Title

Thermal Conductivity Enhancement via Synthesis Produces a New Hybrid Mixture Composed of Copper Oxide and Multi-walled Carbon Nanotube Dispersed in Water: Experimental Characterization and Artificial Neural Network Modeling

RIS ID

144000

Publication Details

Karimipour, A., Malekahmadi, O., Karimipour, A., Shahgholi, M. & Li, Z. (2020). Thermal Conductivity Enhancement via Synthesis Produces a New Hybrid Mixture Composed of Copper Oxide and Multi-walled Carbon Nanotube Dispersed in Water: Experimental Characterization and Artificial Neural Network Modeling. International Journal of Thermophysics, 41 (8),

Abstract

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Nanofluid is a solid–fluid mixture. By using one solid nanoparticle or one fluid, mono-nanofluid (MN) forms, and by using two solid nanoparticles (NPs) or two fluids, hybrid-nanofluid (HN) forms. For this study, for MN, copper oxide (CuO) and for HN, two solids, which are CuO and multi-walled carbon nanotube (MWCNT) were dispersed in base fluid which is water. After nanofluid preparation, thermal conductivity was measured, and the achievements were numerically modeled. After that, XRD–EDX were performed for the phase-structural analysis. Then, FESEM was examined for NPs-microstructural study. Thermal conductivity (TC) of MN and HN were investigated at 0.2 % to 1.0 % volume fractions (Vf) in 25 °C to 50 °C temperature (T) ranges. Thermal conductivity enhancements of 19.16 % and 37.05 % were seen at the utmost Vf and T for mono-nanofluid and hybrid-nanofluid, respectively. New correlations have been presented with R2 = 0.9, and also Artificial Neural Network (ANN) has been done with R2 = 0.999. For the presented correlation, 0.86 %, and 0.51 % deviations, and for the trained model, 0.41 % and 0.51 % deviations were estimated for mono-nanofluid and hybrid-nanofluid, respectively. As a final result, by adding MWCNT to CuO–H2O mixture, thermal conductivity is raised by 17.89 %, and the hybrid-nanofluid has acceptable heat-transfer capability.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s10765-020-02702-y