Investigation of the Three-Dimensional Coherent Structures in a Turbulent Boundary Layer with Tomographic-PIV

Elsinga, Gerrit E.; Kuik, Dirk Jan; Oudheusden, B.W. van; Scarano, Fulvio

doi:10.2514/6.2007-1305

Cited by 27 publications

(44 citation statements)

References 15 publications

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“…As a result, it is now being applied as a useful tool in fluid dynamics investigations of predominantly turbulent flows (e.g. Elsinga et al 2007Elsinga and Marusic 2010;Schröder et al 2008a, b;Hain et al 2008;Humble et al 2009;Kühn et al 2009;Ortiz-Duenas et al 2010). Where the mean and RMS flow statistics have been evaluated in these experiments and compared against other data, they appear to agree to within approximately 0.3 pixel particle displacement (Elsinga 2008) showing that the method can be accurate.…”

Section: Introductionmentioning

confidence: 84%

On the velocity of ghost particles and the bias errors in Tomographic-PIV

et al. 2010

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The paper discusses bias errors introduced in Tomographic-PIV velocity measurements by the coherent motion of ghost particles under some circumstances. It occurs when a ghost particle is formed from the same set of actual particles in both reconstructed volumes used in the cross-correlation analysis. The displacement of the resulting ghost particle pair is approximately the average displacement of the set of associated actual particles. The effect is further quantified in a theoretical analysis and in numerical simulations and illustrated in an actual experiment. It is shown that the bias error does not significantly affect the measured flow topology as deduced in an evaluation of the local velocity gradients. Instead, it leads to a systematic underestimation of the measured particle displacement gradient magnitude. This phenomenon is alleviated when the difference between particles displacement along the volume depth is increased beyond a particle image diameter, or when the reconstruction quality is increased or when the accuracy of the tomographic reconstruction is improved. Furthermore, guidelines to detect and avoid such bias errors are proposed.

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Section: Introductionmentioning

confidence: 84%

On the velocity of ghost particles and the bias errors in Tomographic-PIV

et al. 2010

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“…Having the full velocity field allows quantitative visualization of the vortical motion and computation of the three-dimensional conditionally averaged eddies, thereby permitting more direct interpretation of the results than in probe and planar measurements. Previous results obtained with this technique in low-speed turbulent boundary layer flows have been reported in Schröder et al (2007) and Elsinga et al (2007).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional vortex organization in a high-Reynolds-number supersonic turbulent boundary layer

et al. 2010

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Tomographic particle image velocimetry was used to quantitatively visualize the three-dimensional coherent structures in a supersonic (Mach 2) turbulent boundary layer in the region between y/δ = 0.15 and 0.89. The Reynolds number based on momentum thickness Re θ = 34 000. The instantaneous velocity fields give evidence of hairpin vortices aligned in the streamwise direction forming very long zones of lowspeed fluid, consistent with Tomkins & Adrian (J. Fluid Mech., vol. 490, 2003, p. 37). The observed hairpin structure is also a statistically relevant structure as is shown by the conditional average flow field associated to spanwise swirling motion. Spatial low-pass filtering of the velocity field reveals streamwise vortices and signatures of large-scale hairpins (height > 0.5δ), which are weaker than the smaller scale hairpins in the unfiltered velocity field. The large-scale hairpin structures in the instantaneous velocity fields are observed to be aligned in the streamwise direction and spanwise organized along diagonal lines. Additionally the autocorrelation function of the wallnormal swirling motion representing the large-scale hairpin structure returns positive correlation peaks in the streamwise direction (at 1.5δ distance from the DC peak) and along the 45 • diagonals, which also suggest a periodic arrangement in those directions. This is evidence for the existence of a spanwise-streamwise organization of the coherent structures in a fully turbulent boundary layer.

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“…[1,18] Given the quasi-periodic repeatability [6] of burst events and the extended self-similarity [7] of multi-scale eddy coherent structures, a new conditional sampling average method [19] based on the IQSM to extract turbulent coherent structures is given by However, for convenient to compare, we cut uniformly 24 points out as values of j l along the streamwise and spanwise direction respectively, while j l =105WU along the wall-normal direction at every j -th scale. Fig.2 illustrates the program flow chart which can demonstrate how to detect and extract coherent structures from the turbulence tomographic TR-PIV signal database for data mining.…”

Section: Databases and Data Mining Methodsmentioning

confidence: 99%

“…[4] Burst created by the low-speed fluid ejecting from wall region (also called Q2, namely ejection event) or by the high-speed fluid sweeping towards the wall (Q4 or sweep event) is the most important source for the generation of turbulent energy and Reynolds shear stress. [1,5] Existing research results show that within the TBLs we can clearly observe the repeatability [6] of a variety of similar processes as well as the extended selfsimilarity [7] of multi-scale eddy structures. In this paper, the spatial locally averaged velocity strains along the streamwise direction with four scales in the velocity field of turbulent boundary layer were calculated according to the conception of spatial local-averaged velocity structure function by using the three-dimensional three-component (3D-3C) database of time series of velocity vector field measured by tomographic timeresolved PIV (TR-PIV).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, with the rapid development of threedimensional particle image velocimetry (3D-PIV) technology, the data mining (detection and extraction [1]) of threedimensional coherent structures(CSs) of turbulent boundary layers(TBLs) utilizing relevant experimental technology and data processing algorithms [2][3] as well as the analysis of their topology characteristics become a research focus. The coherent structures are defined as such structures that possess the same flow characteristics or closely relate to each other within a space whose spatial magnitude is in the order of boundary layer thickness.…”

Section: Introductionmentioning

confidence: 99%

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Data mining of coherent structures from turbulence tomographic TR-PIV signal database utilizing improved quadrant splitting method

Yang

Jiang

2012

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

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Abstract-In this paper, we calculated the spatial locally averaged velocity strains along the streamwise direction with four scales in the velocity vector field of turbulent boundary layer by utilizing the three-dimensional three-component database of time series of velocity field measured by tomographic time-resolved particle image velocimetry. We introduced an improved quadrant splitting method based on the spatial local-averaged velocity strains and a new conditional sampling phase average method as a datamining criterion to detect and extract the inherent coherent structures from such turbulence signal database. Furthermore, we used the improved quadrant splitting method to obtain and investigate the spatial topologies of fluctuating velocity and fluctuating vorticity with four scales whose center was a strong second-quadrant event (Q2) or a fourth-quadrant event (Q4). Results illustrate that there is a better similarity of the multiscale coherent structures in wall-normal direction than the one in other two dimensions.

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Investigation of the Three-Dimensional Coherent Structures in a Turbulent Boundary Layer with Tomographic-PIV

Cited by 27 publications

References 15 publications

On the velocity of ghost particles and the bias errors in Tomographic-PIV

On the velocity of ghost particles and the bias errors in Tomographic-PIV

Three-dimensional vortex organization in a high-Reynolds-number supersonic turbulent boundary layer

Data mining of coherent structures from turbulence tomographic TR-PIV signal database utilizing improved quadrant splitting method

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