  Effective stormwater treatment infrastructures are crucial for mitigating the
adverse effects of runoff on urban water quality. However, designing
cost-effective treatment systems can be challenging due to complex turbulent
flow dynamics. This study presents an in-depth analysis of turbulent flow in
hydrodynamic separators (HS) using time-resolving, high-resolution particle
image velocimetry (PIV) and modal decomposition techniques. The examination of
interrogation window sizes on PIV measurements highlights a trade-off between
spatial resolution and measurement uncertainty. Additionally, the impact of
sampling frequencies and durations on the convergence of turbulence statistics,
such as mean flow and Reynolds stress, is quantified. Results indicate that
higher-order statistics require significantly larger sampling sizes (5x) to
achieve the same level of statistical convergence as mean flow statistics.
Leveraging proper orthogonal decomposition (POD) and spectral proper orthogonal
decomposition (SPOD), dominant turbulence structures and coherent flow features
are identified, providing a foundation for the development of reduced-order
models (ROM). These ROMs demonstrate improved computational efficiency and have
potential for real-time control applications and integration into larger
drainage-scale simulations. Furthermore, the outcome of this study establishes
a high-quality, open-source turbulent flow database for HS systems, offering
the civil and environmental engineering community valuable resources to guide
the development and application of computational fluid dynamics (CFD) tools.
This study represents a critical step toward the ultimate goal of facilitating
scientifically guided HS design and optimization.
