Effect of Roughness on Wall-Bounded Turbulence

Bhaganagar, Kiran; Kim, John; Coleman, Gary N.

doi:10.1023/b:appl.0000044407.34121.64

Cited by 169 publications

(139 citation statements)

References 22 publications

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“…Given the relatively large size of the roughness in the present case, this is perhaps a little surprising. It is also in direct contrast to the findings of Bhaganagar et al (2004) who, from DNS of a channel flow with one . Values of λ p in the legends with symbols identifying each curve.…”

Section: The Turbulence Fieldcontrasting

confidence: 87%

“…With the increasing levels of computer power now available, a number of authors have employed direct numerical simulation (DNS) and large eddy simulation (LES) to explore flows over arrays of sharp-edged elements - Stoesser et al (2003), Kanda, Moriwaki & Kasamatsu (2004), Coceal et al (2006Coceal et al ( , 2007b, Coceal, Thomas & Belcher (2007a), Xie & Castro (2006), Xie, Coceal & Castro (2008) and provide the majority of such studies, but see also Bhaganagar, Kim & Coleman (2004) for DNS over roughness characterized by elements not having sharp edges. These have all been of single (or sometimes two) specific roughness arrays but they have shown unequivocally that such approaches are far superior to RANS methods and can be used successfully to explore the detailed nature of the flow within and above the canopy region.…”

Section: Introductionmentioning

confidence: 99%

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Channel flow over large cube roughness: a direct numerical simulation study

2010

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Computations of channel flow with rough walls comprising staggered arrays of cubes having various plan area densities are presented and discussed. The cube height h is 12.5 % of the channel half-depth and Reynolds numbers (u τ h/ν) are typically around 700 -well into the fully rough regime. A direct numerical simulation technique, using an immersed boundary method for the obstacles, was employed with typically 35 million cells. It is shown that the surface drag is predominantly form drag, which is greatest at an area coverage around 15 %. The height variation of the axial pressure force across the obstacles weakens significantly as the area coverage decreases, but is always largest near the top of the obstacles. Mean flow velocity and pressure data allow precise determination of the zero-plane displacement (defined as the height at which the axial surface drag force acts) and this leads to noticeably better fits to the log-law region than can be obtained by using the zero-plane displacement merely as a fitting parameter. There are consequent implications for the value of von Kármán's constant. As the effective roughness of the surface increases, it is also shown that there are significant changes to the structure of the turbulence field around the bottom boundary of the inertial sublayer. In distinct contrast to twodimensional roughness (longitudinal or transverse bars), increasing the area density of this three-dimensional roughness leads to a monotonic decrease in normalized vertical stress around the top of the roughness elements. Normalized turbulence stresses in the outer part of the flows are nonetheless very similar to those in smooth-wall flows.

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Section: The Turbulence Fieldcontrasting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Channel flow over large cube roughness: a direct numerical simulation study

2010

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“…Krogstad & Antonia (1994) later demonstrated that these observations could be attributed to a rotation of the large-scale structure towards the wall-normal axis relative to the smooth-wall case. These observations were corroborated by the particleimage velocimetry results of Keirsbulck et al (2002) and the direct numerical channel simulations of Bhaganagar, Kim & Coleman (2004), with k/δ = 3.8 and 5.4 %, respectively. Antonia & Krogstad (2001) conducted measurements in the boundary layers developed over two walls with different roughnesses but with matching U + , and found significant differences in the observed turbulence characteristics in the outer layer, though the surfaces had similar values of k/δ ≈ 1.8 and 2.1 %.…”

Section: Introductionsupporting

confidence: 65%

“…Antonia & Krogstad (2001) conducted measurements in the boundary layers developed over two walls with different roughnesses but with matching U + , and found significant differences in the observed turbulence characteristics in the outer layer, though the surfaces had similar values of k/δ ≈ 1.8 and 2.1 %. However, the combined experimental and numerical study of Krogstad et al (2005) found that, for a rough-wall channel with k/δ = 3.4 % and a surface topology similar to the transverse grooves of Bhaganagar et al (2004), the influence of the roughness did not extend beyond approximately five roughness heights from the surface, and the outer layer was unaffected. The authors attributed the apparent discrepancy to fundamental differences between channel and boundary layer flows.…”

Section: Introductionmentioning

confidence: 99%

“…While k/δ appears to provide a meaningful measure of the degree of inner-outer interaction, the results of Wu & Christensen (2007) suggest that bulk-averaged geometric measures of the roughness (such as the mean surface height or the root-mean-square roughness height) only poorly characterize the influence of the roughness. By expressing the roughness size in terms of the equivalent sandgrain roughness, k s (which is associated with the total losses in fully developed turbulent pipe flow rather than any geometric parameter, Nikuradse 1933), Wu & Christensen showed that the relative roughnesses, k s /δ, of Krogstad et al (1992), Keirsbulck et al (2002) and Bhaganagar et al (2004) were 6.7, 13 and 13 %, respectively, and so that the application of the Townsend (1976) scaling assumptions may have been inappropriate in these cases. For the turbulent channel case of Krogstad et al (2005), Wu & Christensen noted that k s /δ was as high as 25 %, rendering the lack of observed effects of the roughness upon the turbulent flow structures even more surprising, regardless of boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

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Similarity of the streamwise velocity component in very-rough-wall channel flow

Birch

Morrison

2010

J. Fluid Mech.

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The streamwise velocity component is studied in fully developed turbulent channel flow for two very rough surfaces and a smooth surface at comparable Reynolds numbers. One rough surface comprises sparse and isotropic grit with a highly nonGaussian distribution. The other is a uniform mesh consisting of twisted rectangular elements which form a diamond pattern. The mean roughness heights (± the standard deviation) are, respectively, about 76(±42) and 145(±150) wall units. The flow is shown to be two-dimensional and fully developed up to the fourth-order moment of velocity. The mean velocity profile over the grit surface exhibits self-similarity (in the form of a logarithmic law) within the limited range of 0.04 6 y/ h 6 0.06, but the profile over the mesh surface does not, even though the mean velocity deficit and higher moments (up to the fourth order) all exhibit outer scaling over both surfaces. The distinction between self-similarity and outer similarity is clarified and the importance of the former is explained. The wake strength is shown to increase slightly over the grit surface but decrease over the mesh surface. The latter result is contrary to recent measurements in rough-wall boundary layers. Single-and twopoint velocity correlations reveal the presence of large-scale streamwise structures with circulation in the plane orthogonal to the mean velocity. Spanwise correlation length scales are significantly larger than corresponding ones for both internal and external smooth-wall flows.

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An immersed boundary method wall model for high‐Reynolds‐number channel flow over complex topography

Anderson

2012

Numerical Methods in Fluids

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SUMMARY High‐Reynolds‐number channel flows regularly encounter topographies composed of multiple length scales and that protrude into the boundary layer. Physically, the presence of immersed obstacles leads to increased velocity gradients, turbulence production, and manifestation of wakes. Considerable challenges are associated with numerically describing the presence of obstacles in channel flows. Common approaches include generation of a computational mesh that is uniquely designed for the flow and obstacle, the immersed boundary method, and terrain‐following coordinates. There are challenges and limitations associated with each of these techniques. Specification of boundary conditions representing the perimeter of solid obstacles is a primary challenge of the immersed boundary method. In this document, a simplistic canopy stress‐like wall model is used to impose boundary conditions. The model isolates aerodynamically relevant local frontal areas through evaluation of the gradient of the topographic height field. The gradient of the height field describes both the surface‐normal direction and the frontal area, making it ideal for detecting areas on which the flow impinges. The model is tested in numerical simulations of turbulent half‐channel flow over topographies with different obstacles affixed–right prisms, rectangular prisms, ellipsoidal mounds, and sinusoids. In all cases, the performance is strong relative to datasets presented in the literature. Results are finally presented for numerical simulation of flow over complex synthetic fractal‐like topography and a synthetic city. These results show interesting trends in how the turbulent multiscale flow field responds to multiscale topography. Copyright © 2012 John Wiley & Sons, Ltd.

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Effect of Roughness on Wall-Bounded Turbulence

Cited by 169 publications

References 22 publications

Channel flow over large cube roughness: a direct numerical simulation study

Channel flow over large cube roughness: a direct numerical simulation study

Similarity of the streamwise velocity component in very-rough-wall channel flow

An immersed boundary method wall model for high‐Reynolds‐number channel flow over complex topography

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