Improvement of Reduced-Order Model for Two-Dimensional Cylinder Flow Based on Global Proper Orthogonal Decomposition in Terms of Robustness and Computational Speed

Abstract Reduced-order models (ROMs) are widely used in fluid engineering to enable rapid prediction of flow fields for parametric analysis, design optimization, and control applications. Proper orthogonal decomposition (POD) is commonly employed to construct ROMs because it provides an optimal basis for representing a given flow dataset. However, POD-based ROMs often lack robustness when applied to flow conditions that differ from those included in the training data. Incorporating multiple flow conditions can improve robustness, but this generally increases the computational cost of ROM prediction, which limits practical applicability in engineering workflows. In this study, we propose a ROM framework that achieves fast and robust flow prediction even when the dataset contains a large number of flow conditions. The proposed approach employs a novel two-step order-reduction strategy based on POD. In the second reduction step, flow conditions that are most relevant to the target prediction are selectively retained, thereby reducing the computational cost without sacrificing accuracy. The performance of the proposed ROM is evaluated for a two-dimensional unsteady flow past a circular cylinder, a canonical benchmark problem in fluid engineering. The model accurately reproduces the relationship between vortex-shedding frequency and Reynolds number obtained from full numerical simulations. Furthermore, the proposed ROM reduces the computational cost by approximately 50% compared with a conventional POD-based ROM constructed using flow data at 27 different Reynolds numbers.