Assessment of Unsteady Shear-Thinning Hybrid Nanofluid Flow Past a Convectively Heated Vertical Cone Via Numerical and Artificial Neural Network Analysis

Abstract

This study meticulously examines the time-dependent Casson hybridized nanofluid (copper-molybdenum disulfide/kerosene) flow across a convectively heated vertical cone, considering velocity slip, thermal radiation, and heat source influences. It also considers the effects of chemical reactions, magnetic fields, and mixed convection. The model equations are converted into highly nonlinear coupled ordinary differential equations (ODE) using pertinent similarity variables. To ensure the accuracy of our results, we employ the Runge-Kutta-Fehlberg technique integrated into MATLAB to monitor the oscillation of different controlling parameters. This study also uncovers streamlined patterns for various magnetic parameter values. The key strength of this work lies in the meticulous analysis of the Levenberg-Marquardt artificial neural networks (ANNs) validation, training, and testing. Regression, fitness analysis, mean square error, and error histogram are utilized to validate this ANN. Figures are presented to illustrate the temperature and velocity profiles of the hybridized nanofluid, as well as the concentration profiles of the nanoparticles for different values of the relevant parameters. The primary velocity profile is found to increase with the unstable, magnetic, and velocity slip parameters, while the secondary velocity profiles exhibit the opposite effect. This study's significant finding, which adds to the field of fluid dynamics and nanofluid research, offers intriguing possibilities for enhancing heat and mass transport in a variety of industrial applications (cooling processes, paints, space technology, conductive coatings, medicines, bio-sensors, cosmetics, etc.).