On the near-wall characteristics of acceleration in turbulence

2010 ◽  
Vol 659 ◽  
pp. 405-419 ◽  
Author(s):  
K. YEO ◽  
B.-G. KIM ◽  
C. LEE
Keyword(s):  
Turbulent Flows ◽  
Streamwise Vortex ◽  
Reynolds Numbers ◽  
Viscous Sublayer ◽  
Centripetal Acceleration ◽  
Vortical Structures ◽  
Turbulent Channel Flows ◽  
Background Shear ◽  
Kolmogorov Constant ◽  
Near Wall

The behaviour of fluid-particle acceleration in near-wall turbulent flows is investigated in numerically simulated turbulent channel flows at low to moderate Reynolds numbers, Reτ = 180~600). The acceleration is decomposed into pressure-gradient (irrotational) and viscous contributions (solenoidal acceleration) and the statistics of each component are analysed. In near-wall turbulent flows, the probability density function of acceleration is strongly dependent on the distance from the wall. Unexpectedly, the intermittency of acceleration is strongest in the viscous sublayer, where the acceleration flatness factor of O(100) is observed. It is shown that the centripetal acceleration around coherent vortical structures is an important source of the acceleration intermittency. We found sheet-like structures of strong solenoidal accelerations near the wall, which are associated with the background shear modified by the interaction between a streamwise vortex and the wall. We found that the acceleration Kolmogorov constant is a linear function of y+ in the log layer. The Reynolds number dependence of the acceleration statistics is investigated.

Modeling Reynolds-Number Effects in Wall-Bounded Turbulent Flows

1996 ◽  
Vol 118 (2) ◽  
pp. 260-267 ◽  
Author(s):  
R. M. C. So ◽  
H. Aksoy ◽  
S. P. Yuan ◽  
T. P. Sommer
Keyword(s):  
Reynolds Number ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Strain Tensor ◽  
Reynolds Numbers ◽  
Turbulence Statistics ◽  
Wide Range ◽  
Peak Shear Stress ◽  
Reynolds Number Effects ◽  
Near Wall

Recent experimental and direct numerical simulation data of two-dimensional, isothermal wall-bounded incompressible turbulent flows indicate that Reynolds-number effects are not only present in the outer layer but are also quite noticeable in the inner layer. The effects are most apparent when the turbulence statistics are plotted in terms of inner variables. With recent advances made in Reynolds-stress and near-wall modeling, a near-wall Reynolds-stress closure based on a recently proposed quasi-linear model for the pressure strain tensor is used to analyse wall-bounded flows over a wide range of Reynolds numbers. The Reynolds number varies from a low of 180, based on the friction velocity and pipe radius/channel half-width, to 15406, based on momentum thickness and free stream velocity. In all the flow cases examined, the model replicates the turbulence statistics, including the Reynolds-number effects observed in the inner and outer layers, quite well. Furthermore, the model reproduces the correlation proposed for the location of the peak shear stress and an appropriately defined Reynolds number, and the variations of the near-wall asymptotes with Reynolds numbers. It is conjectured that the ability of the model to replicate the asymptotic behavior of the near-wall flow is most responsible for the correct prediction of the Reynolds-number effects.

Reynolds number scaling of inertial particle statistics in turbulent channel flows

2014 ◽  
Vol 758 ◽  
Author(s):  
Matteo Bernardini
Keyword(s):  
Reynolds Number ◽  
Turbulent Flows ◽  
Large Scale ◽  
Spatial Organization ◽  
Reynolds Numbers ◽  
Stokes Number ◽  
Wall Region ◽  
Inertial Particles ◽  
Turbulent Channel Flows ◽  
Strong Segregation

AbstractThe effect of the Reynolds number on the behaviour of inertial particles in wall-bounded turbulent flows is investigated through large-scale direct numerical simulations (DNS) of particle-laden canonical channel flow spanning almost a decade in the friction Reynolds number, from $\def \xmlpi #1{}\def \mathsfbi #1{\boldsymbol {\mathsf {#1}}}\let \le =\leqslant \let \leq =\leqslant \let \ge =\geqslant \let \geq =\geqslant \def \Pr {\mathit {Pr}}\def \Fr {\mathit {Fr}}\def \Rey {\mathit {Re}}\mathit{Re}_{\tau } = 150$ to $\mathit{Re}_{\tau } = 1000$. Lagrangian particle tracking is used to study the motion of six different particle sets, described by a Stokes number in the range $\mathit{St} = 1\text {--}1000$. At all Reynolds numbers a strong segregation in the near-wall region is observed for particles characterized by intermediate Stokes number, in the range $\mathit{St} =10\text {--}100$. The wall-normal concentration profiles of such particles collapse in inner scaling, thus suggesting the independence of the turbophoretic drift from the large-scale outer motions. This observation is also supported by the spatial organization of the suspended phase in the inner layer, which is found to be universal with the Reynolds number. The deposition rate coefficient increases with $\mathit{Re}_{\tau }$ for a given $\mathit{St}$. Suitable inner and outer scalings are proposed to collapse the deposition curves across the available ranges of Reynolds and Stokes numbers for the different deposition regimes.

Comparison and Scaling of the Bursting Period in Rough and Smooth Walls Channel Flows

1999 ◽  
Vol 121 (4) ◽  
pp. 735-746 ◽  
Author(s):  
S. Demare ◽  
L. Labraga ◽  
C. Tournier
Keyword(s):  
Turbulent Flows ◽  
Major Part ◽  
Reynolds Numbers ◽  
Channel Flows ◽  
Quadrant Analysis ◽  
Number Range ◽  
Mixed Variables ◽  
Detection Techniques ◽  
Turbulent Channel Flows ◽  
Bursting Process

The Structure of Smooth and k-type rough wall turbulent channel flows was examined over a Reynolds number range of 12,700–55,000 using a few of the most common detection techniques (Modified U_Level (MODUL), TERA, Quadrant analysis) and conditional averages. When grouping time is used and a threshold-independent range could be found, all of the above techniques yield approximately the same time between bursts in the major part of the turbulent flow. In the range of Reynolds numbers studied, the bursting frequency is best scaled on mixed variables. The conditional averaged patterns give a representation of the bursting process and show some differences between turbulent flows which develop on smooth and rough walls, in both inner and outer regions.

Pressure Development in the Entrance Region and Fully Developed Region of Generalized Channel Turbulent Flows

1976 ◽  
Vol 43 (1) ◽  
pp. 13-19
Author(s):  
S. M. Hai
Keyword(s):  
Turbulent Flows ◽  
Reynolds Numbers ◽  
Channel Flows ◽  
Pressure Drops ◽  
Turbulent Channel Flows ◽  
Numerical Predictions ◽  
High Reynolds Numbers ◽  
Pressure Development ◽  
High Reynolds ◽  
Very High

In this paper, the pressure drops in the developing length of generalized turbulent channel flows are investigated, and the effects of Reynolds number and distance from the channel entrance are examined. The numerical method is also used to predict the pressure drops in simple channel flows for very high Reynolds numbers (of the order of 100,000). Excellent agreement is obtained between experimental data and numerical predictions.

On the near-wall vortical structures at moderate Reynolds numbers

2014 ◽  
Vol 48 ◽  
pp. 75-93 ◽  
Author(s):  
P. Schlatter ◽  
Q. Li ◽  
R. Örlü ◽  
F. Hussain ◽  
D.S. Henningson
Keyword(s):  
Reynolds Numbers ◽  
Vortical Structures ◽  
Near Wall

Lattice Boltzmann for Turbulence Modeling

2018 ◽  
Author(s):  
Sauro Succi
Keyword(s):  
Turbulent Flows ◽  
Lattice Boltzmann ◽  
Turbulence Modeling ◽  
Real Life ◽  
Practical Interest ◽  
Reynolds Numbers ◽  
Full Resolution ◽  
To Come ◽  
Highly Turbulent Flows ◽  
Main Ideas

This chapter introduces the main ideas behind the application of LBE methods to the problem of turbulence modeling, namely the simulation of flows which contain scales of motion too small to be resolved on present-day and foreseeable future computers. Many real-life flows of practical interest exhibit Reynolds numbers far too high to be directly simulated in full resolution on present-day computers and arguably for many years to come. This raises the challenge of predicting the behavior of highly turbulent flows without directly simulating all scales of motion which take part to turbulence dynamics, but only those that fall within the computer resolution at hand.

scholarly journals Turbulence model performance for ventilation components pressure losses

2021 ◽  
Author(s):  
Karsten Tawackolian ◽  
Martin Kriegel
Keyword(s):  
Reynolds Number ◽  
Turbulence Model ◽  
Pressure Loss ◽  
Low Reynolds Number ◽  
Turbulence Models ◽  
Reynolds Numbers ◽  
Test Case ◽  
Pressure Losses ◽  
Loss Coefficients ◽  
Near Wall

AbstractThis study looks to find a suitable turbulence model for calculating pressure losses of ventilation components. In building ventilation, the most relevant Reynolds number range is between 3×104 and 6×105, depending on the duct dimensions and airflow rates. Pressure loss coefficients can increase considerably for some components at Reynolds numbers below 2×105. An initial survey of popular turbulence models was conducted for a selected test case of a bend with such a strong Reynolds number dependence. Most of the turbulence models failed in reproducing this dependence and predicted curve progressions that were too flat and only applicable for higher Reynolds numbers. Viscous effects near walls played an important role in the present simulations. In turbulence modelling, near-wall damping functions are used to account for this influence. A model that implements near-wall modelling is the lag elliptic blending k-ε model. This model gave reasonable predictions for pressure loss coefficients at lower Reynolds numbers. Another example is the low Reynolds number k-ε turbulence model of Wilcox (LRN). The modification uses damping functions and was initially developed for simulating profiles such as aircraft wings. It has not been widely used for internal flows such as air duct flows. Based on selected reference cases, the three closure coefficients of the LRN model were adapted in this work to simulate ventilation components. Improved predictions were obtained with new coefficients (LRNM model). This underlined that low Reynolds number effects are relevant in ventilation ductworks and give first insights for suitable turbulence models for this application. Both the lag elliptic blending model and the modified LRNM model predicted the pressure losses relatively well for the test case where the other tested models failed.

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