Ai-driven multilayer modeling of Tetra-Hybrid Casson nanofluid flow with thermal Radiation: Implications for solar energy and energy conversion

• Developed an AI-driven multilayer modeling framework integrating similarity transformations, numerical solvers, and Artificial Neural Networks (ANN) for Casson nanofluid flows with thermal radiation. • Conducted a comparative analysis of single, hybrid, ternary, and tetra-hybrid nanofluids, demonstrating that tetra-hybrid Casson nanofluids achieve the most significant enhancement in heat transfer performance. • Demonstrated that the Lorentz force suppresses velocity, while thermal radiation reduces the temperature profile, with ANN predictions accurately capturing nonlinear behaviors. • Provided detailed evaluation of skin friction and Nusselt number trends under variations of Lorentz and Prandtl numbers, confirming the superior thermal performance of tetra-hybrid nanofluids. • Suggested practical applications in solar thermal collectors, perovskite solar cells, and hybrid energy storage systems, bridging theoretical modeling with energy system applications. This paper discusses a three-dimensional flow-based study and comparison of single to tetra-hybrid Casson nanofluids behavior via an Artificial Intelligence (AI) based analysis and comparative simulation, which considers the effects of thermal radiation on a stretching sheet. The four nanofluids are loaded with four different kinds of nanoparticles, namely Zirconium dioxide (ZrO 2 ), Dry lubricant (MoS 2 ), Nuclear fuel (UO 2 ), and Multi-walled Carbon Nanotubes (MWCNTs), and selected according to the criteria of the applicability of the solar energy and energy conversion systems. By using similarity transformations, a set of nonlinear Newtonian ordinary differential equations (ODEs) is obtained by the governing partial differential equations (PDEs). An Artificial Neural Network (ANN) model is trained through a multi-layered Python-based model that allows using innovative activation functions and Adaptive Moment Estimation (Adam) optimizer to approximate the complex nonlinear models. The trained ML model is additionally set to forecast the features of the flows and evaluate the criteria of the contribution of major parameters. It gives a graphical explanation of network architecture, training, and validation. The findings reveal a lag in flow velocity as the Lorentz force, ratio of velocity, and Casson fluid features increase. Conversely, the temperature profile is enhanced by the more intense sources of heating and diminished by the increased absorptions of radiation. Also, the skin friction and Nusselt behavior are compared with Lorentz and Prandtl numbers, which could be applied in optimizing perovskite solar cells, and integrated energy storage technologies. Categorically, this experiment does not only compare single, hybrid, ternary, and tetra-hybrid Casson nanofluids, but also points out the progressive thermal and flow improvements realized with a combination of nanoparticles. Integration of ANN offers nonlinear fluid prediction tool that has fewer computational costs but is still accurate. The results prove the potential of tetra-hybrid nanofluids in the development of solar thermal collectors, perovskite-based solar energy systems and next-generation energy storage technologies and fill the gap between the conceptualization and practical implementation of the results.