Design of artificial neural network for buongiorno model with nanofluids flow through stretching sheet

The flow of nanofluids has been described by the Buongiorno model over a stretching sheet, along with the thermophysical characteristics of nanoliquids. This study examines four nanoparticles (a water-based fluid), namely aluminum oxide, sometimes referred to as alumina Al2O3, copper Cu, silver Ag and lastly titanium oxide, sometimes referred to as titania TiO2. The mathematical model for fluid flow is computed and analyzed numerically. Similarity variables were used to convert the governing partial differential equations into nonlinear ODEs. The Lobatto IIIA method in MATLAB bvp4c solves these equations numerically. The numerical outcomes include velocity, temperature, and nanoparticle concentration profiles. These findings are illustrated and thoroughly discussed. The performance of flow and heat transfer is examined in connection to thermophoresis, Brownian motion, and nanoparticle volume fraction (φ). This research has demonstrated that the stretching sheet presents unique solutions. Conversely, as the volume fraction increases, both Nb and Nt diminish, leading to an enhancement in the heat transfer rate. Furthermore, the validity of the bvp4c technique will be verified by applying the Levenberg-Marquardt technique in artificial neurons for backward propagation. Using the previously listed numerical techniques, the influence of physical quantities is analyzed using tabular and graphic representations in order to evaluate the model's significance.