Hybrid Nanoparticle Geometry Optimization for Thermal Enhancement in Solar Collectors Using Neural Network Models

This study investigates the thermal transport behavior of a time-dependent viscoelastic nanofluid moving over a widening cylindrical surface. A steady magnetic influence is introduced along the transverse direction due to photonic heating, thermal sources, or absorbers, and modified Fourier conduction. A mixture of CoFe2O4 and Fe3O4 nanoparticles are uniformly distributed in ethylene glycol to form a hybrid nanofluid. Using a suitable similarity transformation, the governing equations were reformulated into a set of nonlinear ordinary differential equations. The collocation method (CM) is employed as a discretization approach, combined with feedforward neural networks (FNNs) to enhance computational accuracy. Unsteady patterns in both fluid motion and heat distribution were identified, with the localized Nusselt coefficient influenced by relevant scaling parameters. Results are illustrated through plots and structured data formats for various nanoparticle geometries, including spherical, brick, and platelet forms. The analysis revealed that spherical nanoparticles enhance heat transfer by up to 18–22% compared with brick and platelet forms under strong unsteadiness and relaxation effects. As temporal fluctuation indicators intensify, the thermal distribution increases; however, increasing the relaxation coefficient in the heat response leads to diminished energy levels.