Predicting swirling flow states in finite-length pipes using physics-informed neural networks

We investigate the capabilities of physics-informed neural networks (PINNs) in predicting the dynamics of axisymmetric, inviscid-limit swirling flow states in finite-length pipes. The inlet flow is described by the circumferential and axial velocity profiles, along with a fixed azimuthal vorticity, while the outlet flow is characterized by a zero radial velocity state. A fully-connected deep neural network is implemented to learn the solution to the unsteady stream function-circulation equations governing the dynamics of swirling flows. To evaluate the results predicted by PINNs, we also solve the problem using global analysis techniques and numerical simulations. We establish a correlation between the outlet states of the solutions obtained using these three different approaches. Our results suggest that PINNs have great potential in predicting swirling flow states. Moreover, the results provide insights into the stability of various states and the nature of flow evolution.