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
Document Type
Article
Publication Date
12-24-2025
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 ((Formula presented.)) and entropy generation ((Formula presented.)) under varying parameter conditions. Models for (Formula presented.) and (Formula presented.) were developed using Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) to provide comprehensive insights into the system. The coefficient of determination (R2) value for the RSM model of (Formula presented.) was 99.98%, whereas that for (Formula presented.) was 99.76%. The ANN models demonstrated high precision, with error margins ranging from 10−3 to zero for (Formula presented.) and 10−4 to zero for (Formula presented.). Sensitivity analysis revealed that (Formula presented.) was strongly influenced by the permeability parameter (Formula presented.), whereas (Formula presented.) was more sensitive to low values of (Formula presented.) and intermediate to maximum values of Brinkman number (Formula presented.). 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.
Source Publication
Heat Transfer (ISSN 2688-4534 | eISSN 2688-4542)
Recommended Citation
Mingliang, Z., Rehman, A. U., Asghar, Z., Zeeshan, A., & Gorla, R. S. R. (2025). 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. Heat Transfer, htj.70152. https://doi.org/10.1002/htj.70152
Comments
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