This research effort develops software tools for closed-loop active flow control. The flow fields used to demonstrate the tools span laminar and turbulent boundary layers, separated airfoil flows, and three-dimensional wake and bluff-body flows. The work takes a combined fluids and controls approach, improving these flows beyond what open-loop actuation, active or passive, can achieve. Control is built on data-driven reduced-order models. Proper Orthogonal Decomposition (POD) remains a foundation, extended here to Dynamic Mode Decomposition, sparse regression (SINDy), cluster-based models, and resolvent analysis that identify the dominant coherent structures and their dynamics. Sensor fusion combines surface pressure, hot-wire, and optical measurements to drive a state estimator, whether a Kalman filter, nonlinear observer, or learned estimator, that reconstructs the modal amplitudes from limited, noisy data. Machine learning, including deep reinforcement learning, synthesizes control laws where the dynamics resist reduced-order description. The controller commands synthetic jet, plasma, or active-surface actuators in real time. The experimental program provides the data foundation: volumetric PIV and particle tracking velocimetry (PTV) of unsteady and actuated flows supply full-field measurements for model construction and controller training, while wind-tunnel tests validate the closed loop under realistic disturbance and Reynolds-number conditions. Real-time implementation on embedded and FPGA hardware closes the loop at the required bandwidth. A structured approach to flow controller development has been elusive for decades; advances in data-driven modeling, machine learning, sensor fusion, and volumetric measurement now make an end-to-end method feasible.

Relevant References:

Seidel, Jürgen, Casey Fagley, and Thomas McLaughlin. "Feedback flow control: a heuristic approach." AIAA Journal 56.10 (2018): 3825-3834.

Brunton, Steven L., Bernd R. Noack, and Petros Koumoutsakos. "Machine learning for fluid mechanics." Annual review of fluid mechanics 52.1 (2020): 477-508.

Vignon, Colin, Jean Rabault, and Ricardo Vinuesa. "Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions." Physics of fluids 35.3 (2023): 031301.

Closed loop flow control; Active flow control; Data-driven modeling; Reduced-order modeling; Proper Orthogonal Decomposition; Machine learning; Sensor fusion; Volumetric PIV/PTV; Unsteady aerodynamics; Actuated flows; Synthetic jet; Plasma actuator; Vortex dynamics; UAV

citizenship

Open to U.S. citizens

level

Open to Postdoctoral and Senior applicants

Additional Benefits

relocation

Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.

health insurance

A group health insurance program is available to awardees and their qualifying dependents in the United States.