Defining the roughness sublayer and its turbulence statistics

Florens, Emma; Eiff, Olivier; Moulin, F.

doi:10.1007/s00348-013-1500-z

Cited by 48 publications

(73 citation statements)

References 35 publications

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“…For lower Reynolds numbers, the roughness sublayer has a lower thickness. This estimated thickness of the roughness sublayer for the current surfaces is of comparable magnitude to measurements of the roughness sublayer thickness in experiments of turbulent flows over dense cubic roughness (Cheng & Castro 2002;Florens et al 2013), where an extent of the roughness sublayer of ≈0.75-1.2 roughness heights above the roughness canopy was found.…”

Section: Roughness Sublayersupporting

confidence: 81%

“…The thickness of the roughness sublayer can be measured based on the dispersive stresses of the flow field (Pokrajac et al 2007;Florens et al 2013), u i = u i − u i xy , i.e. the difference between the local time-averaged value of a velocity component and its time-and plane-averaged value.…”

Section: Roughness Sublayermentioning

confidence: 99%

“…Busse, M. Thakkar and N. D. Sandham Closer to the rough surface, the time-averaged velocity field shows significant spatial inhomogeneities induced by the presence of the surface roughness (Bottema 1996;Florens, Eiff & Moulin 2013). This part of the flow is called the roughness sublayer.…”

mentioning

confidence: 99%

“…Cheng & Castro 2002) or bar and cubic roughness in open channels (see e.g. Pokrajac et al 2007;Florens et al 2013). The roughness sublayer typically extends up to a height of two to five times the roughness height into the flow (Raupach, Antonia & Rajagopalan 1991).…”

mentioning

confidence: 99%

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Reynolds-number dependence of the near-wall flow over irregular rough surfaces

2016

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The Reynolds-number dependence of turbulent channel flow over two irregular rough surfaces, based on scans of a graphite and a grit-blasted surface, is studied by direct numerical simulation. The aim is to characterise the changes in the flow in the immediate vicinity of and within the rough surfaces, an area of the flow where it is difficult to obtain experimental measurements. The average roughness heights and spatial correlation of the roughness features of the two surfaces are similar, but the two surfaces have a significant difference in the skewness of their height distributions, with the graphite sample being positively skewed (peak-dominated) and the grit-blasted surface being negatively skewed (valley-dominated). For both cases, numerical simulations were conducted at seven different Reynolds numbers, ranging from Re τ = 90 to Re τ = 720. The positively skewed surface gives rise to higher friction factors than the negatively skewed surface in all cases. For the highest Reynolds numbers, the flow has values of the roughness function U + well in excess of 7 for both surfaces and the bulk flow profile has attained a constant shape across the full height of the channel except for the immediate vicinity of the roughness, which would indicate fully rough flow. However, the mean flow profile within and directly above the rough surface still shows considerable Reynolds-number dependence and the ratio of form to viscous drag continues to increase, which indicates that at least for some types of rough surfaces the flow retains aspects of the transitionally rough regime to values of U + or k + well in excess of the values conventionally assumed for the transitionally to fully rough threshold. This is also reflected in the changes that the near-wall flow undergoes as the Reynolds number increases: the viscous sublayer, within which the surface roughness is initially buried, breaks down and regions of reverse flow intensify. At the highest Reynolds numbers, a layer of near-wall flow is observed to follow the contours of the local surface. The distribution of thickness of this 'blanketing' layer has a mixed scaling, showing that viscous effects are still significant in the near-wall flow.

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Section: Roughness Sublayersupporting

confidence: 81%

Section: Roughness Sublayermentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Reynolds-number dependence of the near-wall flow over irregular rough surfaces

2016

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“…Figure 8 shows the different possible configurations for each of the three length scales: s, k and D. To determine experimentally l 0 , the flow rate by integrating the calculated vertical velocity profile (steps 3c, 3d and 3e) is compared to the measured flow rate with an optimization method (simplex algorithm from Matlab). For all experiments from literature (Kouwen and Unny, 1969;Meijer and Velzen, 1999;Lopez and Garcia, 2001;Righetti and Armanini, 2002;Poggi et al, 2004;Jarvela, 2005;Ghisalberti and Nepf, 2006;Murphy and Nepf, 2007;Kubrak et al, 2008;Nezu and Sanjou, 2008;Huai et al, 2009;Yang and Choi, 2009;Florens et al, 2013) (2008) are reused, together with those obtained specifically for the present study. As indicated by Konings et al (2012), the experiments carried out with leafy vegetation behaved in a particular way because of viscosity terms.…”

Section: Submerged Conditionsmentioning

confidence: 99%

Design of emergent and submerged rock-ramp fish passes

Cassan

Laurens

2016

Knowl. Manag. Aquat. Ecosyst.

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-An analytical model is developed to calculate the stage-discharge relationship for emergent and submerged rock-ramp fish passes. A previous model has been modified and simplified to be adapted to a larger range of block arrangement. For submerged flows, a two-layer model developed for aquatic canopies is used. A turbulent length scale is proposed to close the turbulence model thanks to a large quantity of data for fully rough flows from the literature and experiments. This length scale depends only on the characteristic lengths of arrangements of obstacles. Then the coefficients of the logarithmic law above the canopy can also be deduced from the model. As a consequence, the total discharge through the fish pass is computed by integrating the vertical velocity profiles. A good fit is found between the model and commonly observed values for fish pass or a vegetated canopy. The discharge of the fish pass is then accurately estimated for a large range of hydraulic conditions, which could be useful for estimating fish passability through the structure.

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