Event-Based Imaging Velocimetry for Dimensionality Reduction in Turbulent Flows

This study examines the use of neuromorphic event-based vision (EBV) cameras for low-order modeling to assess their potential for real-time flow control. We compare their performance to conventional Particle Image Velocimetry (PIV). A synchronized experiment using Event-Based Image Velocimetry (EBIV) and PIV was conducted on a submerged water jet flow at Re=2600. The findings show that EBIV provides comparable flow statistics and spectral content to PIV, despite higher noise levels in high-frequency regions (St>1.5). Proper Orthogonal Decomposition (POD) analysis revealed that EBIV effectively identifies dominant flow structures and spectral energy distribution, demonstrating its potential for applications in real-time flow control. Furthermore, a Low Order Reconstruction (LOR) study confirmed that EBIV provides comparable spatial and temporal bases to those of conventional PIV, with discrepancies below a few percentage points. The study underscores EBIV's promise for real-time, imaging-based flow control, advocating for dedicated data-processing frameworks to enhance measurement quality. Future work will focus on optimizing algorithms and exploring broader fluid dynamics applications, integrating EBV cameras into closed-loop control systems.