Modelling Entropy in Magnetized Flow of Eyring-Powell Nanofluid through Nonlinear Stretching Surface with Chemical Reaction: A Finite Element Method Approach

The present paper explores the two-dimensional (2D) incompressible mixed-convection flow of magneto-hydrodynamic Eyring-Powell nanofluid through a nonlinear stretching surface in the occurrence of a chemical reaction, entropy generation, and Bejan number effects. The main focus is on the quantity of energy that is lost during any irreversible process of entropy generation. The system of entropy generation was examined with energy efficiency. The set of higher-order non-linear partial differential equations are transformed by utilizing non-dimensional parameters into a set of dimensionless ordinary differential equations. The set of ordinary differential equations are solved numerically with the help of the finite element method (FEM). The illustrative set of computational results of Eyring-Powell (E-P) flow on entropy generation, Bejan number, velocity, temperature, and concentration distributions, as well as physical quantities are influenced by several dimensionless physical parameters that are also presented graphically and in table-form and discussed in detail. It is shown that the Schemit number increases alongside an increase in temperature, but the opposite trend occurs in the Prandtl number. Bejan number and entropy generation decline with the effect of the concentration diffusion parameter, and the results are shown in graphs.

Automated summary

This paper investigates the behavior of two-dimensional incompressible mixed-convection flow of magneto-hydrodynamic Eyring-Powell nanofluid on a nonlinear stretching surface. The study reveals that entropy generation during irreversible processes leads to energy loss. By using non-dimensional parameters, a set of higher-order non-linear partial differential equations are transformed into a set of dimensionless ordinary differential equations, which are then solved numerically using the finite element method. The computational results demonstrate that the Schmidt number increases with temperature, while the Prandtl number exhibits the opposite trend. Bejan number and entropy generation decrease with the concentration diffusion parameter.