Closed-loop separation control over a sharp edge ramp using Genetic Programming

TL;DR

This study demonstrates that genetic programming control (GPC) can effectively mitigate flow separation over a sharp edge ramp by optimizing actuation, outperforming periodic forcing in energy efficiency and flow reattachment.

Contribution

The paper introduces the application of model-free genetic programming control to turbulent boundary layer separation, showing its advantages over traditional periodic forcing methods.

Findings

GPC reduces flow separation and promotes early re-attachment.

GPC achieves similar or better flow control with less actuation energy.

GPC outperforms periodic forcing in flow reattachment efficiency.

Abstract

We experimentally perform open and closed-loop control of a separating turbulent boundary layer downstream from a sharp edge ramp. The turbulent boundary layer just above the separation point has a Reynolds number $Re_{\theta}\approx 3\,500$ based on momentum thickness. The goal of the control is to mitigate separation and early re-attachment. The forcing employs a spanwise array of active vortex generators. The flow state is monitored with skin-friction sensors downstream of the actuators. The feedback control law is obtained using model-free genetic programming control (GPC) (Gautier et al. 2015). The resulting flow is assessed using the momentum coefficient, pressure distribution and skin friction over the ramp and stereo PIV. The PIV yields vector field statistics, e.g. shear layer growth, the backflow area and vortex region. GPC is benchmarked against the best periodic forcing. While open-loop control achieves separation reduction by locking-on the shedding mode, GPC gives rise to similar benefits by accelerating the shear layer growth. Moreover, GPC uses less actuation energy.