal conductivity

In this study, thermal conductivity of graphene nano-sheets (GNs)/ethylene glycol (EG) nanofluid was compared with EG thermal conductivity at 25–70°C and 0.005–0.5 wt% to examine the effects of GNs nanoparticles. For all samples, presence of nanoparticles intensifies EG thermal conductivity up to 54.6%. Moreover, loading GNs into EG inverts the dependency of the thermal conductivity to temperature. As the temperature rises, the thermal conductivity of the base fluid decreases, while for nanofluid, thermal conductivity increases. Based on the results, by incorporating more nanoparticles, the positive effects of nanoparticles on thermal conductivity s reduced. It was concluded that with increasing temperature, the effect of adding GNs on the thermal conductivity is strengthened. Neural network implementation showed that this method can forecast kGNs/EGkEG with maximum error of less than 3%.

Thermal conductivity measurement of VO2 nanofluid using bidirectional 3ω method

Nanofluids containing vanadium dioxide (VO2) are used in applications such as actuators, smart windows, and gastrointestinal tracts. Therefore, measuring the thermal conductivity of nanofluids with VO2 characteristics is important in various environmental industries. In this study, the bidirectional 3 omega (3ω) method was used to measure the thermal conductivity of nanofluids. Experimental equipment for measuring VO2 nanofluids in various environments was designed and fabricated. The effectiveness of the bidirectional 3ω equipment was verified by measuring the thermal conductivity of ethylene glycol, which has been extensively reported in the literature. The effects of elapsed time, specimen thickness, and operation temperature on the thermal conductivity are discussed in this paper. In addition, the measuring error was investigated with regard to the precipitation of particles in the suspension.

The effect of tungsten trioxide nanoparticles on the thermal conductivity of ethylene glycol under different sonication durations: An experimental examination

Incorporation of tungsten trioxide (WO3) nanoparticles into ethylene glycol (EG) was performed to examine the nanoparticles presence on thermal conductivity behavior. To explore the mass fraction and temperature efficacy on the amount of thermal conductivity enhancement, many samples were produced at 0.005, 0.01, 0.05, 0.1, 0.5, 1,5 wt% and conductivity measurements were performed at 5–65 °C. Stable samples were exposed to sonic waves for 15, 30, 45 and 60 min to inspect the thermal conductivity dependency on the sonication duration. Thermal conductivity improvement of EG was observed in all cases due to the presence of WO3 nanoparticles. Based on experiments, the best thermal conductivity enhancement has reached up to 32.38%. The rate of thermal conductivity enhancement was higher at low mass fractions. Temperature rising was found to amplify the positive nanoparticles efficacy. Eventually thermal conductivity improvements due to enlarged sonication time were reported up to 4%.

Improving the thermal conductivity of ethylene glycol by addition of hybrid nano-materials containing multi-walled carbon nanotubes and titanium dioxide: applicable for cooling and heating

In this research, thermal conductivity of a hybrid nanofluid containing multi-walled carbon nanotubes (MWCNTs) and titanium dioxide (TiO2) has been examined. Many samples were prepared by loading MWCNTs and TiO2 at 50:50 mass% into the ethylene glycol (EG) to measure the thermal conductivity at temperatures within 25–50 °C and solid volume fractions of 0–1%. The amount of positive nanoparticles incorporation efficacy on the thermal conductivity was influenced by the volume fraction as well as the temperature. As the temperature augmented from 25 to 50 °C, the EG thermal conductivity was intensified by 9.6%, while this figure for nanofluid at 1 vol.% was 18.2%. Therefore, it is concluded that the loading nanoparticles amplified the thermal conductivity sensitivity to temperature. Statistical analysis showed that the positive effects of loading nanoparticles on thermal conductivity are amplified by rising temperature. At 25 °C, loading nanoparticles (1 vol.%) can amplify the thermal conductivity by 16%, while at 50 °C, this figure was 25.18% (maximum thermal conductivity enhancement). A correlation was developed using response surface methodology (RSM), and the input parameters significance was specified applying analysis of variance (ANOVA). Finally, using 28 machine learning-based algorithms, the hybrid nanofluid thermal conductivity has been predicted and compared with the RSM outputs.

Heat transfer enhancement due to nanoparticles, magnetic field, thermal and exponential space-dependent heat source aspects in nanoliquid flow past a stretchable spinning disk

This study explores the heat transfer characteristics of nanoliquid flowing over a rotating disk in the presence of the applied magnetic field and convective boundary condition. The nanoliquid is flowing due to the rotation of the disk with uniform stretching of a disk along the radial direction. Effects of ESHS (exponential space-related heat source) and THS (thermal-related heat source) are the focal concern of this article. The effective thermal conductivity of ethylene glycol (EG)-based graphene oxide (GO) nanoliquid is estimated by using Nan’s model whereas effective dynamic viscosity is calculated through Brinkman model. The partial differential system which governed the problem is transformed by using Von-Karman stretching transformations to the ordinary differential system. The subsequent two-point ODBVP (ordinary differential boundary value problem) is treated numerically. The consequence of effective parameters of the problem on different flow fields is illustrated graphically. The numerical values of shear stress and heat transfer rate (Nusselt number) are also calculated. Further, the slope of the data points is determined to quantify the outcome. Validation of the present results is made by direct comparison with the available results and an excellent agreement is found. It is found that the rate of heat transfer increased with nanoparticle volume fraction at the rate 0.4153 and the friction factor increased by increasing nanoparticle volume fraction at the rate 3.0681. The fluctuation rate of Nusselt number due to the variation of the ESHS parameter is almost three times more than that of THS parameter.

ANN modelling and experimental investigation on effective thermal conductivity of ethylene glycol:water nanofluids

In this study, ethylene glycol (EG)–water (35:65 %v)-based nanofluids have been prepared to study enhancement in thermal conductivity. Nanofluids containing nanoparticles of materials CuO, Al2O3 and TiO2 of different mass concentrations from 0.2 to 2% were prepared using ultrasonication. Thermal conductivity measurement was carried using KD2 Pro thermal properties analyser in the temperature range of 30–60 °C. The study investigated the effect of concentration of nanoparticles, temperature and nanoparticle material on effective thermal conductivity of nanofluids. The results showed a significant improvement in effective thermal conductivity due to the addition of nanoparticles to the base fluid. Correlations were developed for predicting the effective thermal conductivity considering each material separately, and a generalized correlation considering the three materials. Subsequently, ANN modelling was carried out for predicting the effective thermal conductivity of nanofluids and compared with developed correlations. The modelling work carried out in this study is more generalized as literature results were considered in addition to the results from the present study. ANN modelling predicts the effective thermal conductivity better than the proposed correlations.

Effects of sonication duration and nanoparticles concentration on thermal conductivity of silica-ethylene glycol nanofluid under different temperatures: An experimental study

In this study, the effects of SiO2 nanoparticles presence on the thermal conductivity of ethylene glycol were examined. Samples containing nanoparticles with mass fractions of 0.005, 0.01, 0.05, 0.1, 0.5, 1 and 5% were prepared by applying two-step method. The nanofluid thermal conductivity at temperatures of 5, 25, 45 and 65°C was measured by KD2. It was found that at the higher temperature, adding nanoparticles has a greater effect on improving thermal conductivity. Based on results, as the nanoparticles mass fraction increased, the potential of nanoparticles to improve thermal conductivity reduced. Experimental measurements showed that thermal conductivity improved up to 28.34%. As the sonication time increases, the thermal conductivity improves. The effect of sonication time on thermal conductivity depends on the temperature and nanoparticle mass fraction. It was concluded that the effect of sonication time intensifies with increasing temperature and mass fraction. In the best condition, sonication time can improve the thermal conductivity up to 4.96%.

Enhancement of thermal conductivity of Cu-Cr dispersed nanofluids according to multiscale modeling

Nanofluids are the suspension of nanoparticles (<100 nm) in conventional heat transfer fluids like water, ethylene glycol, etc. They often show enhanced thermal conductivity as compared to the base fluids. In the present work, thermal conductivity enhancement of Cu-Cr dispersed nanofluids is being studied by experiments as well as modeling. The modeling of the nanofluid is based on the mechanism that evenly dispersed nanoparticles within a nanofluid undergo Brownian motion. The heat pickup by the nanoparticles from the heat source during collision was estimated from MD simulation. This has been coupled with stochastic stimulation to estimate thermal conductivity enhancement. In experiments, the Cu-1%Cr and Cu-3%Cr nanoparticles have been synthesized by mechanical alloying. The measured quantity of synthesized nanoparticles has been dispersed in ethylene glycol using programmed ultrasonication. Different surfactants like sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB) and oleic acid have been used to prepare nanofluids. The effect of particle loading and surfactant concentration on stability of ethylene glycol based nanofluids has been visually studied by TEM and Zeta potential analysis. In the best cases of Cu-Cr dispersed nanofluids was stable for 5 days from the time of synthesis. The thermal conductivity of ethylene glycol based Cu-Cr nanofluid has been measured by transient hot wire method using Flucon LAMBDA equipment.

A comparative analysis of shape factor and thermophysical properties of electrically conducting nanofluids [formula omitted] and [formula omitted] towards stretching cylinder

In the present article focus has been on examining the effect of the induced magnetic field on stagnation point flow together with heat transfer of nanofluid. Keeping the industrial prospects of this study, ethylene glycol has been taken as the base fluid whose ability to conduct heat has been examined numerically in presence of nano-scaled particles of copper and titanium dioxide. The industrial fluid flow is considered towards a stretching cylinder. Cylindrical coordinates have been opted to for mathematical formulation of governing nonlinear system which has been simplified by means of similarity analysis. The computational procedure of shooting technique has been opted to tackle the transformed system. Results for velocity and magnetic field along with temperature distribution are computed by keeping the iterative error less than 6 decimals. Influence of emerging prominent parameters are discussed and displayed through graphs. Moreover, bar charts have been plotted for the skin friction coefficient and Nusselt number. The novel findings of current investigation include: the enhancement in ethylene glycol thermal conductivity because of titanium dioxide and copper nanoparticles. The improvement is significantly dominant in presence of titanium dioxide. It is also noted that the heat transfer mechanism notably relay upon nanoparticles shape and size. Moreover, blade-shaped copper particles contribute to the highest temperature and blade-shaped particles lead to maximum surface heat flux. Furthermore, the magnitude of skin friction is reported to be higher for copper nanoparticles and lowest in presence of titanium dioxide.

An experimental study on the thermal conductivity of cerium oxide/ethylene glycol nanofluid: developing a new correlation

The main aim of this experimental study is to examine the effect of cerium oxide nanoparticles on the thermal conductivity of ethylene glycol. In this way, cerium oxide nanoparticles with the particle diameter of 10–30 nm have been used for making nanofluid samples. The samples were made in volume concentration range of 0.25–2.5% using a two-step method. Visual observation of nanofluid samples showed that they have acceptable stability. Transient hot wire method was used for measuring the thermal conductivity of the samples. Measurements were done for all samples at temperatures ranging from 25to 50°C. Measurements showed that the thermal conductivity of nanofluid enhanced with increasing temperature and solid volume fraction. The results also showed that the thermal conductivity of ethylene glycol could enhance by about 22% when the nanoparticles volume fraction reaches 2.5%. This enhancement occurred at 50°C. Finally, a mathematical correlation was presented to predict the thermal conductivity ratio of CeO2/EG using curve-fitting. This correlation, a two-variable function of temperature and volume fraction, showed a linear relationship between thermal conductivity ratio and these variables.

Measurement and modeling of thermal conductivity of graphene nanoplatelet water and ethylene glycol base nanofluids

Three graphene nanoplatelet (GNP) nanofluids with different base fluids, viz. ethylene glycol (EG), deionized water (DW), and EG/DW (1:1) were prepared and characterized. The stability of GNP nanofluid was analyzed. Thermal conductivity was tested over the temperature range −20 °C to 50 °C. A new model is proposed for the effective thermal conductivity of the GNP nanofluid considering Brownian motion, length, thickness, average flatness ratio and interfacial thermal resistance of GNP, and it was compared with Maxwell, H-C and Chu models. The maximum thermal conductivity enhancement of EG, EG/DW (1:1) and DW based nanofluid is 4.6%, 18% and 6.8% respectively. Interestingly, the thermal conductivity of EG based GNP nanofluids does not show appreciable enhancement. The thermal conductivity enhancement of EG/DW (1:1) GNP nanofluid is greater than that of pure EG GNP nanofluid. In particular, the enhancement ratio at subzero temperature is larger than that at higher temperatures. The new model and Chu model are in agreement with the experimental data, and the new model is more rational for the GNP nanofluids. The new model shows that the influence of Brownian motion of GNP on thermal conductivity is significant at higher temperatures, higher concentration and smaller nanoparticles.

Experimental evaluation, new correlation proposing and ANN modeling of thermal properties of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles for internal combustion engines applications

Thermal conductivity of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles was investigated experimentally at concentration of 0.045 to 1.9% and a temperature of 30–50 °C. ZnO particles (with an average diameter of 10–30 nm) and double wall carbon nanotubes (DWCNT) (internal diameter of 3–5 nm and 5–15 nm external diameter) were mix at a ratio of 90%: 10% and dispersed in ethylene glycol (EG) then its thermal conductivity was measured. The results showed that maximum relative thermal conductivity (TCR) at temperature of 50 °C and the concentration of 1.9%, equivalent to 24.9%. Economic evaluation and qualitative performance showed that nanofluids hybrid compared with ZnO and nanofluids containing MWCNT, in terms of increasing thermal conductivity (TCE) and economically, is quite effective. A new correlation to predict TCR in terms of concentration of nanoparticles and the temperature was proposed. This correlation has a coefficient of determination (R-squared) and the maximum error of 0.9826 and 2.9%, respectively. The greatest sensitivity was calculated at a maximum temperature and solid volume fraction. Based on the TCR data the artificial neural network (ANN) was developed. The best case ANN containing two hidden layer and 3 neurons in each layer was obtained. This ANN has an R-squared and MSE and was equal to 0.9966% AARD and 1.3127e-05 and 0.0489, respectively. The comparison between experimetnal data, correlation and ANN outputs shows the accuracy and capability of ANN in modeling the TCR data.

ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer

In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71% solid volume fraction and temperatures from 30 to 50 °C. SiO2 and DWCNT’s nanoparticles dispersed in EG as base fluid, and its thermal conductivity was measured. The thermal conductivity was obtained 38% more than ethylene glycol thermal conductivity at some temperatures. A new correlation (R 2 = 0.9925) was proposed to predict experimental thermal conductivity ratio as a function of volume concentration and temperature. Also an artificial neural network was designed for thermal conductivity ratio data predicting. The best artificial neural network topology has two hidden layers with five neurons in each layer. Comparing the experimental thermal conductivity ratio with artificial neural network outputs and the correlation shows the high capacity and accuracy of artificial neural network in thermal conductivity ratio data predicting.

New experimental correlation for the thermal conductivity of ethylene glycol containing Al2O3–Cu hybrid nanoparticles

In the present study, the thermal conductivity of the Al2O3–Cu/EG has been experimentally investigated. For this purpose, a mixture of Al2O3 and Cu nanoparticles with a ratio of 50:50 was dispersed in ethylene glycol at different concentrations (0.125, 0.25, 0.5, 1.0, 1.5 and 2.0) and temperatures (25–50 °C). The two-step method is used for dispersing nanoparticles in the base fluid. Results show that the thermal conductivity of nanofluid is more than the base fluid, and it depends on volume concentration and temperature. Experimental results have been compared with some of the most famous models, and it has been found that these models are unable to predict the thermal conductivity of investigated nanofluid. Finally, based on the experimental data, a correlation is presented as a function of the temperature and volume fraction of nanoparticles.

Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications

In the present study, the thermal conductivity of SiO2-MWCNT/EG hybrid nanofluid has been investigated experimentally at solid volume fraction range from 0.025 to 0.86% and temperatures range from 30 to 50 °C. SiO2 particles and multi wall carbon nanotubes (MWCNTs) dispersed with the ratio of 70:30% by mass in ethylene glycol (EG) as the base fluid. The thermal conductivity ratio of mentioned hybrid nanofluid increased to 20.1% more than EG thermal conductivity at 50 °C and the solid volume fraction of 0.86%. Also in the present study, a new correlation was proposed to predict experimental TCR (thermal conductivity ratio) based on the solid volume fraction and the temperature. The R-squared for the proposed correlation is equal to 0.9864. The sensitivity of nanofluid’s thermal conductivity was increased with temperature and solid volume fraction increasing. Also, an ANN was designed for TCR data modeling and forecasting. The most optimal topology was an ANN contains two hidden layers and four neurons in each hidden layer. The R-squared, MSE, and AARD for proposed ANN are equal to 0.9989, 6.8344e−06, and 0.0105, respectively. The results indicated that the neural network is stronger than the correlation in the estimating and predicting experimental thermal conductivity ratio.