Research Papers: Fundamental Issues and Canonical Flows

Electrochemical Measurements for Real-Time Stochastic Reconstruction of Large-Scale Dynamics of a Separated Flow

[+] Author and Article Information
F. Fadla, A. Graziani, M. Lippert, D. Uystepruyst

Department of Mechanical Engineering,
University of Valenciennes
and Hainaut-Cambresis,
Valenciennes 59300, France

F. Kerherve

Institut PPRIME - UPR 3346,
Department of Fluides, Thermique, Combustion,
CNRS - University of Poitiers - ENSMA,
Poitiers 86000, France

R. Mathis

LML - UMR-CNRS 8107,
Team ER Confined and Free Shear Flows,
Villeneuve d'Ascq 59000, France

L. Keirsbulck

Department of Mechanical Engineering,
University of Valenciennes
and Hainaut-Cambresis,
Valenciennes 59300, France

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received December 9, 2015; final manuscript received July 8, 2016; published online September 12, 2016. Assoc. Editor: Peter Vorobieff.

J. Fluids Eng 138(12), 121204 (Sep 12, 2016) (8 pages) Paper No: FE-15-1909; doi: 10.1115/1.4034198 History: Received December 09, 2015; Revised July 08, 2016

The ability of electrochemical sensors to properly measure wall shear stress is here considered for using these sensors as potential candidates for time-resolved estimation of the large-scale activity occurring in the flow separation region downstream of a bump. The inflow Reynolds number considered, based on the channel full height and the incoming bulk velocity, is Reb= 1735. The methodology implemented consists in combining the electrochemical sensors with particle image velocimetry (PIV) measurements and to build a model estimate of a low-order representation of the flow field. The model estimate is based on a multitime reformulation of the complementary technique. The present paper shows the potential of electrochemical sensors for properly resolving the low-frequency flapping mode whose control was recently shown to be a potential candidate to significantly reduce separation.

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Fig. 3

Iso-contours of mean streamwise velocity. The red squares represent the wall sensors.

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Fig. 2

Boundary layer profile determined at x*=−6 and plotted with inner variables. Taken from Ref. [15].

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Fig. 1

(a) Picture and (b) schematic view of the closed-loop hydrodynamic channel at Lamih

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Fig. 4

Example of a sequence showing the large-scale structure evolution extracted from the visualization. White scale corresponds to the channel full height.

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Fig. 5

Normalized premultiplied power spectral density of the wall shear stress fluctuations at the position x⋆=0.50 (red line) and 1.00 (black line). Dotted line denotes the normalized flapping frequency.

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Fig. 6

Directionless comparison of the concurrent fluctuating wall shear stress obtained by electrochemical method and their estimate using HS-PIV for sensors located at x⋆=1.00 (a) and at x⋆=1.75 (b). Directionless estimation by using PIV (red line), obtained by the electrochemical method (black line). Wall-tangential velocities are extracted from the HS-PIV at Δyn⋆=0.03 from the wall.

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Fig. 7

Cross-correlation coefficient between streamwise velocity fluctuations and the wall shear stress for wall sensors located (a) x⋆=0.5 and (b) x⋆=1.5 downstream

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Fig. 8

Eigenvalue spectrum

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Fig. 9

Streamwise component u1(n)(x) of the spatial eigenfunction (a) mode n = 1 and (b) mode n = 2. White and black colors indicate negative and positive levels, respectively.

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Fig. 11

Time history of the first temporal POD coefficients (black line with symbols) obtained from PIV and (blue line) estimated using mCT. (Red line) low-pass filtered estimated temporal POD coefficient.

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Fig. 12

Snapshots of (a) and (b) PIV data, (c) and (d) low-order reconstructed streamwise velocity component with Ng = 1 at two different instants (top and bottom)

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Fig. 13

Extrema positions of the edges of the recirculation region during a long examined time sequence. (Red) Upper and (blue) lower positions. Results are obtained using the time-resolved low-order estimate of the streamwise velocity component (Ng = 1).

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Fig. 10

Correlation between first POD temporal coefficient and the different wall sensors



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