Our team’s contribution to the 11th iTi conference on turbulence 2025 (iTi 2025) has been published in the Journal of Physics: Conference Series. The paper “Real-time event-based particle image velocimetry for active flow control” is a collaboration coauthored by Christian Willert (DLR Institute of Propulsion Technology), Luca Franceschelli, Enrico Amico (Politecnico di Torino), Marco Raiola, Gioacchino Cafiero , and Stefano Discetti.
You can find it available in Open Access.
Abstract
This work investigates event-based vision (EBV) as a tool for real-time flow diagnostics in configurations analogous to two-dimensional, two-component particle image velocimetry. Because EBV reduces the data stream compared to conventional frame-based imaging, it enables kilohertz-rate pseudo-framing and efficient processing on standard computing hardware. We present a pseudo-frame-based implementation called “real-time event-based imaging velocimetry,” which delivers velocity fields at several hundred hertz with 𝒪(106) vectors per second.
We experimentally demonstrate the concept on a small-scale jet in water, achieving event rates above 100×106 events/s and online processing at 250–700 Hz, depending on seeding and interrogation settings. Beyond these validation cases, we illustrate two active flow control applications on a jet in air: open-loop optimization of jet mixing using Bayesian optimization and closed-loop control of a water jet using reinforcement learning. These results position EBV as a cost-effective, scalable sensing technology with strong potential for real-time feedback in flow control.
