Artificial neural network-guided heat transfer analysis of tri-hybrid nanofluid thin film flow under realistic thermal constraints: Advanced energy applications

The growing demand for proficient thermal sources in next-generation energy advancements necessitates innovative approaches for enhancement of heat transfer. This investigation is motivated by potential of neural networks (ANN) to intelligently optimize the thermal performance of tri hybrid nanofluid (THNF) with applications of nonlinear radiative effects and realistic thermal constraints. By integrating advanced computational intelligence with complex fluid dynamics, the analysis aims to deliver a robust framework for smart thermal management in compact and high-performance systems. A blood-based tri hybrid nanofluid with suspension of copper oxide (CuO), titanium dioxide (TiO 2 ) and silicon dioxide (SiO 2 ) subject to thin film flow has been considered. Heat transfer analysis is entertained by entertaining the additional heat source. The computational accuracy and efficiency of hybrid nanofluid is optimized with applications of artificial neural network (ANN). The analysis further emphasizes the synergistic thermal consequences for THNF nanoparticles, which surpasses viscous and hybrid nanofluid. More realistic convective thermal constraints are followed for robust prediction and enhancement of thermal phenomenon. The numerical data is calculated via shooting technique. It has been concluded that the integration of ANN suggests a more reliable framework for design optimization and advanced thermal technologies. The performed analysis bridges an advancement in thermal engineering with intelligent computational framework, suggesting promising solutions for next generation energy resources and small thermal technologies.