AI-driven analysis of peristaltic transport of Bingham fluids in curved channel with implications in complex structural fluid dynamics

Abstract

This study explores the development of empirical relationships for the critical transport performance parameters, pressure rise and entropy generation in peristaltic flow of Bingham fluid through curved channel. The aim is to study structural fluid dynamics to evaluate the interaction between fluid transport and channel geometry by considering effects of curved channel structure on pressure distribution, and heat losses and hence improving efficiency. To do so a consistent correlations of input parameters like curvature, Bingham number, and Brinkman number and the corresponding output responses is developed using a combination of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Numerical solutions of the governing equations are obtained using MATLAB’s bvp4c solver, ensuring precise modeling of the flow dynamics. Optimiality is ensured by parameter sensitivity analysis and residual assessments reveal that the Bingham number has a significant impact on pressure rise, while the curvature parameter plays a pivotal role in entropy generation. Although the Brinkman number has minimal effect on pressure rise, its influence on entropy generation exhibits a complex, parameter-dependent behavior. The developed models are rigorously validated, showing strong predictive accuracy with low error margins and high correlation coefficients across training, testing, and validation phases. The findings of this research offer critical insights into optimizing peristaltic flow in practical, non-Newtonian fluid systems, contributing to the advancement of fluid management technologies and systems efficiency.