scholarly journals The Investigation of Effects of Temperature and Nanoparticles Volume Fraction on the Viscosity of Copper Oxide-ethylene Glycol Nanofluids

2017 ◽  
Author(s):  
Mohammad Hemmat Esfe
Keyword(s):  
Ethylene Glycol ◽  
Copper Oxide ◽  
Volume Fraction ◽  
Relative Viscosity ◽  
Experimental Results ◽  
Different Temperatures ◽  
Nanoparticles Volume Fraction ◽  
Effects Of Temperature ◽  
Nanofluid Viscosity ◽  
Experimental Values

In the present article, the effects of temperature and nanoparticles volume fraction on the viscosity of copper oxide-ethylene glycol nanofluid have been investigated experimentally. The experiments have been conducted in volume fractions of 0 to 1.5 % and temperatures from 27.5 to 50 °C. The shear stress computed by experimental values of viscosity and shear rate for volume fraction of 1% and in different temperatures show that this nanofluid has Newtonian behaviour. The experimental results reveal that in a given volume fraction when temperature increases, viscosity decreases, but relative viscosity varies. Also, in a specific temperature, nanofluid viscosity and relative viscosity increase when volume fraction increases. The maximum amount of increase in relative viscosity is 82.46% that occurs in volume fraction of 1.5% and temperature of 50 °C. Some models of computing nanofluid viscosity have been suggested. The greatest difference between the results obtained from these models and experimental results was down of 4 percent that shows that there is a very good agreement between experimental results and the results obtained from these models.

scholarly journals Heat transfer studies in compact heat exchanger using ZnO and TiO2 nanofluids in ethylene glycol/water

2018 ◽  
Vol 24 (4) ◽  
pp. 309-318
Author(s):  
Srinivasan Manikandan ◽  
Rajoo Baskar
Keyword(s):  
Heat Transfer ◽  
Nusselt Number ◽  
Ethylene Glycol ◽  
Volume Fraction ◽  
Inlet Temperature ◽  
Percentage Increase ◽  
Cold Side ◽  
Volume Fractions ◽  
Experimental Values ◽  
Tio2 Nanofluids

This paper reports an experimental study on the heat transfer characteristics of a nanofluid consisting of ZnO/water/ethylene glycol (EG) and TiO2/water/ /ethylene glycol. In this study, the base fluids of ethylene glycol (EG):water (W) with volume fractions of 30:70, 40:60, and 50:50 were prepared, and 0.2 to 1.0 volume fractions of ZnO and TiO2 nanofluids were used as a cold side fluid. The prime objective of this study is to identify the effects of nanofluid concentration and three different hot fluid inlet temperatures viz., 55, 65 and 75?C C on the heat transfer enhancement of cold side fluid. The results are compared with base fluids and the percentage increase of the Nusselt number because of nanoparticle addition is noted both experimentally and theoretically. The results showed that at the hot fluid inlet temperature of 75?C, the increase in the Nusselt number is maximum with volume concentrations of 0.6 and 0.8% for ZnO and TiO2 nanofluids, respectively. The corresponding maximum Nusselt number enhancements are about 11.5 and 21.4%, respectively, for the base fluid volume fraction of 30:70 (EG:W). There is good agreement between the results calculated from experimental values and the correlation.

Modeling of Temperature-Dependent Hardness for Pure FCC and HCP Metals

2020 ◽  
Vol 12 (02) ◽  
pp. 2050022
Author(s):  
Niandong Xu ◽  
Weiguo Li ◽  
Jianzuo Ma ◽  
Yong Deng ◽  
Haibo Kou ◽  
...  
Keyword(s):  
Theoretical Model ◽  
Melting Point ◽  
Present Model ◽  
Experimental Results ◽  
Effect Of Temperature ◽  
Temperature Dependent ◽  
Different Temperatures ◽  
Pure Metals ◽  
Experimental Values ◽  
Hardness Values

In this study, a theoretical model is developed to characterize the quantitative effect of temperature on the hardness of pure FCC and HCP metals. The model is verified by comparison with the available experimental results of Cu, Al, Zn, Mg, Be, Zr, Ni, Ir, Rh, and Ti at different temperatures. Compared with the widely quoted Westbrook model and Ito–Shishokin model which need piecewise fitting to describe experimental values, the present model merely needs two hardness values at different temperatures to predict the experimental results, reducing reliance on conducting lots of experiments. This work provides a convenient method to predict temperature-dependent hardness of pure metals, and it is worth noting that it can be applied to a wide temperature range from absolute zero to melting point.

A Study on the Effects of Temperature and Volume Fraction on Thermal Conductivity of Copper Oxide Nanofluid

2007 ◽  
Vol 7 (6) ◽  
pp. 2161-2166 ◽  
Author(s):  
Ching-Song Jwo ◽  
Ho Chang ◽  
Tun-Ping Teng ◽  
Mu-Jnug Kao ◽  
Yu-Ting Guo
Keyword(s):  
Thermal Conductivity ◽  
Copper Oxide ◽  
Volume Fraction ◽  
Effects Of Temperature

Investigation on Thermal Conductivity of Low Concentration AlN/EG Nanofluids

2013 ◽  
Vol 546 ◽  
pp. 112-116
Author(s):  
Yan Jiao Li ◽  
Chang Jiang Liu ◽  
Zhi Qing Guo ◽  
Qiu Juan Lv ◽  
Fang Xie
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Maxwell Model ◽  
Experimental Results ◽  
Effect Of Temperature ◽  
Transient Hot Wire ◽  
Hot Wire ◽  
Wire Method ◽  
Nanoparticles Volume Fraction

The thermal conductivity of AlN/EG nanofluids was investigated by transient hot-wire method. Experimental results indicated that the thermal conductivity of AlN/EG nanofluids increase nearly linear with the increase of nanoparticles volume fraction, and the results can’t be predicted by conditional Maxwell model. The effect of temperature on effective thermal conductivity of AlN/EG nanofluids was investigated. Result indicated that the thermal conductivity of AlN/EG nanofluids increased with the increase of temperature.

Effect of Volume Fraction and Temperature on Thermal Conductivity of SiO2 Nanofluids

2011 ◽  
Vol 306-307 ◽  
pp. 1178-1181 ◽  
Author(s):  
Bao Jie Zhu ◽  
Wei Lin Zhao ◽  
Dong Dong Li ◽  
Jin Kai Li
Keyword(s):  
Thermal Conductivity ◽  
Ethylene Glycol ◽  
Volume Fraction ◽  
Experimental Results ◽  
Transient Hot Wire ◽  
Hot Wire ◽  
Transient Hot Wire Method ◽  
Wire Method ◽  
Thermal Conductivities

Thermal conductivities of two kinds of nanofluids (SiO2-water and SiO2-ethylene glycol) were measured by transient hot-wire method at different volume fraction and temperature. Influences of volume fraction of particles and temperature on thermal conductivities of nanofluids were analyzed. The Experimental results show that thermal conductivities of nanofluids are higher than those of base fluids, and increase with the increase of volume fraction and temperature. When approximately 0.5% volume fraction of SiO2nanoparticles are added into water and ethylene glycol at the temperature 50°C, the thermal conductivities are enhanced 46.2% and 62.8% respectively.

Influence of TiC0.3N0.7 on viscosity of TiO2-containing slag

2018 ◽  
Vol 115 (4) ◽  
pp. 412 ◽  
Author(s):  
Renze Xu ◽  
Jianliang Zhang ◽  
Kexin Jiao ◽  
Yanxiang Liu
Keyword(s):  
Arrhenius Equation ◽  
Volume Fraction ◽  
Relative Viscosity ◽  
Apparent Volume ◽  
Pure Liquid ◽  
Two Phase ◽  
Cylinder Method ◽  
Experimental Values ◽  
Solid Liquid ◽  
The Relationship

The influence of TiC0.3N0.7 on viscosities of CaO-SiO2-MgO-Al2O3-TiO2 slags was investigated by the rotating cylinder method in this work. From the viscosity experimental results, it was found that the viscosity of the two-phase suspension system increased with increasing the volume fraction of TiC0.3N0.7 particle and decreased with increasing the rotation speed. The viscosity increased with the temperature decreasing and the relationship between viscosity and temperature could be described by the Arrhenius equation. However, temperature has little influence on the relative viscosity which is the ratio of solid–liquid mixture viscosity to pure liquid viscosity. The modified Einstein–Roscoe equation based on the present experimental values could well estimate the viscosity of TiC0.3N0.7 containing melts. The apparent volume of TiC0.3N0.7 was calculated to be 5.0–6.0 times of its real volume.

Effect of Temperature and Humidity on Measuring Ionization Current Inside Contaminated Pipes

2013 ◽  
Author(s):  
Pingchuan Li ◽  
Xianguo Tuo ◽  
Mingzhe Liu ◽  
Jun Ren ◽  
Qibiao Wang ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Long Range ◽  
Regression Method ◽  
Experimental Results ◽  
Ion Current ◽  
Effect Of Temperature ◽  
Multiple Regression Method ◽  
Temperature And Humidity ◽  
Different Temperatures ◽  
Effects Of Temperature

This paper reported the experimental results of ion current under different temperatures and relative humidity using long range alpha detector (LRAD). An approximation relation between the measuring values, temperatures and relative humidity has been obtained using the linear multiple regression method. The experimental results have shown that the measuring values decrease with the increase of temperature and humidity. The influence of humidity on results outweighs that of temperatures. And both temperature and humidity are obviously negative correlated with measured values. Further experiments will be performed to confirm the coupling effects of temperature and humidity and reported later.

scholarly journals Experimental studies on the viscosity of Fe nanoparticles dispersed in ethylene glycol and water mixture

2016 ◽  
Vol 20 (5) ◽  
pp. 1661-1670 ◽  
Author(s):  
Amir Karimi ◽  
Sadatlu Abdolahi ◽  
Mehdi Ashjaee
Keyword(s):  
Ethylene Glycol ◽  
Base Fluid ◽  
Volume Concentration ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Experimental Studies ◽  
Theoretical Models ◽  
Experimental Results ◽  
Water Mixture ◽  
Fe Nanoparticles

In this paper, experimental studies are conducted in order to measure the viscosity of Fe nanoparticles dispersed in various weight concentration (25/75%, 45/55% and 55/45%) of ethylene glycol and water (EG-water) mixture. The experimental measurements are performed at various volume concentrations up to 2% and temperature ranging from 10?C to 60?C. The experimental results disclose that the viscosity of nanofluids increases with increase in Fe particle volume fraction, and decreases with increase in temperature. Maximum enhancement in viscosity of nanofluids is 2.14 times for 55/45% EG-water based nanofluid at 2% volume concentration compared to the base fluid. Moreover, some comparisons between experimental results and theoretical models are drawn. It is also observed that the prior theoretical models do not estimate the viscosity of nanofluid accurately. Finally, a new empirical correlation is proposed to predict the viscosity of nanofluids as a function of volume concentration, temperature, and the viscosity of base fluid.

scholarly journals Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment

Nanomaterials ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1767 ◽  
Author(s):  
Mohammadhadi Shateri ◽  
Zeinab Sobhanigavgani ◽  
Azin Alinasab ◽  
Amir Varamesh ◽  
Abdolhossein Hemmati-Sarapardeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Volume Fraction ◽  
Intelligent System ◽  
Relative Viscosity ◽  
Support Vector ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Rbf Kernel ◽  
Input Variables ◽  
Nanofluid Viscosity

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.

Sign in / Sign up

Export Citation Format

Share Document