An Assessment of Event-Based Imaging Velocimetry for Dimensionality Reduction in Turbulent Flows

Abstract

This study investigates the feasibility of using neuromorphic event-based vision (EBV) camera for low-order modelling, a key enabler for real-time flow control. A synchronized experiment with simultaneous Event Based Image Velocimetry (EBIV) and Particle Image Velocimetry (PIV) is performed on a submerged water jet flow at Re = 2600. Flow statistics, spectral content, and reduced-order modeling capabilities using Proper Orthogonal Decomposition (POD) are assessed. Velocity fields are computed via standard PIV processing and compared after interpolation onto a common grid. The analysis reveals good agreement in jet flow statistics and spectra between EBIV and conventional PIV, with differences observed in the power spectral density (PSD) for high frequencies ($St>1.5$) due to higher noise levels in EBIV data. Nonetheless, most of the spectral content is correctly captured by the EBIV. EBIV successfully identifies dominant flow structures and spectral distribution of energy. The correct identification of temporal modes confirms EBIV's potential for flow control applications based on reduced order models. Despite minor discrepancies downstream of the jet in the Low Order Reconstruction (LOR) analysis, attributed to EBIV's challenge in accurately reconstructing broad-spectrum turbulent regions, the technology proves promising for real-time imaging-based flow control.