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Disturbance Source Identification for Flow Control

Disturbance Source Identification for Flow Control

Wednesday 28/06/2017
  • Igal Gluzman
  • Work towards PhD degree under the supervision of Prof. Jacob Cohen and Prof. Yaakov Oshman
  • Classroom 165, ground floor, Library, Aerospace Eng.
  • Department of Aerospace Engineering
  • Technion – Israel Institute of Technology
  • The talk will be given in Hebrew

We present a novel approach for identifying disturbance sources in wall-bounded shear flows, which can prove useful for active control of boundary layer transition from laminar to turbulent flow. The idea underlying this research is to consider the flow state, as measured in sensors, as a mixture of sources, and to use Blind Source Separation (BSS) techniques to recover the separate sources and their unknown mixing process.

We begin by introducing a BSS method based on the Independent Component Analysis (ICA) technique, and provide physics-based criteria for proper sensor placement in order to successfully separate Tollman-Schlichting (TS) wave disturbances. We verify these criteria by successfully implementing the ICA method in appropriate computer simulations of shear flow.

To alleviate the limitations of the ICA-based source identification technique, we next present a BSS method based on the Degenerate Unmixing Estimation Technique (DUET). This method can be used to identify any (a priori unknown) number of sources by using the data acquired by only two sensors.  We adapt and apply the DUET method to identify disturbance sources in measured mixtures comprising TS waves and wave-packets. Furthermore, we extend the DUET-based method to also determine the propagation velocity vector of each of the identified sources by using sensor signals from three different locations in the flow field.  The viability of the method is demonstrated via numerical simulations and specially-designed wind-tunnel experiments.

The BSS-based identification methods presented in this work rely on the capability to accurately measure velocity signal mixtures using nonlinear flow sensors, such as hot-wire flow velocity probes. This gives rise to the problem of fast and accurate sensor calibration. To address this issue we have developed a Gaussianization-based statistical calibration technique for estimating the nonlinear calibration curve of hot-wire probes.  The method uses as input a measured sequence of voltage samples, corresponding to different unknown flow velocities in the desired  operational range, and only two measured voltages along with their known (calibrated) flow velocities. The novel calibration method is validated against standard calibration methods using data acquired by hot-wire probes in wind-tunnel experiments.

Light refreshments will be served before the lecture
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