Synthesis of new dihybrid nanofluid of TiO2/MWCNT in water-ethylene glycol to improve mixture thermal performance: preparation, characterization, and a novel correlation via ANN based on orthogonal distance regression algorithm

Nanofluid refers to the mixture of fluid and solid nanoparticles. If this mixture contains more than one NP or fluid, it is called hybrid nanofluid; further, if HN contains more than one NP and also more than one fluid, it is called dihybrid nanofluid. In this research, first, titanium dioxide NP was dispersed in the water-ethylene glycol basefluid and formed an HN. Then, thermal conductivity of HN was measured. After that, MWCNT NP was added to the HN and formed a DHN. Further, TC of DHN measured. Both HN and DHN TCs were compared, and the results revealed that by adding MWCNT, thermal conductivity enhanced about 30.83% (from 25.65% of HN to 56.48% of DHN). On the other hand, to analyze the phase structure, and to observe the microstructure, X-ray diffraction analysis, energy-dispersive X-ray analysis, and field emission scanning electron microscopy were examined. The measured TC for both samples was at volume fractions up to 1.0% and temperatures up to 50 degrees C. After an experimental study, two novel correlations were calculated by the curve-fitting method for HN and DHN, individually. In the end, to predict the other Vf and temperature, an artificial neural network has been modeled for both HN and DHN.

Automated summary

The study focused on the thermal conductivity enhancement of hybrid nanofluid (HN) and dihybrid nanofluid (DHN) by dispersing titanium dioxide nanoparticles and multi-walled carbon nanotubes. Experimental results showed a 30.83% increase in thermal conductivity when MWCNT was added, compared to HN. X-ray diffraction analysis and microscopy were used to analyze the phase and microstructure. Novel correlations were calculated for HN and DHN, and an artificial neural network was modeled for predicting thermal behavior at various volume fractions and temperatures.