An analytical wall-function for turbulent flows and heat transfer over rough walls

Suga, Kazuhiko; Craft, Tim; Iacovides, Hector

doi:10.1016/j.ijheatfluidflow.2006.03.011

Cited by 75 publications

(51 citation statements)

References 41 publications

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“…The paper makes the premise (as in [11]) that this may be accomplished by making the non-dimensional zeroeddy-viscosity height y þ n a function of e k þ s (the equivalent-sand-roughness height k s in wall units). In contrast to that paper, however, the functional variation with e k þ s is here deduced more satisfactorily from asymptotic comparison with the log law.…”

Section: Discussionmentioning

confidence: 99%

“…(1) As in Suga et al [11], it should be a function of the non-dimensional roughness height e k þ s ¼ k se u t n ; (2) It should be chosen so as to provide the best asymptotic matching to an equilibrium mean-velocity profile (i.e., log law, with roughness dependence) as e y þ ! 1.…”

Section: 3mentioning

confidence: 99%

“…When the "tilde" version is employed, the two dimensionless heights are proportional: e y þ ¼ C 1=4 m y*. Recently, Suga et al [11] have extended the analytical wall function to rough boundaries. Roughness enters the wall function solely through y þ n , which is made an empirical function of e k þ s (equivalent sand roughness in wall units).…”

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confidence: 99%

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CFD Calculation of Turbulent Flow with Arbitrary Wall Roughness

Apsley

2007

Flow Turbulence Combust

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Wall functions are used in CFD calculations of turbulent flow to handle the noslip boundary condition at solid surfaces without an unacceptably-large number of grid cells. Recent work in the Turbulence Mechanics Group at the University of Manchester has been aimed at developing wall functions suitable for non-equilibrium flows -primarily for smooth walls. For environmental fluid-mechanics problems, however, there is a need to extend the new wall-function concepts to arbitrary wall roughness. Suga, Craft and Iacovides (Extending an analytical wall function for turbulent flows over rough walls, 6th International Symposium on Engineering Turbulence Modelling and Measurements, Sardinia, 2005) have shown how this may be achieved by making the zero-eddy-viscosity height, y n , a function of roughness. Their function is, however, largely empirical, and this paper goes further by demonstrating that the zero-eddy-viscosity height can be more satisfactorily determined by appealing to higher-order consistency with the log law. Roughness-dependent changes are also made to the near-wall prescription of the turbulent kinetic energy dissipation rate, e. The wall function for arbitrarily-rough surfaces is developed in detail and its ability to reproduce the Darcy friction factor in rough-walled pipes is tested. Some computational results for the more complex application which stimulated this work -sediment transport and sandbank morphodynamics -are then given. The application of the wall function to the Reynolds-stress transport equations and modifications for streamwise pressure gradients are also discussed.

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Section: Discussionmentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

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CFD Calculation of Turbulent Flow with Arbitrary Wall Roughness

Apsley

2007

Flow Turbulence Combust

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“…However, it is well known that wall-functions based on the semi-logarithmic variation of the near-wall velocity do not apply under non-equilibrium flow conditions including impingement and steep pressure gradients, as is apparent in the inclined impinging jet (see, e.g., [43,44]). In such cases, advanced wall treatments are typically used, e.g., generalized wall functions [45], analytical wall-functions [46,47] or numerical integration of boundary layer equations [48], that produce the required value of the wall shear stress over the near-wall cell. Such values are difficult to obtain experimentally.…”

Section: Near-wall Shear Stressmentioning

confidence: 99%

Database of Near-Wall Turbulent Flow Properties of a Jet Impinging on a Solid Surface under Different Inclination Angles

Ries

Rißmann

et al. 2018

Fluids

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Abstract:In the present paper, direct numerical simulation (DNS) and particle image velocimetry (PIV) have been applied complementarily in order to generate a database of near-wall turbulence properties of a highly turbulent jet impinging on a solid surface under different inclination angles. Thereby, the main focus is placed on an impingement angle of 45 • , since it represents a good generic benchmark test case for a wide range of technical fluid flow applications. This specific configuration features very complex flow properties including the presence of a stagnation point, development of the shear boundary layer and strong streamline curvature. In particular, this database includes near-wall turbulence statistics along with mean and rms velocities, budget terms in the turbulent kinetic energy equation, anisotropy invariant maps, turbulent length/time scales and near-wall shear stresses. These properties are useful for the validation of near-wall modeling approaches in the context of Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulations (LES). From this study, in which further impingement angles (0 • , 90 • ) have been considered in the experiments only, it turns out that (1) the production of turbulent kinetic energy appears negative at the stagnation point for an impingement angle other than 0 • and is balanced predominantly by pressure-related diffusion, (2) quasi-coherent thin streaks with large characteristic time scales appear at the stagnation region, while the organization of the flow is predominantly toroidal further downstream, and (3) near-wall shear stresses are low at the stagnation region and intense in regions where the direction of the flow changes suddenly.

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“…A wall-function-type approach will clearly be needed. In this connection it is noted that Suga et al [20] have recently extended the AWF scheme to study rough walls. The air-sea interface is, of course, much more complex than a rough rigid wall but at least that provides a starting point.…”

Section: Hurricane Modelling -The Next Grand Challenge?mentioning

confidence: 99%