Specific Heat Analysis of Water and Ethylene Glycol Based rGO-ND Hybrid Nanofluids: Experimental and Artificial Neural Network Predictions

The isobaric specific heat was measured experimentally for two kind of hybrid nanofluids like water and ethylene glycol based reduced graphene oxide-nanodiamond (rGO-ND) hybrid nanofluids at different particle volume loadings of 0.2%, 0.4%, 0.6%, 0.8% and 1.0%, and in the temperature range from 293 K to 333 K, respectively. The obtained experimental specific heat data was used for the artificial neural network (ANN) algorithms of Support Vector Regression (SVR), and Levenberg-Marquardt (LM) models for the predictions. Results indicated that, the specific heat of water, and ethylene glycol-based hybrid nanofluids at 1.0% vol. of hybrid nanofluid is lowered by 1.09% and 1.10% at a temperature of 333 K, compared to their own base fluids. The SVR and LM models for the specific heat of water-based hybrid nanofluids predict accurately with a correlation coefficient of 0.99849, and 0.99957, similarly, the SVR and LM models for the specific heat of ethylene glycol-based hybrid nanofluids predict accurately with a correlation coefficient of 0.99998, and 0.99906, respectively. The obtained data was compared with other kind of nanofluids data. The polynomial regression equation was proposed for the water and ethylene glycol-based hybrid nanofluids through the SVR model.