Significance: This article explores the innovative aspect of solving beating cilia related flow with physics informed neural networks (PINNs). PINNs is best way to resolve beating cilia motion without relying on dense meshes or iterative solvers. Purpose: This study explores the beating cilia in confined fluid domains in topological inclined wavy channels. Carreau nanofluid model has been utilized to study such configuration. These features make cilia driven flows valuable for microfluidic cooling systems, particle manipulation and biological transport studies where precise control of near wall dynamics is critical. This study shows how ciliary actuation supports efficient fluid motion without relying on mechanical pumping units. For the velocity control, the inclined magnetic angle has been considered. Methodology: PINNs methodology with a PDFP optimizer has been used to capture physical meaning of beating cilia inside inclined wavy channels. This scheme shows the global view of the solution across space and time. It reduces oscillations in the loss and keeps the network focused on the true physics residuals. Findings: Numerical variation in power-law index parameter reshapes the local shear stiffness of the Carreau nanofluid fluid. WhenWeissenberg number is increasing, stretching the pressure lobes along the channel and disrupts the phase coherence between the wall motion and the fluid response.
Shehzadi et al. (Fri,) studied this question.