Deep learning-based modeling of hybrid nanofluid heat transfer for enhanced solar thermal efficiency

This paper investigates convective flow of electrically conducting hybrid nanofluids (TiO2-CuO/water) on an elastic flat plate within the magnetic field in order to increase the efficiency of solar energy revolving systems. The governing partial differential equations are simplified by using similarity transformations and are further solved using an ordinary differential equations solver built using a Python deep learning framework. A supervised model with rectified linear unit activation and Adam optimizer is found to have high accuracy mean square error approximately 10−5 in fitting the nonlinear dynamics. The parametric analysis shows that higher magnetic fields inhibit the velocity but thicken the thermal and concentration boundary layers, and Prandtl and Schmidt numbers represent the diffusive modes of heat and mass. Eckert boosts temperature through viscous heating, and Brownian motion promotes the diffusion and heat transfer of the nanoparticles. Outcomes show that hybrid nanofluids have outstanding thermal transport performance, placing them in the spotlight as a possible solution for solar thermal applications in the future. The paper also presents some problems and dilemmas of nanoparticle stability, radiative impacts, and practical integration of systems that open possibilities in theory and the feasibility arena.