Separation control using hydrogen bubble visualization · Decreasing frequency of actuation. 12...

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

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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)

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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

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System response

Sensor signal (velocity)

Actuator position (arbitrary units)

Initial perturbation

Leading to periodic actuationConvective time delay

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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!