Evaluation of Physicothermal Properties of Solar Thermic Fluids Dispersed with Multiwalled Carbon Nanotubes and Prediction of Data Using Artificial Neural Networks

A review of multiwalled carbon nanotubes as solar thermic fluids and their thermophysical properties is done in this article. The basic fluids were ethylene glycol and water in ratios of 100 : 0, 90 : 10, and 80 : 20. To investigate how surface modification impacts thermophysical properties, three base fluids were combined with surfactant-assisted MWCNTs and oxidized MWCNTs in weight fractions of 0.125, 0.25, and 0.5 percent, respectively. It takes two months to check whether the dispersion stays constant. Thermal conductivity and viscosity measurement were done using heated discs and Anton Paar viscometers. Using oxidized MWCNTs to disperse, the base fluids increased thermal conductivity by 15% to 24%. Surfactant-assisted MWCNTs in nanofluids perform worse than oxidized MWCNTs. The dynamic viscosity of nanofluids is higher than that of basic fluids between 50 and 70 degrees C. During a mathematical computation, all of the MWCNT weight fractions and ethylene glycol volume percentages are included. The correlation may be a good fit for the experimental data within limits. The characteristics are forecasted using feed-forward backpropagation. In this research, buried layer neurons and factors are examined.