Predictive Modeling and Sensitivity Analysis of Thermodynamic Irreversibilities in Peristaltic Transport of Bingham Plastic Fluid in Porous Media Using Artificial Neural Networks and Response Surface Methodology

ABSTRACT This study focused on the optimal analysis of thermodynamic irreversibilities in the peristaltic transport of a Bingham plastic fluid in an asymmetric porous channel. The governing differential equations were solved numerically using MATLAB's boundary value problem fourth‐order method (bvp4c function) to estimate the pressure rise per wavelength () and entropy generation () under varying parameter conditions. Models for and were developed using Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) to provide comprehensive insights into the system. The coefficient of determination ( R 2 ) value for the RSM model of was 99.98%, whereas that for was 99.76%. The ANN models demonstrated high precision, with error margins ranging from 10 −3 to zero for and 10 −4 to zero for . Sensitivity analysis revealed that was strongly influenced by the permeability parameter , whereas was more sensitive to low values of and intermediate to maximum values of Brinkman number . Model validation using residual plots, normal probability plots, and observation order comparisons confirmed the excellent agreement between the observed and predicted values. Both the RSM and ANN models achieved regression values near unity, demonstrating their robustness in modeling parameter interactions and system responses. This study establishes a reliable framework for analyzing peristaltic transport in complex fluid systems and provides valuable insights for optimization.