ANN model for magnetised Casson fluid flow under the influence of thermal radiation and temperature stratification: Comparative analysis

Abstract

The article compares the performance of multi‐layer back‐propagated neural networks using the Levenberg–Marquardt (LMS) method and Bayesian Regularization Scheme (BRS) for analysing heat transfer in magnetized Casson fluid over flat and cylindrical surfaces. Factors like mixed convection, Joule heating, thermal radiation and temperature stratification are considered. Both LMS and BRS prove effective in handling complex interactions in numerical studies. Non‐linear ODE systems are solved using a shooting technique to obtain Nusselt number datasets, which are then divided for training, testing and validation purposes. The accuracy of the artificial neural network (ANN) results is confirmed by the agreement between predicted and target Nusselt numbers. Analysis of mean square error (MSE), regression fitness and error histogram further validates ANN adeptness. Furthermore, heat transfer rate is increasing for the Casson parameter, Grashof number and radiation parameter, while, it declines for stratification parameter, Prandtl and Eckert number. The study explores the impact of various parameters on velocity and temperature profiles, revealing that ANN provides accurate Nusselt number results for both flat and cylindrical surfaces. The research contributes a novel perspective to understanding ANN behaviour in heat transfer analysis, particularly for magnetized Casson fluid flow over two stretching surfaces.