Pressure and spanwise velocity fluctuations in turbulent channel flows: Logarithmic behavior of moments and coherent structures

This study is concerned with the effects of the flow structures including the near-wall coherent eddies in turbulent channel flows on the dispersion and deposition of nano- and micro-particles. A pseudo-spectral computational code was used for direct numerical simulations (DNS) of the Navier-Stokes equations and the corresponding time histories of the instantaneous fluid velocities were evaluated. Under the oneway coupling assumption, the trajectories of a wide range of particle sizes from 10 nm to 80 μm with dimensionless relaxation time of 2.2e−6 to 142 were obtained by solving the particle equation of motion including Stokes drag and Brownian excitations. Dispersion and deposition of particles in the turbulent flow were evaluated and the effects of turbulence structure on different size particles were studied. The simulation results showed that the concentration distribution of small particles that behave like fluid tracer particles were quite random. However, the preferential concentrations appeared as the dimensionless relaxation time increased to 2–20. In particular, the influence of coherent structures in the near-wall regions was clearly detectable on the concentration distribution of particles, as well as, in their deposition pattern. For τ+ = 20 particles due to the increase of relaxation time and inertia of particles, the small-scale turbulent features were filtered out and only the effect of large-scale turbulent eddies could be identified. For τ+ = 2–20 particles, the ensemble/time average of the position of the deposited particles showed specific spacing which was comparable to the size of the near-wall coherent structures.

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0812 Direct numerical simulation of large-scale coherent structures in high-Re turbulent channel flows

The Proceedings of the Fluids engineering conference ◽

10.1299/jsmefed.2014._0812-1_ ◽

2014 ◽

Vol 2014 (0) ◽

pp. _0812-1_-_0812-2_

Author(s):

Ryota WATANABE ◽

Yoshinobu YAMAMOTO ◽

Yoshiyuki TSUJI

Keyword(s):

Numerical Simulation ◽

Direct Numerical Simulation ◽

Coherent Structures ◽

Large Scale ◽

Channel Flows ◽

Turbulent Channel Flows

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Large eddy simulation of magnetohydrodynamic turbulent channel flows with local subgrid-scale model based on coherent structures

Physics of Fluids ◽

10.1063/1.2194967 ◽

2006 ◽

Vol 18 (4) ◽

pp. 045107 ◽

Cited By ~ 46

Author(s):

Hiromichi Kobayashi

Keyword(s):

Large Eddy Simulation ◽

Coherent Structures ◽

Channel Flows ◽

Scale Model ◽

Subgrid Scale ◽

Model Based ◽

Eddy Simulation ◽

Turbulent Channel Flows ◽

Large Eddy ◽

Subgrid Scale Model

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Identity of attached eddies in turbulent channel flows with bidimensional empirical mode decomposition

Journal of Fluid Mechanics ◽

10.1017/jfm.2019.272 ◽

2019 ◽

Vol 870 ◽

pp. 1037-1071 ◽

Cited By ~ 10

Author(s):

Cheng Cheng ◽

Weipeng Li ◽

Adrián Lozano-Durán ◽

Hong Liu

Keyword(s):

Empirical Mode Decomposition ◽

Stokes Equations ◽

Reynolds Shear Stress ◽

Current Approach ◽

Channel Flows ◽

Velocity Fluctuations ◽

Turbulent Channel Flows ◽

Mode Decomposition ◽

Bidimensional Empirical Mode Decomposition ◽

Attached Eddies

Bidimensional empirical mode decomposition (BEMD) is used to identify attached eddies in turbulent channel flows and quantify their relationship with the mean skin-friction drag generation. BEMD is an adaptive, non-intrusive, data-driven method for mode decomposition of multiscale signals especially suitable for non-stationary and nonlinear processes such as those encountered in turbulent flows. In the present study, we decompose the velocity fluctuations obtained by direct numerical simulation of channel flows into BEMD modes characterized by specific length scales. Unlike previous works (e.g. Flores & Jiménez, Phys. Fluids, vol. 22(7), 2010, 071704; Hwang, J. Fluid Mech., vol. 767, 2015, pp. 254–289), the current approach employs naturally evolving wall-bounded turbulence without modifications of the Navier–Stokes equations to maintain the inherent turbulent dynamics, and minimize artificial numerical enforcement or truncation. We show that modes identified by BEMD exhibit a self-similar behaviour, and that single attached eddies are mainly composed of streaky structures carrying intense streamwise velocity fluctuations and vortex packets permeating in all velocity components. Our findings are consistent with the existence of attached eddies in actual wall-bounded flows, and show that BEMD modes are tenable candidates to represent Townsend attached eddies. Finally, we evaluate the turbulent-drag generation from the perspective of attached eddies with the aid of the Fukagata–Iwamoto–Kasagi identity (Fukagata et al., Phys. Fluids, vol. 14(11), 2002, pp. L73–L76) by splitting the Reynolds shear stress into four different terms related to the length scale of the attached eddies.

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Accuracy of the CRW Models for Prediction of the Deposition and Dispersion of Particles in Inhomogeneous Turbulent Channel Flows

Volume 5: Multiphase Flow ◽

10.1115/ajkfluids2019-4856 ◽

2019 ◽

Author(s):

Amir A. Mofakham ◽

Goodarz Ahmadi

Keyword(s):

Experimental Data ◽

Stochastic Model ◽

Turbulent Flows ◽

Deposition Velocity ◽

Finite Size ◽

Channel Flows ◽

Concentration Profiles ◽

Velocity Fluctuations ◽

Deposition Velocities ◽

Turbulent Channel Flows

Abstract The accuracy of the continuous random walk (CRW) stochastic model for prediction of dispersion and deposition of suspended particles in inhomogeneous turbulent channel flows was explored. The Reynolds-averaged Navier-Stokes (RANS) equations in conjunction with the Reynolds Stress Transport model was used to evaluate the mean flow and RMS velocity fluctuation characteristics of a fully developed turbulent channel flow at shear Reynolds number of 219. Then, spherical particles with diameters ranging from 10 nm to 30 μm and dimensionless relaxation times of 10−4 to 50 (in wall units) were uniformly introduced into the channel and their trajectories were evaluated by using the equation of particle motion including the Stokes drag and Brownian excitation. The particle laden flow was assumed to be sufficiently dilute so that the particle-particle collisions and the effects of particles on the flow could be ignored. To incorporate the effects of turbulence velocity fluctuations on particle motions, first, the Conventional-CRW stochastic model, which was originally proposed for homogenous turbulent flows, was used. The particles were tracked for the duration of 10,000 wall units of time and the deposition of particles on the walls was evaluated. By conducting ensemble averaging, the steady-state concentration profiles and deposition velocity of the particles were calculated. Comparison of the predicted results with the direct numerical simulation (DNS) and experimental data suggests that the deposition velocity was overestimated. In addition, unrealistic accumulation of fluid-point particles in the near-wall regions, and overestimation of the turbophoresis effects on finite-size particles were also observed. The poor agreement of the concentration profiles and deposition velocities resulting from the conventional (homogenous flow) CRW model with the experimental and the DNS data pointed to the lack of accuracy of the Conventional-CRW model in generating instantaneous fluid velocity fluctuations seen by ultrafine and finite-size particles in inhomogeneous turbulent flows. Then, the normalized Langevin equation with a drift correction term that was suggested by Bocksell and Loth [1] was used as an improved CRW model for applications to inhomogeneous flows. The simulations for the same range of particle sizes were repeated and the corresponding concentration profiles and the deposition velocity were evaluated. It was shown that the improved CRW model led to a reasonable uniform concentration profile for the ultrafine particles and the predicted concentration profiles of finite-size particles quantitatively matched with the DNS data. In addition, the evaluated deposition velocities from the improved CRW model were also in a good agreement with the experimental data and empirical model predictions.

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