Iterative Learning Active Flow Control Applied to a Compressor Stator Cascade With Periodic Disturbances

Abstract

This paper presents the capability of iterative learning active flow control to decrease the impact of periodic disturbances in an experimental compressor stator cascade with sidewall actuation. The periodic disturbances of the individual passage flows are generated by a damper flap device that is located downstream of the trailing edges of the blades. The device mimics the throttling effect of periodically closed combustion tubes in a pulsed detonation engine (PDE). For the purpose of rejecting this disturbance, the passage flow is manipulated by fluidic actuators that introduce an adjustable amount of pressurized air through slots in the sidewalls of the cascade. Pressure sensors that are mounted flush to the suction surface of the middle blade provide information on the current flow situation. These data are fed back in real-time to an optimization-based iterative learning controller (ILC). By learning from period to period, the controller modifies the actuation amplitude such that, eventually, a control command trajectory is calculated that reduces the impact of the periodic disturbance on the flow in an optimal manner.