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Separation control usinghydrogen bubble visualization
V. Parezanovic, L. Cordier and A. Spohn
Journées du GDR « Contrôle Des Décollements »18-19 Novembre 2015, Ecole Centrale de Nantes
ANR “SEPACODE” – Étude de la Physique du Décollement et Réalisation de son Contrôle
2
Closed‐loop control of a mixing layer
• Only in relation with the initial K-H frequency • Limited frequency selection
Wiltse and Glezer(2011) Exp. Fluids
Pinier, Ausseur, Glauser and Higuchi(2007) AIAA Journal
Flow conditions:
Re = 7900U0 = 7.8 cm/sSt = 0.34 (fn = 0.64 Hz)
At x = 0δ99 ≈ 9 mmθ = 0.9 mm
Actuator:Horizontal oscillating wireΦ = 0.13 mmA = 1 - 3 mmf = 0.1 – 2 Hz
• flow section 0.3 m x 0.50 m x 2.10 m
• speed < 0,50 m/sec
Ramp:L = 100 mml = 600 mmh = 60 mm
Experimental setup
Sensors:
2x CCD cameraPoint Grey FLEA390 fps1280 x 1024
0
170 mm
62 mmx
y
~70 mm(separation)
Sensors
100 mm(leading edge to actuator)
~750 mm
Flow seeding:
Hydrogen bubbles (synchronized with camera acquisition)
5
Velocity measurements...
1. Detect the timeline locations in « x » from a single image2. Measure the distance between two adjacent timelines3. Divide by the time between two hydrogen bubble pulses4. Obtain velocity time series from a sequence of images
Example of measurement of velocity along a horizontal line (green box)
Peaks of light intensity in the region of interest
Local velocity time series from a sequence of images
, 1
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... in the boundary layer...
Experiment
Blasius profile
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...and of the whole(*) flow fieldR
e~16
000
1200
010
000
8000
6000
(*) except in the unseeded part of the flow field
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Natural flow(s)
Re~6000
Re~8000
Re~10000
Re~12000
Re~16000
Velocity time series, measured near vortex roll-up:
Re~16000
Re~12000
Re~10000Re~8000
Re~6000
Estimation of the natural frequency fkh (Kelvin-Helmholtz)
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Objective function and open‐loop
Re~6000
Re~8000
Re~10000
Re~12000
Re~16000
Re~8000
Mean velocity U vs. xExtract instantaneous velocity u along a line:
dx
X
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Closed‐loop control
Re~8000
Open-loop
Closed-loop
b – actuator displacements – sensor signal (velocity fluctuations)C – offset (mean position of the actuator above the ramp)
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Closed‐loop vs. sensor position
Re~8000
Open-loop
Closed-loop
Increasingx of the sensor
Decreasing frequency of actuation
12
System response
Sensor signal (velocity)
Actuator position (arbitrary units)
Initial perturbation
Leading to periodic actuationConvective time delay
13
Conclusions
1. Feedback type of control can be effective in convective flows2. What do we feed back?3. Problems with feedback control (mean flow modification, amplitude selection, control law
optimization...)4. Modelling of the system response?
For more information look at:
• Parezanovic et al. Mixing layer manipulation experiment – from periodic forcing to machine learning closed-loop control , Flow, Turbulence and Combustion (2014).
• Duriez et al. Closed-loop turbulence control using machine learning, arXiv preprint arXiv:1404.4589 (2014).
• Parezanovic et al. Frequency selection by feedback control in a turbulent shear-flow, in preparation for JFM.
zu
controller
u
natural resonance
Feedback control in a mixing layer
Mixing layer over a cavity
Control law ~ Cavity resonance?
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Thank You!
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