Stagnation Point Nanofluid Flow in a Variable Darcy Space Subject to Thermal Convection Using Artificial Neural Network Technique

This study is focused on examining how solar radiation, thermal convection, and viscous dissipation impact the stagnation point flow in a variable porous space. The colloidal nanoparticles of TiO_2 (titanium oxide) have dispersed in different base fluids taken as H 2 O (water) and C 2 H 6 O 2 (ethylene glycol). Boosting heat transfer is made possible by the porous space in hybrid nanofluid flow. The use of porous media leads to increased surface area for heat transfer, which in turn improves heat exchange between the nanofluid and the solid matrix. A suitable set of transformable variables is employed to convert the leading equations in dimensionless form. This set of new equations has been evaluated by employing Mathematica and artificial neural network (ANN). It has been noticed in this work that, the thermal panels have augmented with escalation in volume fractions of solid nanoparticles as well as with positive and negative growth in Eckert number. The range of the parameter Rd, S, Da,Gr,ϕ is considered between 0 and 1. While Ec number range is taken out of this range for better performance.