Analysis of Dynamical Assisting and Opposing Flow Characteristics of Darcy Surface-Filled Ternary Nanoparticles and Fourier Flux: Artificial Neural Network and Levenberg Method
Maddina Dinesh Kumar, C. S. K. Raju, H. Ashraf, Nehad Ali Shah, Amjad Ali, Abdelaziz Mennouni, Noor Muhammad, Abderrahim Wakif, Katta Ramesh, Hanumesh Vaidya, Tosin Oreyeni, B. C. Prasanna Kumara
Abstract
In this study, the effects of suction and buoyancy are used to analyze the Ternary hybrid nanofluid flow on a stretching sheet through a porous media. Ternary hybrid nanofluid is (Carbon nanotubes) CNT[Formula: see text]Graphene[Formula: see text] with base fluid as Water. Case-1 Buoyancy Assisting flow and Case-2 Buoyancy Opposing flow. Hybrid nanofluids have been used to speed up the heat transfer process. Nonlinear partial differential equations (PDEs) have been converted to ordinary differential equations (ODEs) using Lie group transformations. The ODE45, an algorithmic approach, has been using the aid of this built-in solver, and the resulting Ordinary differential equations were resolved. The general relationship between temperature, velocity, heat transfer rate, and shear stress on a stretchy surface is shown for a range of values of the significant factors. The temperature profiles have been rising with the impact of [Formula: see text]. Using streamlines to examine the flow pattern of a fluid and a method of machine learning, in terms of modern language, an artificial neural network (ANN) made up of artificial neurons or nodes is known as a neural network. A neural network is a network or circuit of genetic neurons.