The liquid film of the time-dependent cross-fluid flow over an inclined disk through an artificial neural network

Abstract

The liquid film is mainly used in coating, cooling, lubrication, thermal, and mechanical engineering. The viscosity of a cross fluid is governed by its shear rate, which lies in the class of non-Newtonian fluids. Furthermore, this model correctly distinguishes the flow region into both high and low shear rates regions. The current study concentrates on the electromagnetohydrodynamic (EMHD) liquid-film flow of the cross nanofluid over an inclined disk for heat- and mass-transfer applications. The cross-nanofluid flow of the liquid film is considered time dependent and variable in thickness. The solution of the problem is obtained through the homotopy analysis method (HAM). The HAM results are then handled through the Least Mean-Square (LMS)-based Artificial Neural Network (ANN). The proposed (LMS-ANN) models are tested for dependability, capability, validity, and reliability through regression, error analysis, and histograms. The ANN outputs are drawn in figures and tables and are discussed. Epochs 218, 96, 297, 180, 213, 184, 173, and 155 marked the best performance for the fluid model. The various parameters reveal that cross nanofluids enhance heat-transfer efficiency by promoting convective heat transfer.