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This research aims to present the preparation, characterization and thermophysical properties of water-based microencapsulated n -eicosane PCM suspensions for thermal energy storage. n- eicosane PCMs are coated with urea–formaldehyde polymer shell. The microcapsules have been characterized by Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) analyses. The differential scanning calorimetry (DSC) is also performed for measurement of the latent heat value, melting and freezing temperatures, and specific heat of MEPCMs. Then, the thermophysical characteristics of the MEPCM suspension including the thermal conductivity k m and viscosity µ m have been measured at different particle concentration (2, 5 and 10 wt%) and different temperatures from 25 to 50 °C. New correlations are developed to predict the thermophysical characteristics of MEPCM suspensions. Furthermore, the application performance of artificial neural network for predicting of k m and µ m is evaluated, and it is found that a neural network with the optimum structure of 2-4-4-2 provides a very accurate prediction of the experimental results. [ABSTRACT FROM AUTHOR]

In this study, the rheological behavior and dynamic viscosity of 5 W30 engine oil based ZnO-MWCNT (30:70) hybrid nanofluid with various volume fractions (VFs) ranging from 0.05 to 1 vol%, temperatures in the range of 5–55 °C, and shear rates altering from 50 to 1000 rpm are experimentally evaluated. The measured viscosity values at different shear rates (SRs) and temperatures revealed that the hybrid nanofluid under study has a non-Newtonian behavior. For example, with increasing SR from 50 to 300 rpm, the maximum viscosity reduction (= 25.6%) occurs at temperature of 5 °C and VF of 1%, while with increasing SR from 700 to 1000 rpm, the minimum viscosity reduction (= 7.6%) occurs at temperature of 55 °C and VF of 0.05%. Moreover, the power law index is observed less than unity indicating pseudoplastic behavior of hybrid nanofluid under study in all VFs and temperatures. The experimental results show that the increase of the VF of nanoparticles leads to the elevated viscosity. By raising the nanofluid temperature, the viscosity of hybrid nanofluid reduces, while engine oil without additives has a high viscosity at elevated temperatures. As an example, considering constant SR of 300 rpm, the increase of temperature from 5 to 55 °C leads to the 78.4% and 85.5% viscosity reduction for the hybrid nanofluid with VF of 0.5% and 0.75%, respectively. In order to predict the experimental data, a three-variable correlation (depending on the temperature, VF of nanoparticles, and SR) and artificial neural network modeling are developed. It is concluded that an excellent agreement exists between the experimental data, correlation results, and predicted data by neural network, however, neural network modeling demonstrated its capability to estimate the viscosity of 5 W30 engine oil based ZnO-MWCNT hybrid nanofluid more precisely rather than the proposed correlation. Hence, the neural network modeling is highly recommended for prediction of viscosity of the hybrid nanofluid under study. [Display omitted] • Rheological behavior of ZnO-MWCNT (30:70)/5 W30 hybrid nanofluid is evaluated. • Effects of concentration, temperature, and shear rates on the viscosity is studied. • A three-variable correlation for viscosity estimation is developed. • Comparison of correlation results with those of available correlations is done. • A neural network modeling is developed for predicting the experimental data. [ABSTRACT FROM AUTHOR]