Experimental and machine learning insights on heat transfer and friction factor analysis of novel hybrid nanofluids subjected to constant heat flux at various mixture ratios

This study explores the combined effects of aluminum oxide (Al2Os)/graphene oxide (GO) hybrid nanofluids in 50:50 and 80:20 ratios, offering a notable improvement over conventional Al2Os or GO nanofluids. It delivers a thorough comparison of thermophysical properties such as thermal conductivity and viscosity and heat transfer performance across water, Al2Os nanofluids, and the Al2Os/GO hybrids. Nanofluids at 0.1-0.5 % volume concentrations were tested in a horizontal circular pipe under constant heat flux and turbulent flow with an inlet temperature of 60 degrees C. The maximum Nu enhancements of 64, 56 and 41 % were noted for Al2Os/GO (50:50), Al2Os/GO (80:20), and Al2Os nanofluids, respectively at 0.5 vol%, compared to water. The maximum pressure drop of Al2O3/GO (50:50) nanofluid is 5.64 and 8.3 % greater than that of Al2O3/GO (80:20) and Al2O3 nanofluid, respectively at 0.5 vol%. The peak thermal performance index of 1.56, 1.48, and 1.33 is observed for Al2Os/GO (50:50), Al2Os/GO (80:20), and Al2Os nanofluids. The integration of a multi-layer perceptron artificial neural network further enhances accuracy in predicting thermal performance, surpassing the precision of conventional empirical models. The adopted model showed excellent predictive accuracy, with correlation coefficients of 0.98493 in training, 0.9837 in validation, and 0.98698 in testing.