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Research Papers

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

[+] Author and Article Information
Simon J. Steinberg

Chair of Measurement and Control,
Department of Process Engineering,
Technische Universität Berlin,
Hardenbergstr. 36a,
Berlin 10623, Germany
e-mail: simon.steinberg@tu-berlin.de

Marcel Staats

Chair of Aerodynamics,
Department of Aeronautics and Astronautics,
Technische Universität Berlin,
Marchstr. 12,
Berlin 10587, Germany
e-mail: marcel.staats@ilr.tu-berlin.de

Wolfgang Nitsche

Professor
Chair of Aerodynamics,
Department of Aeronautics and Astronautics,
Technische Universität Berlin,
Marchstr. 12,
Berlin 10587, Germany
e-mail: wolfgang.nitsche@tu-berlin.de

Rudibert King

Professor
Chair of Measurement and Control,
Department of Process Engineering,
Technische Universität Berlin,
Hardenbergstr. 36a,
Berlin 10623, Germany
e-mail: rudibert.king@tu-berlin.de

1Corresponding author.

Manuscript received July 17, 2015; final manuscript received August 3, 2015; published online August 25, 2015. Editor: Kenneth C. Hall.

J. Turbomach 137(11), 111003 (Aug 25, 2015) (8 pages) Paper No: TURBO-15-1152; doi: 10.1115/1.4031251 History: Received July 17, 2015; Revised August 03, 2015

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.

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Figures

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Fig. 1

Stator cascade with damper flaps

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Fig. 2

Blade and sidewall actuator geometry

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Fig. 3

Sidewall actuation and sensor positions on the middle blade's suction surface

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Fig. 4

Fluidic actuator configuration and working principle: (a) downstream actuation-slot active and (b) upstream actuation-slot active

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Fig. 5

Impact of the disturbance generator on passage 4: (a) time-averaged cp-distribution, (b) first PC ( • ), (c) second PC (◻), and (d) amplitudes of the first two PCs

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Fig. 6

Impact of the sidewall actuation on passage 4: (a) time-averaged cp-distribution, (b) first PC, (c) amplitudes of the first PC, and (d) actuation amplitude

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Fig. 7

Closed-loop results of passage 4 for different sensor blade y -positions: (a) evolution of the error norm along the iterations, (b) initial (i = 0) and converged (i = 20) control error, and (c) first try (i = 1) and converged actuation amplitude

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Fig. 8

Comparison of cp -profile time series at y/H=0.5 in passage 4

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