Abstract
Abstract
Coating flows of electro-conductive functional magnetic nanofluids external to both wedge and cone shapes embedded in porous media under external magnetic field modulation are investigated theoretically and numerically. Chemical reaction, thermal radiation, heat generation and activation energy effects are included in the mathematical model. Buongiorno’s two-component nanoscale model is deployed to evaluate mass diffusion phenomena of the nanoparticles under thermophoretic and Brownian motion effects. A separate species equation is included for reactive solute diffusion. By making use of suitable similarity transformation techniques, the original system of partial differential equations is reduced to a system of nonlinear ordinary differential equations with suitable wall and free stream boundary conditions. The MATLAB 'bvp4c' solver is used to numerically solve the nonlinear dimensionless ordinary differential boundary value problem. Verification with previous studies is included. The influence of key emerging parameters on velocity and temperature is displayed graphically. Response Surface Methodology (RSM) is then deployed to conduct statistical analysis and optimize the transport process for skin friction, Nusselt number (heat transfer rate), solutal Sherwood number (solute mass transfer rate) and nanoparticle Sherwood number. The results show that for both the cone and wedge geometries, flow acceleration is produced with decreasing Richardson numbers. Increasing Brownian motion parameter generates strong micro-convection currents in the base fluid, increasing the overall thermal conductivity and enhancing temperature and associated thermal boundary layer thickness. Increasing thermophoresis parameter intensifies thermal diffusion and elevates temperatures, reduces nanoparticle volume fraction and decreases nanoparticle species boundary layer thickness. An elevation in internal heat-generating and Rosseland radiation-conduction parameter both contribute significantly to the dramatic rise in temperature. With enhanced R² values of 98.56% for the cone and 99.04% for the wedge, the nanoparticle Sherwood number achieves a high level of prediction accuracy. The simulations are relevant to coating flows of biochemical engineering devices with functional nano-liquids.