An assessment of event-based imaging velocimetry for efficient estimation of low-dimensional coordinates in turbulent flows
Luca Franceschelli, Christian Willert, Marco Raiola, Stefano Discetti
Abstract
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.