Real-time particle image velocimetry using event-based imaging

Abstract

In the context of active flow control the continuously increasing speed of both imaging and computing hardware are enabling real-time particle image velocimetry (RT-PIV) processing scenarios. The basic idea is to extract flow relevant information from real-time processed PIV data to determine flow-controlling parameters with minimal inference time (latency). The proposed contribution describes the implementation of a PIV system based event-based vision (EBV) camera technology and is capable of processing incoming imagery at high frame rates approaching 1 kHz. Unlike conventional cameras, EBV cameras only detect and report intensity changes in the observed scene, providing an asynchronous data stream of contrast change events. With its reduced data rate and potentially rapid data processing capability, EBV is a strong candidate for this purpose. To date, the real-time capability of EBV has not been fully exploited with the exception of the 3d particle tracking velocimetry (PTV) implementations by Rusch & Rösgen (2023) that reconstructed the (time-averaged) 3d flow field in real-time. However, their setup can only simultaneously track a small number of particles of O(50-100). The present work aims at acquiring and processing imagery containing O(1000) particles necessitating approaches differing from conventional PTV.