PIV-in-the-loop: real-time flow field optimization of active turbulence grids

Unsteady and turbulent flows play a central role in transport, mixing, and aerodynamic performance across engineering and natural systems. Tailoring such flows requires both accurate diagnostics and robust methods to optimize actuation strategies for specific targets. This study presents a closed-loop framework that combines automated particle image velocimetry (PIV) measurements with an optimization algorithm to tune actuation parameters in real time, without operator intervention, based solely on flow field analysis.

We demonstrate the method using an active turbulence grid in a wind tunnel, where the actuation of multiple vanes is optimized to produce prescribed flow characteristics. By varying modal control parameters, the system autonomously identifies actuation strategies that achieve different turbulence intensities while also regulating homogeneity and isotropy. The results illustrate how wake-based optimization provides a direct and flexible means of shaping inflow conditions, enabling controlled studies of turbulence and transition in complex environments.

More broadly, the method offers a general and adaptable platform for data-driven flow control. Its ability to iteratively refine actuation strategies from flow field data alone makes it particularly well suited for experimental setups where conventional approaches, such as direct force balances, are either unavailable or less meaningful. This capability opens new opportunities for tailoring turbulence, testing flow responses under controlled disturbances, and probing the role of coherent structures in canonical and applied problems.