Eduction of near wall flow structures responsible for large deviations of the momentum–heat transfer analogy and fluctuations of wall transfer rates in turbulent channel flow

2007 ◽  
Vol 36 (8) ◽  
pp. 1327-1334 ◽  
Author(s):  
J. Pallares ◽  
A. Vernet ◽  
J.A. Ferré ◽  
F.X. Grau
Keyword(s):  
Heat Transfer ◽  
Large Deviations ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Flow Structures ◽  
Transfer Rates ◽  
Wall Flow ◽  
Near Wall

Identification of near-wall flow structures producing large wall transfer rates in turbulent mixed convection channel flow

2010 ◽  
Vol 39 (1) ◽  
pp. 15-24 ◽  
Author(s):  
A. Fabregat ◽  
J. Pallares ◽  
A. Vernet ◽  
I. Cuesta ◽  
J.A. Ferré ◽  
...  
Keyword(s):  
Mixed Convection ◽  
Channel Flow ◽  
Flow Structures ◽  
Transfer Rates ◽  
Wall Flow ◽  
Near Wall

Fluid Flow and Heat Transfer in Duct Fan Flows with Top-Wall Rectangular-Wing Turbulators

2008 ◽  
Vol 24 (2) ◽  
pp. N15-N19
Author(s):  
T. Y. Chen ◽  
Y. H. Chen
Keyword(s):  
Heat Transfer ◽  
Nusselt Number ◽  
Fluid Flow ◽  
Bottom Wall ◽  
Flow Structures ◽  
Mean Velocity ◽  
Flow Parameters ◽  
Flow And Heat Transfer ◽  
Wall Flow ◽  
Near Wall

ABSTRACTFluid flow and heat transfer in duct fan flows with a 90° rectangular-wing turbulator, mounted on the top duct wall, were experimentally studied and compared with the bottom-wall turbulator results. Threecomponent velocities were measured to characterize the flow structures and to obtain near-wall flow parameters. Temperatures on heat transfer surfaces were measured to obtain Nusselt number distributions. Results show that the turbulator has the effect to increase the near-wall axial mean velocity, axial vorticity and turbulent kinetic energy, and, consequently, augment the heat transfer. The axial mean velocity and axial vorticity play an influential role on the heat transfer distributions for the flows across the top-wall and bottom-wall turbulators, respectively.

Heat transfer enhancement of turbulent channel flow using tandem self-oscillating inverted flags

2018 ◽  
Vol 30 (7) ◽  
pp. 075108 ◽  
Author(s):  
Yujia Chen ◽  
Yuelong Yu ◽  
Wenwu Zhou ◽  
Di Peng ◽  
Yingzheng Liu
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Enhancement ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Transfer Enhancement

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.

Modelling smooth- and transitionally rough-wall turbulent channel flow by leveraging inner–outer interactions and principal component analysis

2019 ◽  
Vol 863 ◽  
pp. 407-453 ◽  
Author(s):  
Sicong Wu ◽  
Kenneth T. Christensen ◽  
Carlos Pantano
Keyword(s):  
Principal Component Analysis ◽  
Outer Layer ◽  
Principal Component ◽  
Turbulent Channel Flow ◽  
Reynolds Numbers ◽  
Wall Turbulence ◽  
Rough Wall ◽  
Smooth Wall ◽  
Wall Flow ◽  
Near Wall

Direct numerical simulations (DNS) of turbulent channel flow over rough surfaces, formed from hexagonally packed arrays of hemispheres on both walls, were performed at friction Reynolds numbers $Re_{\unicode[STIX]{x1D70F}}=200$, $400$ and $600$. The inner normalized roughness height $k^{+}=20$ was maintained for all Reynolds numbers, meaning all flows were classified as transitionally rough. The spacing between hemispheres was varied within $d/k=2$–$4$. The statistical properties of the rough-wall flows were contrasted against a complementary smooth-wall DNS at $Re_{\unicode[STIX]{x1D70F}}=400$ and literature data at $Re_{\unicode[STIX]{x1D70F}}=2003$ revealing strong modifications of the near-wall turbulence, although the outer-layer structure was found to be qualitatively consistent with smooth-wall flow. Amplitude modulation (AM) analysis was used to explore the degree of interaction between the flow in the roughness sublayer and that of the outer layer utilizing all velocity components. This analysis revealed stronger modulation effects, compared to smooth-wall flow, on the near-wall small-scale fluctuations by the larger-scale structures residing in the outer layer irrespective of roughness arrangement and Reynolds number. A predictive inner–outer model based on these interactions, and exploiting principal component analysis (PCA), was developed to predict the statistics of higher-order moments of all velocity fluctuations, thus addressing modelling of anisotropic effects introduced by roughness. The results show excellent agreement between the predicted near-wall statistics up to fourth-order moments compared to the original statistics from the DNS, which highlights the utility of the PCA-enhanced AM model in generating physics-based predictions in both smooth- and rough-wall turbulence.

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