Experimental study on the thermal properties of Al₂O₃-CuO/water hybrid nanofluids: Development of an artificial intelligence model

In this work, Al₂O₃ and CuO nanoparticles were synthesized by a novel sol-gel method. Then, water-based Al₂O₃ and Al₂O₃-CuO (50:50) nanofluids were produced by the two-step method. The viscosity and thermal conductivity of nanofluids were determined for the concentration and temperature range of 0-1.0 vol.% and 30-60°C, respectively. Sodium dodecylbenzene sulfonate surfactant was used to enhance the nanofluid stability. Field emission scanning electron microscopy, transmission electron microscopy, and x-ray diffraction techniques were used for the morphological characterization of the nanoparticles. The pH and zeta potential were used to determine the stability of the nanofluid. The outcomes show that the maximum augmentation in thermal conductivity and viscosity of hybrid nanofluid is 14.6 and 6.5% higher than Al₂O₃ nanofluid for 1.0 vol.% at 60 and 30°C, respectively. The maximum viscosity enhancement of Al₂O₃ and hybrid nanofluid is 14.9 and 21.4% is noticed at 30°C for a concentration of 1 vol. % relative to the base liquid. The novel equations were proposed to estimate the viscosity and thermal conductivity of hybrid nanofluid based on the experimental results with R² values of 0.99 and 0.98, respectively. A cascaded forward neural network model was developed to predict the thermal properties using experimental datasets. The performance enhancement ratio of hybrid nanofluids indicated its potential for solar energy applications.