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Identification of Surrogate Control Variables for a Robust Active Flow Controller of an Experimental High Speed Stator Cascade
Citation key 2013_steinberg_asme
Author Steinberg, S. and Tiedemann, C. and King, R. and Peitsch, D.
Pages GT2013-94179
Year 2013
ISBN 978-0-7918-5518-8
DOI 10.1115/GT2013-94179
Location San Antonio, Texas, USA
Journal ASME Turbo Expo 2013: Turbine Technical Conference and Exposition
Volume Volume 4: Ceramics; Concentrating Solar Power Plants; Controls, Diagnostics and Instrumentation; Education; Electric Power; Fans and Blowers
Month 06
Note V004T06A002,
Technische Universität Berlin:
S. Steinberg, C. Tiedemann, R. King, D. Peitsch
Editor ASME
Series Turbo Expo: Power for Land, Sea, and Air
Abstract Active flow control is a powerful option to ensure secure operation and enhancement of the performance of axial compressors. To achieve these goals for aerodynamically highly loaded compressor blade profiles even under disturbed conditions, the magnitude of the actuation needs to be adjusted by a closed-loop controller. To this end, sensors must be placed at some meaningful positions at the surface of the blades giving information about the flow situation inside the passages. The sensor information can then lead to surrogate control variables to close the loop. Often, good sensor positions are unknown initially and therefore chosen naively or experience-driven. To obtain more informative surrogate control variables, a different approach is chosen here. Starting with a highly instrumented blade inside a linear stator cascade, featuring 16 pressure gauges in an area which is suspected to lead to high information content with respect to detrimental flow separations at the sidewalls, a Principal Component Analysis is done. The principal components provide valuable information about where and how intensively the flow is influenced by the actuation. This is validated by comparison with the results of oil flow visualizations and wake measurements. The goal is to find a linear combination of as few sensors as possible to provide a meaningful input for the closed-loop controller. As experiments are conducted up to Ma = 0.8, the signal-to-noise ratio becomes a critical issue. For this reason, specifically weighted data are introduced here. A linear combination of sensor data is obtained, describing the main effects of the actuation with an almost linear mapping. For the given set of sensors, that linear combination achieves a maximum signal-to-noise ratio, which makes it well suited as a control variable. The practical usefulness of the control variable within a robust ℋ∞-flow controller is verified in experiments in a high speed stator cascade.
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