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 (k(nf)) 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 k(nf) 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 (phi). Sensitivity analysis of experimental data was also performed to slope the knf changes to determine the best temperature and f for nanofluid operation. Experimental data show that nanofluids with phi= 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. (C) 2021 The Author(s). Published by Elsevier B.V.

Automated summary

This study investigated the thermal conductivity of hybrid nanofluids containing different ratios of nanoparticles in an ethylene glycol base fluid and established an artificial neural network model based on experimental data. The results showed that hybrid nanofluids with 1% nanoparticles exhibited over 40% increase in thermal conductivity, and were more cost-effective in energy management compared to mono nanofluids.