This study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60 °C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60 °C and 30 °C, respectively, for a concentration of 1 vol% compared to base fluid. Similarly, graphene oxide-TiO₂ hybrid nanofluids achieved thermal conductivity and viscosity ratios exceeding 43% and 144% compared to the base fluid at similar conditions. This data highlights the significance of nanofluid concentration in influencing thermal conductivity, while temperature was found to have a more pronounced effect on viscosity. To tackle the challenge of modeling the thermophysical properties of these hybrid nanofluids, advanced machine learning models were applied. The Random Forest (RF) model outperformed others (Gradient Boosting and Decision Tree) in both the cases of thermal conductivity and viscosity with greater adaptability to handle fresh data during model testing. Further analysis using shapely additive explanations based on cooperative game theory revealed that relative to temperature, nanofluid concentration contributes more to the predictions of the thermal conductivity ratio model. However, the effect of nanofluid concentration was more dominant in the case of viscosity ratio model.

The massive demand in the electrical power sector has resulted in a large demand for reliable, cost efficient, and environmentally friendly insulation oil to reduce the dependency on mineral oil. The hybridization of nanoparticles in vegetable oil is a novel method to enhance the thermal properties of vegetable oil. This study focuses on the experimental investigation of the thermophysical properties of coconut oil, soybean oil, and palm oil-based hybrid nanofluids suspended with Al₂O₃-TiO₂ nanoparticles at a mass concentration of 0.2, 0.4, and 0.6%. The ratio between Al₂O₃ and TiO₂ nanoparticles was maintained constant at 50:50. The main purpose of the study is to evaluate the thermal conductivity, dynamic viscosity, and density of different vegetable base oils suspended with Al₂O₃-TiO₂ in the temperature range of 30 to 60 °C. The influence of temperature on the augmentation of thermophysical properties for different vegetable oil-based hybrid nanofluids is investigated experimentally. The experimental results for thermal conductivity for the three types of base fluids show that the effect of nanoparticle mass concentration in thermal conductivity enhancement is less significant for temperatures more than 50 °C. The palm oil with a 0.6% Al₂O₃-TiO₂ nanoparticle concentration exhibited the highest thermal conductivity with a 27.5% thermal conductivity enhancement relative to the base oil. The effect of nanofluid temperature on density and viscosity augmentation is more distinct compared with the impact of Al₂O₃-TiO₂ nanoparticles concentrations. Among all three types of hybrid nanofluids, palm oil based nanofluids were found to have superior thermophysical properties compared with coconut oil and soybean oil, with the highest thermal conductivity of 0.628 W/m·k and lowest viscosity of 17.772 mPa·s.

The Proton Exchange Membrane Fuel Cell (PEMFC) is a highly efficient and eco-friendly technology, making it a pivotal solution for sustainable energy systems. Effective thermal management of PEMFCs is essential, and nanofluids have emerged as superior coolants compared to conventional fluids. Less exploration in PEMFC cooling, particularly using reduced graphene oxide (rGO) suspended hybrid nanofluids, supports the present work on the thermal and rheological properties of rGO-based hybrid nanofluids. The experimental exploration involves five different mixtures of base fluid composition comprising ethylene glycol (EG) and water (W). The hybridization of Al₂O₃ and rGO nanoparticles was performed by dispersing both at four different concentrations in the 50:50 base fluid mixture. The experimental procedure involves evaluation of dispersion stability, viscosity, and thermal conductivity of hybrid nanofluids. The results showed that increasing the EG proportion reduced thermal conductivity while increasing viscosity. The maximum thermal conductivity ratio of 1.23 occurred at 80:20 W: EG for 1 vol% concentration at 60 °C, while the highest viscosity ratio of 1.48 was observed at 20:80 W: EG at 30 °C. The developed correlation for viscosity shows an 11.2% reduction in the coefficient of determination obtained for the thermal conductivity model. This study explores the application of Linear Regression (LR), Decision Tree (DT), and eXtreme Gradient Boosting (XGBoost) models for predicting thermal conductivity and viscosity using experimental datasets. The thermal conductivity model showed that XGBoost has the best predictive power, with Test R² = 0.9941, Test mean square error (MSE) = 0.0000, and Test KGE = 0.9613. XGBoost again beat other models in predicting viscosity, with Test R² = 0.9944, Test MSE = 0.0269, and Test KGE = 0.9903. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) graphs showed that the model outputs were greatly affected by the base fluid ratio (BFR), temperature, and concentration. This made the model outputs easy to understand both globally and locally. These findings provide valuable insights for designing efficient cooling solutions for PEMFCs, supporting their broader adoption in energy applications.