Physics-Informed Neural Networks for Time-Dependent Moderate Reynolds Flow Around a Rotating Cylinder: A Study of Lift, Drag, and Wake Dynamics

This study assesses physics-informed neural networks (PINNs) for the unsteady two-dimensional flow past a rotating circular cylinder confined in a channel (blockage ratio 0.2) at Reynolds numbers of 50, 200 and 500. The networks are trained on the non-dimensional Navier–Stokes residuals, the boundary conditions including the rotating wall, and an initial velocity field, using adaptive loss weighting and time marching. A finite-volume reference solver, verified against the Schäfer–Turek benchmark to within 1.2%, is used to quantify the PINN accuracy and to characterise the flow for rotation ratios between −1 and 1.5. Rotation produces a Magnus lift that increases almost linearly with the rotation ratio (Cl¯≈−3.7α), reduces the mean drag and leaves the Strouhal number nearly unchanged, while all cases, including the stationary cylinder at Re=50, exhibit periodic vortex shedding. The data-free PINN reproduces the wake topology and the sign of the Magnus lift, with relative L2 velocity errors of 14–24%, but underestimates the pressure and the hydrodynamic forces. A systematic ablation shows that sparse velocity data (0.4% of the domain) halve the velocity error, and that a hard rotating-wall constraint with boundary-layer sampling further reduces it to 6% and lowers the lift error to 30% at Re=50. The persistent force errors at higher Reynolds numbers identify the representation of the thin rotating boundary layer as the principal limitation of PINNs for this class of flows.