Machine learning forecasting of electroosmotic MHD flow of a buoyancy-driven ternary nanofluid in converging and diverging channels.

Artificial neural networks (ANNs) are utilized to study the buoyancy-driven electroosmotic flow conveying Blood, Gold (Au), Copper (Cu), and Multi-Walled Carbon Nanotubes in convergent ciliated tube (CCT) and divergent ciliated tube (DCT) micro-vessel systems. The research includes significant physical mechanisms, such as magnetohydrodynamic, heat generation, Jeffrey fluid model, Non-dimensional electric potential, and mixed convection resulting from buoyancy force influence. The Debye-Hückel approximation is used to model the electroosmotic influence using the Helmholtz-Smoluchowski velocity. The nonlinear governing equations are solved using the finite element method (FEM) to produce data sets of high quality. An ANNs, which involves the backpropagation LevenbergMarquardt scheme (ANN-BPLMS), is constructed using a large number of data points of the FEM solutions to increase prediction accuracy. These data are then utilized to test and train the artificial neural networks model and precisely forecast velocity field and thermal profile in a broad variety of controlling parameters. The intelligent computing outcomes display strong agreement with the finite element method solutions. The results show that electroosmotic effect effectively regulate flow in both CCT and (DCT) micro-vessel systems, whereas buoyancy forces dramatically improved axial velocity field and temperature profile. Furthermore, the inclusion of [Formula: see text]-[Formula: see text]-[Formula: see text]/[Formula: see text] ternary hybrid nanofluid significantly improves thermal heat transfer rate compared to [Formula: see text]-[Formula: see text]/[Formula: see text] hybrid nanofluid.