ANN-driven insights into heat and mass transfer dynamics in chemical reactive fluids across variable-thickness surfaces

Abstract

This study investigates the heat and mass transfer dynamics in exothermic, chemically reactive fluids over variable-thickness surfaces using advanced numerical methods and artificial neural networks (ANN). The importance of understanding these processes lies in their significant industrial applications, such as in chemical reactors and heat exchangers. We transformed nonlinear partial differential equations into ordinary differential equations and used the bvp4c numerical method to generate a comprehensive data set. The ANN model, trained with the Levenberg–Marquardt algorithm, was evaluated for its accuracy in simulating complex fluid dynamics and thermosolutal transport phenomena. Our results revealed that increasing the second-grade fluid parameter ${\alpha }_1$ enhanced skin friction by 20.38%, heat transfer rate by 1.16%, and mass transfer rate by 4.06%. The ANN model demonstrated high predictive precision with a validation mean squared error of $1.145\times {10}^{-9}$. These findings highlight the effectiveness of the ANN methodology in providing precise simulations of fluid dynamics, which is crucial for optimizing industrial processes.