A DNS of a Turbulent Flow in a Rough-Wall Channel Using Roughness Elements Model.

Miyake, Yutaka; Tsujimoto, Koichi; Agata, Yasuaki

doi:10.1299/jsmeb.43.233

Cited by 25 publications

(11 citation statements)

References 17 publications

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“…Here, c D is a drag coefficient, and A is a representative area (per unit volume). This form of momentum sink has been used in a variety of studies where the roughness elements are completely unresolved [24][25][26]. A drag term representing the effect of unresolved terrain is investigated in [27].…”

Section: Introductionmentioning

confidence: 99%

“…(1). The drag coefficient c D = 0.2 is prescribed, mainly to avoid numerical stability issues they found to be associated with c D = 0.5 used by [25]. In each of the above studies, c D (or the product c D A) was either tuned specifically for the flow under investigation, or it was prescribed in an ad hoc fashion (e.g., to avoid numerical stability issues).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling turbulent flow over fractal trees with renormalized numerical simulation

Chester

Meneveau

Parlange

2007

Journal of Computational Physics

131

129

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High-Reynolds number flow over tree-like fractals is considered, with emphasis on the drag forces produced. Fractal objects display large scale-disparity and complexity while being amenable to a simple and standardized description. Hence, they offer an elegant idealization of the actual boundaries in practical applications where turbulence interacts with boundaries that are characterized by multiple length-scales. First, using large-eddy-simulation of flow over prefractal shapes with increasing numbers of branch generations, the dependence of the tree drag on the inner cutoff-scale of the fractal is studied. It is found that the convergence of the drag coefficient towards a value that is independent of inner cutoff-scale is very slow. In order to address this fundamental difficulty and avoid the need to resolve all the small-scale branches of the fractal, a new numerical modeling technique called renormalized numerical simulation (RNS) is introduced. RNS models the drag of the unresolved branches using drag coefficients measured from both resolved branches and unresolved branches as modeled in previous iterations of the procedure. The RNS technique and its convergence properties are tested by means of a series of simulations using different levels of resolution. Then, RNS is used to investigate the influence of the tree fractal dimension on the drag coefficient. The increase of the drag with fractal dimension is quantified for two types of tree geometry, in two flow configurations. Results illustrate that RNS enables numerical modeling of physical processes associated with fractal geometries using affordable computational resolution.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling turbulent flow over fractal trees with renormalized numerical simulation

Chester

Meneveau

Parlange

2007

Journal of Computational Physics

131

129

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“…An attractive feature of this flow configuration from a computational point of view is that periodic boundary conditions can be used in the streamwise and spanwise directions. In the pioneering study of Miyake, Tsujimoto & Agata (2000), sand-grain roughness was represented by simplified roughness elements on one of the channel walls. Shortly thereafter, DNS of turbulent channel flow with transverse bar roughness were performed by Miyake, Tsujimoto & Masaru (2001), Ikeda & Durbin (2002), Leonardi et al (2003Leonardi et al ( , 2004 and Nagano, Hatori & Houra (2004).…”

Section: Introductionmentioning

confidence: 99%

An experimental and numerical study of channel flow with rough walls

et al. 2005

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A fully turbulent channel flow with smooth and rod-roughened walls has been investigated using hot-wire anemometry and direct numerical simulations (DNS). The mean flow follows the law of the wall for both surfaces and the velocity defect suggests that the outer layer is very little affected by the roughness. The Reynolds stresses appear to be very similar for the two surface geometries outside $y\,{\approx}\,5k$, where $k$ is the roughness height. A quadrant analysis shows that the structural differences close to the wall extend somewhat further out. The turbulence structure is further investigated using stress ratios and the anisotropy tensor, which corroborate the findings from the Reynolds stresses. Many of the recent investigations on boundary layers seem to find large differences between smooth and rough wall data in the outer layer also. A tentative explanation for the apparent dependence on flow type of the surface roughness effects is given.

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“…In the latter case, the boundary-layer thickness is a significant fraction of the total flow depth [13]. Some researchers computed rough surface flow using direct numerical simulation (DNS) [19][20][21]. DNS suffers the limitation of low Reynolds numbers and hence uncertain scale effects.…”

Section: Introductionmentioning

confidence: 99%

Large Eddy Simulation of Near-Bed Flow and Turbulence over Roughness Elements in the Shallow Open-Channel

Zeng

2020

Water

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Turbulent flows in rough open-channels have complex structures near the channel-bed. The near-bed flow can cause bed erosion, channel instability, and damages to fish habitats. This paper aims to improve our understanding of the structures. Transverse square bars placed at the channel-bed form two-dimensional roughness elements. Turbulent flows over the bars are predicted using large eddy simulation (LES). The predicted flow quantities compare well with experimental data. The LES model predicts mean-flow velocity profiles that resemble those in the classic turbulent boundary layer over a flat plate and profiles that change patterns in the vicinity of roughness elements, depending on the pitch-to-roughness height ratio λ/k. The relative turbulence intensity and normalized Reynolds shear stress reach maxima of 15% and 1.2%, respectively, at λ/k = 8, compared to 9% and 0.2% at λ/k = 2. The predicted bottom boundary layers constitute a large portion of the total depth, indicating roughness effect on the flow throughout the water column. Fluid exchange between the roughness cavity and outer region occurs due to turbulence fluctuations. The fluctuations increase in intensity with increasing λ/k ratio. This ratio dictates the number of eddies in the cavity as well as their locations and shapes. It also controls turbulence stress distributions. LES can be used to explore strategies for erosion control, channel restoration, and habitat protection.

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A DNS of a Turbulent Flow in a Rough-Wall Channel Using Roughness Elements Model.

Cited by 25 publications

References 17 publications

Modeling turbulent flow over fractal trees with renormalized numerical simulation

Modeling turbulent flow over fractal trees with renormalized numerical simulation

An experimental and numerical study of channel flow with rough walls

Large Eddy Simulation of Near-Bed Flow and Turbulence over Roughness Elements in the Shallow Open-Channel

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