Unsteady Flow Field Analysis of a Compressor Cascade Based on Dynamic Mode Decomposition

Traditional flow field modeling methods are limited by high computational complexity, making them difficult to apply in practical engineering. This study applies the Dynamic Mode Decomposition (DMD) method to perform reduced-order modeling of unsteady flow fields over an airfoil and a compressor cascade. As a data-driven modal decomposition technique, DMD extracts low-dimensional modes from high-dimensional spatiotemporal data, preserving key dynamic characteristics and significantly reducing computational costs. Numerical simulations were conducted to generate time snapshots, forming matrices of pressure and Mach number snapshots. DMD analysis identified a few dominant modes and their eigenvalues, capturing the primary dynamic behavior of the flow field. The results demonstrate that these modes effectively reconstruct the system’s main characteristics, reducing the need for extensive computational resources and time. The DMD method not only improves modeling efficiency, but also accurately reconstructs complex flow structures. This study validates the feasibility and effectiveness of DMD in reduced-order modeling for unsteady flow fields and includes error analysis for further evaluation.