Artificial neural network of thermal Buoyancy and Fourier flux impact on suction/injection-based Darcy medium surface filled with hybrid and ternary nanoparticles

Abstract

This study's main objective is to analyze the heat and velocity transfer rate with various effects such as the suction and buoyancy effects on a stretching sheet with ternary hybrid nanofluid flow via a porous medium. Ternary hybrid nanofluid is Case-1 Ag-TiO2 with base fluid H2O and Case-2 CNT + Graphene + Al2O3 with base fluid H2O. Hybrid nanofluids have been used to speed up the heat transfer process, following Lie group transformations are used to transform non-linear Partial Differential Equations (PDEs) into Ordinary Differential Equations (ODEs). The boundary value problem solver was used in conjunction with the ODE45 numerical approach to solve the resulting Ordinary differential equations. On a stretchy surface, the overall relationship between temperature, velocity, shear stress, and heat transfer rate are shown for a range of values of the significant factors. The temperature profiles have been rising with the impact of Da and fw. Utilizing streamlines and a machine learning technique to examine the flow pattern of a fluid. Artificial neurons or nodes make up a neural network, which is known as a neural network in the sense that the term is used today. A network or circuit of biological neurons is referred to as a neural network.