Numerical and ANN-based computational study of electric MHD nanofluid flow and heat transfer over a stretching sheet using Cu and Cu-Fe3O4 nanoparticles in 10W40 engine oil

The motivation for the present study arises from its potential applications in high-performance automotive cooling and lubrication systems. The utility of nanoparticle-enhanced engine oil has attracted significant attention to improve the thermal management and energy efficiency. In the current analysis, a steady two-dimensional electrically magnetohydrodynamic flow of Casson nanofluid is investigated. The nanofluid contains Cu and Fe 3 O 4 nanoparticles and these nanoparticles are dispersed in 10W40 engine oil. The flow takes place past a stretchy surface that is placed in a Darcy–Forchheimer porous medium along with convective boundary condition. The governing non-linear partial differential equations subjected to the problem are transformed into a set of nonlinear ordinary differential equations using appropriate similarity transformations. The resulting equations are then solved numerically using the bvp4c solver available in the MATLAB package. A detailed parametric study is carried out to explore the various consequences of physical parameters upon velocity and temperature fields. A feedforward Artificial Neural Network model is also employed to predict the flow behavior. The accuracy of the artificial neural network predictions is then compared with the numerical simulation results. The velocity distribution was suppressed with the increased values of Forchheimer number F r , and Casson fluid parameter β , whereas it enhances with an increase in Darcy number D a , magnetic parameter M , and couple stress parameter K . The temperature profile declines with an increase in values of Prandtl P r , heat generation/absorption parameter Q , casson parameter β . The Eckert number E c and thermal radiation parameter N r also have a significant role on the temperature distribution due to the advanced rate of energy generation, although their repercussions may be either positive or negative depending on the flow conditions. Furthermore, the predicted outcomes obtained from the artificial neural network also show a good agreement with the results from the numerical simulation.