TensorFlow-based deep neural network framework for predicting peristaltic flow of dissipative Cross fluids in curved porous channels

This study investigates the peristaltic transport of a Cross fluid within a curved channel exhibiting symmetric wall motion and temperature-dependent thermal conductivity. Motivated by real-world applications in biomedical pumping, targeted drug delivery, and heat-sensitive fluid systems, the study captures the intricate coupling between wall curvature, shear-thinning fluid behavior, and thermal variability. The non-Newtonian Cross fluid model provides a realistic representation of complex biological fluids, while the inclusion of temperature-dependent thermal conductivity reflects practical thermal dynamics in physiological and industrial processes. A highly nonlinear system of coupled equations governing velocity, temperature, and concentration fields is formulated and solved numerically using Mathematica's powerful NDSolve function. To enhance predictive capability and computational efficiency, a TensorFlow-based artificial neural network (ANN) framework is developed and trained on the generated data. The ANN model demonstrates high accuracy in replicating fluid behavior and significantly reduces computation time. The results reveal the profound influence of governing parameters on flow structure, thermal distribution, and solute transport, offering valuable insights for optimizing peristaltic devices and thermal regulation systems in medical and engineering domains.