DNS of a Turbulent Channel Flow to Guide Vorticity Measurements in the Wall Region

1999 ◽  
pp. 289-297
Author(s):  
P. G. Esposito ◽  
T. Zhou ◽  
R. A. Antonia ◽  
P. Orlandi
Keyword(s):  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Wall Region

Topology of fine-scale motions in turbulent channel flow

1996 ◽  
Vol 310 ◽  
pp. 269-292 ◽  
Author(s):  
Hugh M. Blackburn ◽  
Nagi N. Mansour ◽  
Brian J. Cantwell
Keyword(s):  
Strain Rate ◽  
Channel Flow ◽  
Velocity Gradient ◽  
Outer Region ◽  
Turbulent Channel Flow ◽  
Principal Strain ◽  
Wall Region ◽  
Fine Scale ◽  
Stable Focus ◽  
Topological Features

An investigation of topological features of the velocity gradient field of turbulent channel flow has been carried out using results from a direct numerical simulation for which the Reynolds number based on the channel half-width and the centreline velocity was 7860. Plots of the joint probability density functions of the invariants of the rate of strain and velocity gradient tensors indicated that away from the wall region, the fine-scale motions in the flow have many characteristics in common with a variety of other turbulent and transitional flows: the intermediate principal strain rate tended to be positive at sites of high viscous dissipation of kinetic energy, while the invariants of the velocity gradient tensor showed that a preference existed for stable focus/stretching and unstable node/saddle/saddle topologies. Visualization of regions in the flow with stable focus/stretching topologies revealed arrays of discrete downstream-leaning flow structures which originated near the wall and penetrated into the outer region of the flow. In all regions of the flow, there was a strong preference for the vorticity to be aligned with the intermediate principal strain rate direction, with the effect increasing near the walls in response to boundary conditions.

scholarly journals Large eddy simulation of turbulent channel flow using differential equation wall model

2018 ◽  
Vol 15 (2) ◽  
pp. 75-89
Author(s):  
Muhammad Saiful Islam Mallik ◽  
Md. Ashraf Uddin
Keyword(s):  
Differential Equation ◽  
Flow Field ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Wall Region ◽  
Computational Domain ◽  
Wall Model ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Near Wall

A large eddy simulation (LES) of a plane turbulent channel flow is performed at a Reynolds number Re? = 590 based on the channel half width, ? and wall shear velocity, u? by approximating the near wall region using differential equation wall model (DEWM). The simulation is performed in a computational domain of 2?? x 2? x ??. The computational domain is discretized by staggered grid system with 32 x 30 x 32 grid points. In this domain the governing equations of LES are discretized spatially by second order finite difference formulation, and for temporal discretization the third order low-storage Runge-Kutta method is used. Essential turbulence statistics of the computed flow field based on this LES approach are calculated and compared with the available Direct Numerical Simulation (DNS) and LES data where no wall model was used. Comparing the results throughout the calculation domain we have found that the LES results based on DEWM show closer agreement with the DNS data, especially at the near wall region. That is, the LES approach based on DEWM can capture the effects of near wall structures more accurately. Flow structures in the computed flow field in the 3D turbulent channel have also been discussed and compared with LES data using no wall model.

Predictability Study of Viscoelastic Turbulent Channel Flow Using Deep Learning

2019 ◽  
Author(s):  
Atsushi Nagamachi ◽  
Takahiro Tsukahara
Keyword(s):  
Channel Flow ◽  
Viscoelastic Fluid ◽  
Coherent Structures ◽  
Turbulent Channel Flow ◽  
Wall Region ◽  
Nonlinear Relationship ◽  
Multi Layer Perceptron ◽  
Numerical Instability ◽  
Z Score ◽  
Score Normalization

Abstract We tested Artificial Neural Networks (ANNs) to predict a fully-developed turbulent channel flow of a viscoelastic fluid in preparation for elucidating flow phenomenon and solving the difficulty in DNS (Direct Numerical Simulation) due to numerical instability of the viscoelastic fluid. Two kinds of ANNs (multi-layer perceptron (MLP) and U-Net) were trained using DNS data to predict conformation stress from given instantaneous field. The MLP showed accurate predictions and predictions got better with z-score normalization. ANN predicted accurately in near-wall region having coherent structures. In addition, we demonstrated that ANN get the nonlinear relationship between velocity gradient and viscoelastic stress partially.

Assessing the Effects of Near Wall Corrections of the Force Acting on Particles in Gas-Solid Channel Flows

2005 ◽  
Author(s):  
Boris Arcen ◽  
Anne Tanie`re ◽  
Benoiˆt Oesterle´
Keyword(s):  
Channel Flow ◽  
Lift Force ◽  
Particle Concentration ◽  
Turbulent Channel Flow ◽  
Shear Velocity ◽  
Solid Particles ◽  
Wall Region ◽  
Wall Effects ◽  
Strong Shear ◽  
Near Wall

The importance of using the lift force and wall-corrections of the drag coefficient for modeling the motion of solid particles in a fully-developed channel flow is investigated by means of direct numerical simulation (DNS). The turbulent channel flow is computed at a Reynolds number based on the wall-shear velocity and channel half-width of 185. Contrary to most of the numerical simulations, we consider in the present study a lift force formulation that accounts for the weak and strong shear as well as for the wall effects (hereinafter referred to as optimum lift force), and the wall-corrections of the drag force. The DNS results show that the optimum lift force and the wall-corrections of the drag together have little influence on most of the statistics (particle concentration, mean velocities, and mean relative and drift velocities), even in the near wall region.

Direct Numerical Simulation of Passive Scalar Field in a Turbulent Channel Flow

1992 ◽  
Vol 114 (3) ◽  
pp. 598-606 ◽  
Author(s):  
N. Kasagi ◽  
Y. Tomita ◽  
A. Kuroda
Keyword(s):  
Numerical Simulation ◽  
Direct Numerical Simulation ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Passive Scalar ◽  
Streamwise Velocity ◽  
Heat Fluxes ◽  
Wall Region ◽  
Turbulent Heat ◽  
Turbulent Heat Fluxes

A direct numerical simulation (DNS) of the fully developed thermal field in a two-dimensional turbulent channel flow of air was carried out. The isoflux condition was imposed on the two walls so that the local mean temperature increased linearly in the streamwise direction. With any buoyancy effect neglected, temperature was considered as a passive scalar. The computation was executed on 1,589,248 grid points by using a spectral method. The statistics obtained were root-mean-square temperature fluctuations, turbulent heat fluxes, turbulent Prandtl number, and dissipation time scales. They agreed fairly well with existing experimental and numerical simulation data. Each term in the budget equations of temperature variance, its dissipation rate, and turbulent heat fluxes was also calculated. It was found that the temperature fluctuation θ′ was closely correlated with the streamwise velocity fluctuation u′, particularly in the near-wall region. Hence, the distribution of budget terms for the streamwise and wall-normal heat fluxes, u′θ′ and v′θ′, were very similar to those for the two Reynolds stress components, u′u′ and u′v′.

Mixing of two thermal fields emitted from line sources in turbulent channel flow

2008 ◽  
Vol 609 ◽  
pp. 349-375 ◽  
Author(s):  
E. COSTA-PATRY ◽  
L. MYDLARSKI
Keyword(s):  
Correlation Coefficient ◽  
Channel Flow ◽  
Large Scale ◽  
Turbulent Channel Flow ◽  
Separation Distance ◽  
Wall Region ◽  
Thermal Plume ◽  
Passive Scalars ◽  
Thermal Fields ◽  
Fast Evolution

The interaction of two passive scalars (both temperature in air) emitted from concentrated line sources in fully developed high-aspect-ratio turbulent channel flow is studied. The thermal fields are measured using cold-wire thermometry in a flow with a Reynolds number (Uh/ν) of 10200.The transverse total root-mean-square (RMS) temperature profiles are a function of the separation distance between the line sources (d/h), their average wall-normal position (ysav/h), and the downstream location (x/h), measured relative to the line sources. Similarly, profiles of the non-dimensional form of the scalar covariance, the correlation coefficient (ρ), are a function of the same parameters and quantify the mixing of the two scalars.The transverse profiles of the correlation coefficient are generally largest at the edges of the thermal plume and smallest in its core. When the line sources are not symmetrically located about the channel centreline, the minimum in the correlation coefficient transverse profiles drifts towards the (closer) channel wall. For source locations that are equidistant from the channel centreline, the minimum correlation coefficient occurs at the centreline, due to the underlying symmetry of this geometry. The initial downstream evolution of the correlation coefficient depends significantly on d/h, similar to that in homogeneous turbulence. However, there is always a dependence on ysav/h, which increases in importance as both the downstream distance is increased and the wall is approached. Lastly, the correlation coefficient profiles tend towards positive values in the limit of large downstream distances (relative to the source separation), though further measurements farther downstream are required to confirm the exact value(s) of their asymptotic limit(s).Spectral analysis of the cospectra and coherency spectra indicates that the large scales evolve more rapidly than the small ones. Furthermore, the fast evolution of the large scales was most evident when the sources were located close to the wall. This presumably derives from the large-scale nature of turbulence production, which is strong in the near-wall region.

Large Eddy Simulation of Turbulent Flow Laden With Binary Mixture of Particles

2007 ◽  
Author(s):  
Neng-Tsung Chang ◽  
Chih-Hung Hsu ◽  
Keh-Chin Chang
Keyword(s):  
Binary Mixture ◽  
Large Eddy Simulation ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Wall Region ◽  
Wall Roughness ◽  
Wall Model ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Normal Particle

Particle-laden turbulent channel flow at Reτ = 644, loaded with binary mixture of particles, is numerically studied using the Lagrangian particle tracking method coupled with large eddy simulation. Turbulence statistics of different particle groups are analyzed. Two particle-wall models are applied to this study with / without considering wall roughness. Taking into considerations of rough wall model, the effect of wall roughness in the computations strengthens the wall-normal particle velocity fluctuations. As a result, particles tend to move from the near-wall region to the central core region. It leads to decrement of particle accumulation in the near-wall region as compared to the case considering the smooth wall model. The wall-normal particle mixing capability is enhanced which results in the redistribution of particles in the channel. The behavior of particle motion in the turbulent channel flow should be, thus, dependent on not only the value of Stokes number but also the wall roughness level.

Shear-Improved Smagorinsky Modeling of Turbulent Channel Flow Using Generalized Lattice Boltzmann Equation

2010 ◽  
Author(s):  
Saeed Jafari ◽  
Mohammad Rahnama
Keyword(s):  
Boltzmann Equation ◽  
Channel Flow ◽  
Lattice Boltzmann ◽  
Computational Cost ◽  
Turbulent Channel Flow ◽  
Lattice Boltzmann Equation ◽  
Wall Region ◽  
Smagorinsky Model ◽  
Mean Velocity ◽  
Turbulent Statistics

Generalized Lattice Boltzmann Equation (GLBE) was used for computation of turbulent channel flow for which Large Eddy Simulation (LES) was employed as a turbulence model. The subgrid-Scale turbulence effects were simulated through a Shear-Improved Smagorinsky Model (SISM) which is capable of predicting turbulent near wall region accurately without any wall function. Computations were done for a relatively coarse grid with shear Reynolds number of 180 in a parallelized code. Good numerical stability was observed for this computational framework. Results of mean velocity distribution across the channel showed good correspondence with Direct Numerical Simulation (DNS) data. Negligible discrepancies were observed for computed turbulent statistics between present computations and those reported from DNS. Three-dimensional instantaneous vorticity contours showed complex vortical structures appeared in such flow geometries. It is concluded that such framework is capable of predicting accurate results for turbulent channel flow without adding significant complication and computational cost to the standard Smagorinsky model.

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