Coherent Structure of the Turbulent Boundary Layer at Low and High Velocities

1982 ◽  
Author(s):  
V. Zakkay ◽  
V. Barra
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Phase Relationship ◽  
Lateral Spread ◽  
Flow Structures ◽  
Ordered Structure ◽  
Lateral Direction ◽  
Arrow Head ◽  
Simultaneous Measurements ◽  
Turbulent Burst

An attempt to obtain a description of the coherent, or quasi-ordered structure of the turbulent boundary layer in the lateral direction at low and high velocity is presented in this paper. Simultaneous measurements of velocity, wall pressure and wall shear fluctuations at U∞ = 10, 22.4 and 206 m/sec have been analyzed to obtain a description of the so-called “turbulent burst.” A conditional sampling scheme has been applied to the digitized fluctuations to identify the occurrence of bursts, and their spread thereof in the lateral direction. The results for the lateral spread of the “bursts” indicate that the events can be separated into two groups with opposite phase relationship across the lateral measurements and is thought to be an indication of “arrow-head” or “horseshoe” type shape. The angular spread of the “horseshoe” may be estimated and therefore the angle of each “leg” which makes the x axis may be determined. These results lead to the conclusions that the flow structures at high velocities tend to be very narrow and swept back at, or near the wall and are much wider and flatter away from the wall.

scholarly journals Cross-plane stereo-PIV measurements in a refractive-index-matched environment of flow associated with barchan dunes immersed in a turbulent boundary layer

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Nathaniel Bristow ◽  
Gianluca Blois ◽  
James Best ◽  
Kenneth Christensen
Keyword(s):  
Boundary Layer ◽  
Refractive Index ◽  
Turbulent Boundary Layer ◽  
Coherent Structures ◽  
Fixed Bed ◽  
Flow Structures ◽  
Local Flow ◽  
Piv Measurements ◽  
Complex Interactions ◽  
Barchan Dunes

Barchan dunes are crescent-shaped bedforms that form in aeolian (i.e., wind-driven) environments (including both Earth and other planets, such as Mars) as well as subaqueous environments. Under the forcing of the aloft turbulent boundary layer, they migrate downstream at a rate inversely proportional to their size, which results in complex interactions between neighboring dunes of disparate scales. In particular, it has been observed that dunes will interact at a distance, causing changes in morphology without contacting each other, which is thought to be driven by the way dunes modify the local flow field Bristow et al. (2018); Assis and Franklin (2020). In this study, the coherent structures formed in the wakes of barchan dunes are investigated using measurements of the flow over fixed-bed (i.e., solid) barchan models, both in the wake of an isolated barchan and the interdune region between interacting barchans (Fig. 1(a)). Furthermore, the interactions between the flow structures shed by the dunes and the structures in the incoming boundary layer are analyzed.

Visualization of flow structures in a turbulent boundary layer using a new technique

1989 ◽  
Vol 5 (4) ◽  
pp. 376-382
Author(s):  
Shen Gongxin ◽  
Lian Qixiang ◽  
Huang Zheng ◽  
Ma Guangyun ◽  
Yuan Youming
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
New Technique ◽  
Flow Structures ◽  
A New Technique

Phase-Locked PIV Measurement in the Wake of Model Wind Turbines Under Various Inflow Conditions

2012 ◽  
Author(s):  
David J. Green ◽  
Leonardo P. Chamorro ◽  
Roger E. Arndt ◽  
Fotis Sotiropoulos ◽  
Jian Sheng
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Large Scale ◽  
Flow Structures ◽  
Speed Ratio ◽  
Reynolds Stresses ◽  
Turbulence Statistics ◽  
Horizontal Axis Wind Turbine ◽  
Tip Vortices ◽  
Piv Measurement

This paper focuses on understanding correlative interactions between boundary layer flow structures and the resultant unsteady wake of a Horizontal Axis Wind Turbine (HAWT) model. Phase-locked Particle Image Velocimetry (PIV) is employed to measure turbulence statistics such as velocity, turbulence intensity, shear stress, vorticity, and to subsequently identify large-scale coherent flow structures. In the first stage, phase-lock experiments were performed under free-stream flow conditions. Ten consecutive downstream locations up to six rotor diameters from the turbine are captured. Ensemble averaged velocity and vorticity fields reveal that while the identity of tip vortices are maintained over five rotor diameters downstream of the turbine, their strength decays exponentially. When the turbine is placed in the wake of other units, the vortical structures exhibit a rapid decay in both coherence and strength and substantially suppress the wake-vortex and vortex-vortex interactions, playing an important role in the wake recovery. These observations inspire the current investigation using low-speed phase-locked PIV Interactions among the near wall flow structures in a turbulent boundary layer, hub and tip vortices will be investigated in this paper. The model turbine has a 0.108 m hub height, rotor diameter of 0.128 m and tip speed ratio of 4. It is located in a wind tunnel under nearly zero-pressure-gradient and thermally neutrally stratified conditions. A tripped turbulent boundary layer generated by a picket fence located at the inlet has a boundary layer thickness, δ, of 0.55∼0.6 m. Measurements are performed at Re = 3×105, 4×105, and 12 × 105.. To achieve sufficient spatial resolution, two measurement fields are taken at each stream-wise location to cover upper and lower half of the turbines. Measurements locations extend ten diameters downstream. Robust turbulence statistics, such as velocity fluctuations, Reynolds stresses, full budget of turbulent kinetic energy, are computed from large dataset, totaling 400 GBytes.

scholarly journals Local transport of passive scalar released from a point source in a turbulent boundary layer

2018 ◽  
Vol 846 ◽  
pp. 292-317 ◽  
Author(s):  
K. M. Talluru ◽  
J. Philip ◽  
K. A. Chauhan
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Large Scale ◽  
Reynolds Shear Stress ◽  
Phase Relationship ◽  
Passive Scalar ◽  
Streamwise Velocity ◽  
Concentration Fluctuations ◽  
Normal Velocity ◽  
Velocity Fluctuations

Simultaneous measurements of streamwise velocity ($\tilde{U}$) and concentration ($\tilde{C}$) for a horizontal plume released at eight different vertical locations within a turbulent boundary layer are discussed in this paper. These are supplemented by limited simultaneous three-component velocity and concentration measurements. Results of the integral time scale ($\unicode[STIX]{x1D70F}_{c}$) of concentration fluctuations across the width of the plume are presented here for the first time. It is found that$\unicode[STIX]{x1D70F}_{c}$has two distinct peaks: one closer to the plume centreline and the other at a vertical distance of plume half-width above the centreline. The time-averaged streamwise concentration flux is found to be positive and negative, respectively, below and above the plume centreline. This behaviour is a resultant of wall-normal velocity fluctuations ($w$) and Reynolds shear stress ($\overline{uw}$). Confirmation of these observations is found in the results of joint probability density functions of$u$(streamwise velocity fluctuations) and$\tilde{C}$as well as that of$w$and$\tilde{C}$. Results of cross-correlation coefficient show that high- and low-momentum regions have a distinctive role in the transport of passive scalar. Above the plume centreline, low-speed structures have a lead over the meandering plume, while high-momentum regions are seen to lag behind the plume below its centreline. Further examination of the phase relationship between time-varying$u$and$c$(concentration fluctuations) via cross-spectrum analysis is consistent with this observation. Based on these observations, a phenomenological model is presented for the relative arrangement of a passive scalar plume with respect to large-scale velocity structures in the flow.

Mean Velocity, Reynolds Shear Stress, and Fluctuations of Velocity and Pressure Due to Log Laws in a Turbulent Boundary Layer and Origin Offset by Prandtl Transposition Theorem

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Noor Afzal ◽  
Abu Seena
Keyword(s):  
Boundary Layer ◽  
Shear Stress ◽  
Turbulent Boundary Layer ◽  
Skin Friction ◽  
Velocity Vector ◽  
Reynolds Shear Stress ◽  
Mean Velocity ◽  
Time Period ◽  
Log Law ◽  
Turbulent Burst

The maxima of Reynolds shear stress and turbulent burst mean period time are crucial points in the intermediate region (termed as mesolayer) for large Reynolds numbers. The three layers (inner, meso, and outer) in a turbulent boundary layer have been analyzed from open equations of turbulent motion, independent of any closure model like eddy viscosity or mixing length, etc. Little above (or below not considered here) the critical point, the matching of mesolayer predicts the log law velocity, peak of Reynolds shear stress domain, and turbulent burst time period. The instantaneous velocity vector after subtraction of mean velocity vector yields the velocity fluctuation vector, also governed by log law. The static pressure fluctuation p′ also predicts log laws in the inner, outer, and mesolayer. The relationship between u′/Ue with u/Ue from structure of turbulent boundary layer is presented in inner, meso, and outer layers. The turbulent bursting time period has been shown to scale with the mesolayer time scale; and Taylor micro time scale; both have been shown to be equivalent in the mesolayer. The shape factor in a turbulent boundary layer shows linear behavior with nondimensional mesolayer length scale. It is shown that the Prandtl transposition (PT) theorem connects the velocity of normal coordinate y with s offset to y + a, then the turbulent velocity profile vector and pressure fluctuation log laws are altered; but skin friction log law, based on outer velocity Ue, remains independent of a the offset of origin. But if skin friction log law is based on bulk average velocity Ub, then skin friction log law depends on a, the offset of origin. These predictions are supported by experimental and direct numerical simulation (DNS) data.

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