Thermofluidic transport of Williamson flow in stratified medium with radiative energy and heat source aspects by machine learning paradigm

This study investigates Williamson fluid with stratification aspects through an inclined medium with radiative effects and with consideration of transversally applied magnetic field. Additionally, the study involves novel contribution of thermal generating source and chemically reactive species. Modelling is conceded by incorporating conservation laws in view of ordinary differential setup after employing similar variables. Afterwards, numerical simulations through shooting and Rk-4 procedures are executed to inspect the behavior of flow and thermosolutal distributions versus variation in key parameters. Subsequently, the collected data is evaluated by utilizing a multilayer perceptron-based ANN model. The input data for the heat flux, corresponding to different fluid model parameters, is trained by employing Levenberg-Marquardt paradigm and validated against numerical experiment results. The precision of the predicted data is assessed by calculating the mean squared error, determination coefficient and error rating scale. The magnitude of heat flux coefficient elevates up to 15 % in the existence of radiation effect, while depreciates up to 6 % in the presence of stratification effect. The implementation of ANN model depicts a mean square error value 1.36×10−3 when no heat source, which rises to 1.41×10−2 when a heat source is present. From small values of mean squared error for testing, training and validation estimated for Nusselt number ensures the performance of developed ANN network.