Statistical Optimization and Predictive Analysis of Dissipative MHD Cu–H₂O Nanofluid Flow with Heat and Mass Transfer

Abstract

A numerical analysis is carried out to analyze the hydrodynamics of the nanofluid flow over the semi-infinite porous stretching sheet using the base fluid as water with copper nanoparticles and an external magnetic field. The nonlinear PDEs that govern the problem include the Forchheimer and Ohmic effects to simulate the porous medium. The PDEs are transformed to ODEs using similarity transformations and solved using the Runge-Kutta-Fehlberg method of order 45. The study also includes the effects of the Eckert number, magnetic parameter, Schmidt number, Soret number, and chemical reaction parameter to simulate the transport mechanisms that occur in the problem and can be applicable in real-world scenarios. These parameters can be applicable in the cooling of microchannels, magnetic drug delivery in biomedical applications, combustion and propulsion systems, separation and sorting of DNA molecules and other biological molecules, and environmental cleanup and pollutant removal. The results show that the Forchheimer and Ohmic effects increase the drag force and the chemical reactions saturate when the value of n is greater than 4. To clearly visualize the results, three-dimensional plots of the x and y velocity profiles were plotted using Mathematica, and correlation analysis was carried out to obtain the correlation values for the skin friction coefficient, heat transfer coefficient, and mass transfer coefficient. The velocity fields were also predicted using the regression analysis method with over 99% accuracy and correlation coefficients were obtained. Sensitivity analysis was carried out and the combinational effects of different parameters were studied with the help of Taguchi method and Response Surface Methodology (RSM).