Empirical Modeling and Sensitivity Analysis of Thermal Irreversibility and Pressure Rise in Peristaltic Flow Using Neural Networks and Response Surface Methodology

ABSTRACT This work presents a sensitivity analysis of entropy generation in the peristaltically driven flow of a Newtonian fluid through a symmetric round conduit. The flow is governed by nonlinear coupled partial differential equations that describe the motion of the viscous fluid within the conduit. These are reduced to nonlinear equivalent ODEs by the long‐ wavelength and low Reynolds value assumptions. The solutions obtained using boundary value solver (bvp4c) routine yield several numerical findings. Empirical relation for entropy generation pressure rise per wavelength is developed using the RSM and ANN. The accuracies of the developed empirical relations are verified by calculating the , along with an ANOVA. In this case, high = 98.99% and adjusted = 99.84% values clearly show a good fit of the model. The sensitivity analysis shows that is maximum when the curvature parameter, (), Brinkman number, Grashof number, increase. The artificial neural networks achieve minimal errors, with MSE ranging from to 0, ensuring strong convergence and reliable predictions. Fitness analysis confirm accuracy and efficiency in all scenarios. The developed approach is particularly applicable in biomedical systems, such as artificial organs, micro‐pumps, and drug delivery mechanisms, where efficient heat and fluid transport are critical.