English

Reducing transient energy growth in a channel flow using static output feedback control

Fluid Dynamics 2020-12-25 v1

Abstract

Transient energy growth of flow perturbations is an important mechanism for laminar-to-turbulent transition that can be mitigated with feedback control. Linear quadratic optimal control strategies have shown some success in reducing transient energy growth and suppressing transition, but acceptable worst-case performance can be difficult to achieve using sensor-based output feedback control. In this study, we investigate static output feedback controllers for reducing transient energy growth of flow perturbations within linear and nonlinear simulations of a sub-critical channel flow. A static output feedback linear quadratic regulator~(SOF-LQR) is designed to reduce the worst-case transient energy growth due to flow perturbations. The controller directly uses wall-based measurements to optimally regulate the flow with wall-normal blowing and suction from the upper and lower channel walls. Optimal static output feedback gains are computed using a modified Anderson-Moore algorithm that accelerates the iterative solution of the synthesis problem by leveraging Armijo-type adaptations. We show that SOF-LQR controllers can reduce the worst-case transient energy growth due to flow perturbations. Our results also indicate that SOF-LQR controllers exhibit robustness to Reynolds number variations. Further, direct numerical simulations show that the designed SOF-LQR controllers increase laminar-to-turbulent transition thresholds under streamwise disturbances and delay transition under spanwise disturbances. The results of this study highlight the advantages of SOF-LQR controllers and create opportunities for realizing improved transition control strategies in the future.

Cite

@article{arxiv.2012.13314,
  title  = {Reducing transient energy growth in a channel flow using static output feedback control},
  author = {Huaijin Yao and Yiyang Sun and Talha Mushtaq and Maziar S. Hemati},
  journal= {arXiv preprint arXiv:2012.13314},
  year   = {2020}
}
R2 v1 2026-06-23T21:23:09.363Z