Physics-informed super-resolution of dual-cylinder wake flow fields from coarse-grid data

Abstract

High-resolution flow fields are essential for resolving wake interaction and pressure-coupled unsteady features in bluff-body flows, yet their acquisition from experiments or high-fidelity simulations remains expensive. In dual-cylinder configurations, the interaction between the cylinders can substantially alter the near-wake topology, pressure evolution, and dominant vortex-shedding characteristics, making physically reliable reconstruction from coarse data particularly challenging. In this study, physics-informed neural networks (PINNs) are employed for super-resolution reconstruction of dual-cylinder flow fields at Reynolds number 200 using direct numerical simulation data. To address the non-uniqueness of pressure reconstruction in standard PINNs, a pressure-constrained PINNs (PC-PINNs) framework is proposed by introducing sparse pressure supervision at specific locations to anchor the absolute pressure field. Furthermore, a transfer-learning-enhanced framework (TL-PINNs) is developed to accelerate convergence across related cylinder arrangements. Rather than focusing only on reconstruction accuracy, the present study examines whether the reconstructed fields preserve physically meaningful observables, including near-wake structures, pressure distributions, pressure-fluctuation histories, and dominant spectral signatures. The results show that standard PINNs can recover the major velocity structures but suffer from pressure drift, whereas PC-PINNs markedly improve pressure reconstruction and enable more reliable recovery of pressure-related wake features. TL-PINNs further reduce the training cost while maintaining predictive fidelity and physical interpretability. These findings demonstrate the potential of pressure-constrained physics-informed reconstruction for mechanism-oriented analysis of interference-induced bluff-body flows from coarse-resolution data.