Dissipation rate estimation in the turbulent boundary layer using high-speed planar particle image velocimetry

Zaripov, Dinar; Li, Renfu; Dushin, N. S.

doi:10.1007/s00348-018-2663-4

Cited by 24 publications

(9 citation statements)

References 38 publications

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“…These duration and field-of-view configurations covered the expected range of space-time extension of the NWRFs, see figure 4. We used the same processing technique and filtering procedure as has been used previously (Zaripov, Li & Dushin 2019), with slightly different parameters. In order to achieve a steady-state solution, five iterations were executed using DIC algorithm for the first iteration and WIDIM for other iterations using zero-normalised cross-correlation function when estimating the velocity vectors.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, if the appropriate filtering procedure is not applied, eliminating the measurement error and preserving the real NWRF region, one can get a wrong impression about actual values of the spatiotemporal extension of NWRFs. An appropriate filtering procedure was applied in the present work with the detailed description presented elsewhere (Zaripov et al 2019). This allows us to exclude the frequency region where the measurement noise dominates.…”

Section: Methodsmentioning

confidence: 99%

“…An appropriate filtering procedure was applied in the present work with the detailed description presented elsewhere (Zaripov et al. 2019). This allows us to exclude the frequency region where the measurement noise dominates.…”

Section: Numerical and Experimental Detailsmentioning

confidence: 99%

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On a mechanism of near-wall reverse flow formation in a turbulent duct flow

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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On a mechanism of near-wall reverse flow formation in a turbulent duct flow

et al. 2021

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“…The consistency of the profiles of U(y) and u RMS (y) obtained by all the measurements in the log-layer and above cross-validates the reliability of the measurement system. Also included in figure 3 are the results of three DNS datasets relating to a smooth-wall TBL at a similar Reynolds number (Re τ ∼ 1000), obtained by Schlatter et al [31,32], and Sillero et al [33,34], which have been used as standard databases for various studies of flat-plate TBLs [35][36][37][38][39]. Such a comparison clearly shows that the inner peak of the u RMS profile is effectively resolved by the near-wall PTV measurement, where the uncertainty of the peak u RMS is less than 3%.…”

Section: Methodsmentioning

confidence: 99%

Ratio-cut background removal method and its application in near-wall PTV measurement of a turbulent boundary layer

Wang

Pan

Liu

et al. 2020

Meas. Sci. Technol.

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Optical contamination due to wall reflection creates limitations for near-wall velocity field measurement via either particle image velocimetry (PIV) or particle tracking velocimetry (PTV). In this paper, a simple image pre-processing method, i.e. the ratio cut method, is proposed to deal with this problem. It is based on the ratio between the grayscale intensities of tracer particles and those of the laser-illuminated background, on which a direct minimum cut is applied on the basis of a non-dimensional threshold for background removal. To evaluate its performance in near-wall measurement, this ratio cut method, along with two other typical pre-processing methods, i.e. the minimum removal method and the proper orthogonal decomposition (POD) filtering method, are applied to particle images in the near-wall region of turbulent boundary layers over an opaque roughness wall (ORW), whose characteristic roughness height is small enough to be regarded as hydraulically smooth, but still gives rise to severe wall reflection. Results for a case involving a transparent smooth wall, which suffers less from wall reflection issues, and direct numerical simulation (DNS) data at a similar Reynolds number are employed as reference baselines for performance evaluation. The examination of pre-processed particle images, as well as the probability density function (PDF) of grayscale intensities, indicates that the ratio cut method is capable of eliminating time-dependent flare, reducing noise level, and retaining low-intensity particles in the ORW case. These features are almost completely absent in both the minimum removal method and the POD filtering method. In addition, PTV-obtained velocity statistics for an ORW, pre-processed by the ratio cut method, including data relating to fluctuating intensity and the PDF distribution of fluctuating velocity, are shown to be more consistent with those relating to baseline cases than data obtained by either of the the other two methods used for comparison. Moreover, evidence is also provided regarding the superiority and robustness of this approach, in terms of estimating the mean skin friction from the near-all mean velocity profile.

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“…The last proposal (7.4) is fairly good down to , and then gives a slight overestimation. (In a recent paper, Zaripov, Li & Dushin (2019) apply (7.2) to high-speed planar PIV. The results shown in their figure 13 confirm the present results of figure 17 and the benefit brought by these axisymmetric formula for the estimation of the dissipation in PIV experiments.)…”

Section: Local Axisymmetrymentioning

confidence: 99%

A critical analysis of turbulence dissipation in near-wall flows, based on stereo particle image velocimetry and direct numerical simulation data

et al. 2022

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An experiment was performed using stereo particle image velocimetry (SPIV) in the Laboratoire de Mécanique des Fluides de Lille boundary layer facility to determine all the derivative moments needed to estimate the average dissipation rate of the turbulence kinetic energy $\epsilon = 2 \nu \langle {\mathsf{s}}_{ij}{\mathsf{s}}_{ij} \rangle$ , where ${\mathsf{s}}_{ij}$ is the fluctuating strain rate and $\langle ~\rangle$ denotes ensemble averages. Also measured were all the moments of the full average deformation rate tensor, as well as all of the first, second and third fluctuating velocity moments except those involving pressure. The Reynolds number was $Re_\theta = 7634$ or $Re_\tau = 2598$ . The present paper gives the measured average dissipation, $\epsilon$ and the derivative moments comprising it. The results are compared with the earlier measurements of Balint, Wallace & Vukolavcevic (J. Fluid Mech., vol. 228, 1991, pp. 53–86) and Honkan & Andreopoulos (J. Fluid Mech., vol. 350, 1997, pp. 29–96) at lower Reynolds numbers and to new results from a plane channel flow DNS at comparable Reynolds number. Of special interest is the prediction by George & Castillo (Appl. Mech. Rev., vol. 50, 1997, pp. 689–729) and Wosnik, Castillo & George (J. Fluid Mech., vol. 421, 2000, pp. 115–145) that $\epsilon ^+ \propto {x_2^+}^{-1}$ for streamwise homogeneous flows and a nearly indistinguishable power law, $\epsilon \propto {x_2^+}^{\gamma -1}$ , for boundary layers. In spite of the modest Reynolds number, the predictions seem to be correct. Then the statistical character of the velocity derivatives is examined in detail, and a particular problem is identified with the breakdown of local homogeneity inside $x_2^+ = 100$ . A more general alternative for partially homogeneous turbulence flows is offered which is consistent with the observations. With the help of DNS, the spatial characteristics of the dissipation very near the wall are also examined in detail.

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Dissipation rate estimation in the turbulent boundary layer using high-speed planar particle image velocimetry

Cited by 24 publications

References 38 publications

On a mechanism of near-wall reverse flow formation in a turbulent duct flow

On a mechanism of near-wall reverse flow formation in a turbulent duct flow

Ratio-cut background removal method and its application in near-wall PTV measurement of a turbulent boundary layer

A critical analysis of turbulence dissipation in near-wall flows, based on stereo particle image velocimetry and direct numerical simulation data

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