Physics-informed neural network analysis of kerosene-based penta-hybrid nanofluid flow and heat transfer

Kerosene oil-based penta-hybrid nanofluids have attracted significant attention because of their improved thermal conductivity, mechanical stability, and potential use in advanced heat transfer systems. In this study, a Physics-Informed Neural Network (PINN) Analysis of Kerosene-Based Penta-Hybrid Nanofluid Flow and Heat Transfer, is performed to understand the flow pattern and heat transfer characteristics of a nanostructured fluid loaded with several types of nanoparticles. This new approach integrates the physical soundness of governing transport equations with the deep learning's ability to predict, for modeling nanofluid flow and heat transfer phenomena. The initial partial differential equations governing momentum and energy transfers are converted via appropriate similarity transformations into dimensionless ordinary differential equations. These are then used as the key ingredients (embedded alongside boundary conditions) of the loss function of Physics-Informed Neural Network to make the model's output comply with physical laws. Variations in parameters leading to changes in velocity and temperature distributions are explored, and to check the correctness and trustworthiness results are compared with classical numerical solutions and previously published data. The findings indicate that the PINN approach accurately characterizes the complex flow and heat transfer features of kerosene-based penta-hybrid nanofluids. By incorporating physics-based modeling with deep learning, reliance on extensive numerical data is diminished while excellent predictive capability is preserved. This research draws attention to PINN-based methods as promising and powerful instruments for the study of high-tech nanofluid products and direct engineering of superior heat exchangers, refrigeration systems, and thermal management devices.