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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

LAMIH, UMR-CNRS 8201,
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

Professor
LAMIH, UMR-CNRS 8201,
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

## Abstract

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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## References

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## Figures

Fig. 3

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

Fig. 2

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

Fig. 1

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

Fig. 4

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

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.

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.

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

Fig. 8

Eigenvalue spectrum

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.

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.

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)

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).

Fig. 10

Correlation between first POD temporal coefficient and the different wall sensors

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