An assessment of event-based imaging velocimetry for efficient estimation of low-dimensional coordinates in turbulent flows

This study explores the potential of neuromorphic Event-Based Vision (EBV) cameras for data-efficient representation of low-order model coordinates in turbulent flows. Unlike conventional imaging systems, EBV cameras asynchronously capture changes in temporal contrast at each pixel, delivering high-frequency output with reduced data bandwidth and enhanced sensitivity, particularly in low-light conditions. Pulsed Event-Based Imaging Velocimetry (EBIV) is assessed against traditional Particle Image Velocimetry (PIV) through two synchronized experiments: a submerged water jet and airflow around a square rib in a channel. The assessment includes a detailed comparison of flow statistics and spectral content, alongside an evaluation of reduced-order modeling capabilities using Proper Orthogonal Decomposition (POD). The event stream from the EBV camera is converted into pseudo-snapshots, from which velocity fields are computed using standard PIV processing techniques. These fields are then compared after interpolation onto a common grid. Modal analysis demonstrates that EBIV can successfully identify dominant flow structures, along with their energy and dynamics, accurately discerning singular values, spatial modes, and temporal modes. While noise contamination primarily affects higher modes – less critical for flow control applications – overall performance remains robust. Additionally, comparisons of Low-Order Reconstruction (LOR) validate EBIV’s capability to provide reliable reduced-order models of turbulent flows, essential for flow control purposes. These findings position EBV sensors as a promising technology for real-time, imaging-based closed-loop flow control systems. • Event-Based Vision enables real-time velocimetry with less data. • Statistics and spectral content matches well with conventional PIV. • Modal analysis shows that EBIV accurately captures dominant flow structures. • EBIV demonstrates potential for real-time estimation of low-order coordinates.