Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al<Subscript>2</Subscript>O<Subscript>3</Subscript> nanoparticles by correlation and ANN methods using experimental data

In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT–Al₂O₃/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1–2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 °C. The results indicate that enhancement of nanoparticles’ thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 °C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.     

Prediction of rheological behavior of MWCNTs–SiO2/EG–water non-Newtonian hybrid nanofluid by designing new correlations and optimal artificial neural networks

In this paper, at the first, new correlations were proposed to predict the rheological behavior of MWCNTs–SiO₂/EG–water non-Newtonian hybrid nanofluid using different sets of experimental data for the viscosity, consistency and power law indices. Then, based on minimum prediction errors, two optimal artificial neural network models (ANNs) were considered to forecast the rheological behavior of the non-Newtonian hybrid nanofluid. One hundred and ninety-eight experimental data were employed for predicting viscosity (Model I). Two sets of forty-two experimental data also were considered to predict the consistency and power law indices (Model II). The data sets were divided to training and test sets which contained respectively 80 and 20% of data points. Comparisons between the correlations and ANN models showed that ANN models were much more accurate than proposed correlations. Moreover, it was found that the neural network is a powerful instrument in establishing the relationship between a large numbers of experimental data. Thus, this paper confirmed that the neural network is a reliable method for predicting the rheological behavior of non-Newtonian nanofluids in different models.

     

Thermal conductivity of a hybrid nanofluid

Journal of Thermal Analysis and Calorimetry - Hybrid nanofluid can be considered as a new generation of nanofluids. Despite the success of the researchers in the field of hybrid nanofluids, no...     

Rheological behavior of CuO/EG:W (20:80 v/v) nanofluid from a thermal perspective

Journal of Thermal Analysis and Calorimetry - This study investigates the viscosity of CuO/EG:W (20:80 v/v) nanofluid in the solid volume fraction from 0 to 1% and temperatures between 15 and...     

Proposing a nano-approach to modify viscosity behavior of SAE 5W50 as light road vehicles lubricant

In this paper, viscosity of MWCNT (%50)–TiO₂ (%50)/5W50 is investigated in temperature range of 5–55 °C, solid volume fractions of 0.05%, 0.1%, 0.25%, 0.5%, 0.75% and 1%, and shear rate range of 666.5–10,664 (s^?1). Experimental results showed non-Newtonian behavior of enriched nano-engine oil. Nano-engine oil viscosity reduction (compared to 5W50 base oil) in some specific temperatures and solid volume fractions is one of the unique and interesting results of this research. Maximum viscosity reduction (??11%) occurred in 15 °C and solid volume fraction of 0.05%, and maximum viscosity enhancement (+?17%) was observed in 25 °C and solid volume fraction of 1%. The main goal of present study is to control viscosity increase of nanofluid after adding nanoparticles to the oil. Modeling and prediction of results were achieved via RSM and ANN methods.