ANN Assistance in Electroosmotic Peristaltic Transport of Bingham-Papanastasiou Fluid in a Arteries with High Zeta Potential

This study highlights the impact of peristalsis and electroosmosis on the transportation of blood in human arteries. Blood is represented by the Bingham-Papanastasiou fluid in a curved channel under the influence of a high zeta potential. Special attention is paid to the importance of yield stress, and electroosmotic effects ion managing resistance to flow, wall shear stress and pressure gradient. Moreover, entropic generation characteristics are examined to assess thermodynamic irreversibility, which accompanies electrokinetic peristaltic transport. To improve prediction, artificial neural network (ANN) model was applied to achieve a convenient approximation of interwoven parametric effects. The results indicate that the interaction between the electroosmotic force and peristaltic movement causes a significant variation in velocity, which reduces the rate of entropy generation and increases pumping effectiveness at high zeta potentials. This is particularly useful when using microfluidic devices, biomedical transport, and the optimization of intelligent electrokinetic devices.