Multi-layer neural network computation with numerical simulation of catalysis-induced 3D tetra-hybrid nanofluid flow over oil rig solar panel sheets under magneto-radiative conditions

The current research examines the integrated influence of thermal radiation and heat transfer on incompressible three-dimensional boundary layer flow of tetra-hybrid nanofluids in the presence of a transverse magnetic field. The underlying non-linear partial differential equations are formulated to reflect the simultaneous heat transfer and flow dynamics associated with radiative phenomena, magnetic effects, and nanoparticle interactions. The Levenberg–Marquardt optimization methodology serves to train a Multilayer Artificial Neural Network (MANN) that offers excellent precision along with successful convergence leading to consistent outcomes. An Adams-Bashforth based computational scheme is further employed for validation, establishing a standard for comparison. Based on present investigation, Tetra-hybrid nanoparticles considerably boost thermal conductivity and improve heat transfer performance in comparison to conventional nanofluids. The applied magnetic field regulates flow dynamics and controls the thickness of the boundary layer. It further affects velocity gradients while radiation has an immense effect on the temperature field. The reliability of the proposed hybrid model has been established by a good fit between ANN predictions and numerical outcomes. The study integrates artificial intelligence and numerical methods to provide superior thermal management and energy utilization that combine tetra-hybrid nanofluids with magnetic and radiative effects in an innovative manner.