Computing neural network to analyze heat and mass transfer in the flow of nanofluid between two disks

Abstract

The model of copper nanoparticles which are suspended in the engine oil (EO) and rotated between two stretchable disks is analyzed. The flow, heat, and mass transmission phenomena of nanofluid with magnetohydrodynamics (MHD) have a vital role in many industries. A magnetic field in the vertical direction is imposed in the flow of the nanofluid and Dufour and Soret (DS) effects are discussed in the equations of energy and concentration. The main equations of motion and energy are converted into a set of nonlinear ordinary differential equations (ODEs) after applying the similarity conversions. A popular semi-analytical approach, namely the differential transform method (DTM) is used to get the solution of velocity, temperature, and concentration profiles. The effect of the various parameters on all profiles is graphically presented and explained. The present data of shear stress, the Nusselt, and the Sherwood numbers calculated by DTM are matched and verified by numerical method data and literature for the novelty of the work. The strength of the work is to analyze the validation, training, and testing by using Levenberg-Marquardt artificial neural network (ANN). This ANN is verified by mean square error, error histogram, and regression analysis.