Parametric analysis of MHD flow of nanofluid in stretching sheet under chemical sensitivity and thermal radiation

AbstractThis study examines the impact that entropy production has on electric magnetohydrodynamics (MHD) hybrid nanofluid (Al2O3–SiC) flows due to the effect of thermal radiation and chemical reaction on permeable stretching sheets. A key element of this study is that to increase thermal conductivity and improve convective heat transfer, the field of electricity is used in the MHD model with entropy production, and the solution technique utilizing “bvp4c” was presented. Through appropriate transformations and analyses of entropy generation, the constituent equations have been turned into strong, nonlinear, ordinary differential equations; the entropy generated rate for a system is crucial to optimize energy in the system to work efficiently. Subsequently, these transformed equations are solved by the “bvp4c” package in MATLAB. The thermal transfer system contains velocity, temperature, and concentration profiles with novel parameters included such as the unsteady parameter, the Grashof number, the mass Grashof number, Prandtl number, Brownian motion parameter, the Schmidt number, the thermophoresis parameter, the porosity parameter, the magnetic field parameter, the electric field parameter, the Eckert number, the Brinkman number, the radiation parameter, and chemical reaction. In addition, the study analyses the prediction accuracy of thermal conductivity enhancement using an artificial neural network (ANN) with data from the values of the numerical solution. This analysis is performed with the help of MATLAB. The result of this research concludes that the decrease in the magnetic field and porosity parameters stimulate a rise in the velocity of nanofluid; the parameters of the magnetic field, electric field, and thermal radiation increase, the temperature of the nanofluid increases, and the increase in the parameters of Schmidt number, Brownian motion, and the chemical reaction delivers a decrease in the concentration level of nanofluid. Two current methods are compared to the approach applied in this study. Finally, the ANN results in better accuracy of thermal conductivity enhancement values from the numerical values. Hence, this study concludes with the accurate thermal conductivity enhancement of the nanofluid with the impacts of a chemical reaction and thermal radiation.