Impact of cubic autocatalysis and infinite shear rate characteristics in MHD Carreau fluid over radiated bi-directional sheet; ANN-based computational scheme

The implementation of advanced computational strategies play a significant role in thermal transport analysis of complex fluid flow processes, which is crucial in modern thermal management systems. Artificial neural network (ANN) computational procedures play a vital role in solving the stiff nonlinear mathematical models due to their strong capability to train and predict data efficiently. The present endeavor delves into the discussion of heat transfer mechanism in Carreau fluid over a bi-directional stretching sheet, featuring magnetohydrodynamics (MHD), infinite shear rate characteristics and exhibiting cubic autocatalysis effect. In addition, magnetic effects are added to analyze the heat transfer efficiency more effectively, while dual chemical reactions aid in convenient assessment of fluid concentration. Physical models generated partial differential equations (PDEs) are shifted into ordinary differential equations (ODEs) via introducing similarity variables. Computational analysis is made by employing a joint computational procedure i.e., bvp4c and Levenberg Marquardt neural network scheme (LM-NN). The results are displayed by different MATLAB illustrations and statistical data. The concentration field of Carreau fluid increased due to augmentation in diffusion parameter for both pseudoplasticity and dilatant region. Velocity profile intensified in both axial directions due to numeric growth in values of Weissenberg number and infinite shear rate parameter. Skin friction reduced in both axial directions with augmented values of Weissenberg number, whereas Nusselt number improves when corresponds to x-direction and shows opposite behavior for y-direction.