Mixed convection effect on MHD Oldroyd-B nanofluid flow over a stretching sheet through a porous medium with viscous dissipation-chemical engineering applications

Abstract

In drug delivery systems, polymers are now necessary components, especially because they are flexible, viscous fluids that can help regulate drug release over time. Drug delivery that is targeted, sustained, and has few adverse effects is made possible by the engineering of these polymers to offer a variety of controlled release mechanisms. When it comes to viscosity and rheological characteristics, polymers can provide flexibility, which makes them perfect for use in formulations where fluid qualities are crucial for efficient drug delivery. This work's primary goal is to investigate the mixed convection effect on magnetohydrodynamic flow of Oldroyd-B nanofluid through a porous medium as a model of viscoelastic fluids while accounting for nonlinear thermal radiation, heat generation/absorption, and chemical reaction in the presence of viscous dissipation and Joule heating. The system of partial differential equations controlling the flow process was converted into a new system of ordinary differential equations using symmetric transformations and dimensionless variables. The fourth-order Runge-Kutta method with the shooting technique was then used to solve the equations numerically. A graphical analysis was performed using MATLAB to demonstrate how the main distributions under research reacted when all of the physical elements derived from the study were altered, in addition to outlining the basic concepts, physical interpretations, and potential therapeutic applications. According to certain research findings, the temperature distribution is positively impacted by the thermal radiation coefficient, heat generation and absorption coefficient, thermophoresis coefficient, and Eckert number, while the velocity distribution is negatively impacted by the magnetic field, Darcy number, and mixed convection coefficient. Meanwhile, the concentration distribution of nanoparticles is a decreasing function that is influenced by the Prandtl number, Lewis number, and Brownian motion coefficient.