Direct Numerical Simulation of Turbulent Flow Around a Surface Mounted Cube

Curley, Alex; Uddin, Mesbah; Peters, Brett

doi:10.2514/6.2015-3431

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…It is known that approximately 140 X. I. A. Yang, J. Sadique, R. Mittal and C. Meneveau C DH ≈ 1.4 (Akins, Peterka & Cermak 1977;Hussain & Lee 1980;Curley & Uddin 2015). Essentially we are assuming that the drag coefficient C DH appropriate for an unsheltered or 'just sheltered' cuboid in the array is equal to that of an isolated element.…”

Section: Assumed Shape Function For Mean Velocity Profilementioning

confidence: 97%

Exponential roughness layer and analytical model for turbulent boundary layer flow over rectangular-prism roughness elements

et al. 2016

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We conduct a series of large-eddy simulations (LES) to examine the mean flow behaviour within the roughness layer of turbulent boundary layer flow over rough surfaces. We consider several configurations consisting of arrays of rectangular-prism roughness elements with various spacings, aspect ratios and height distributions. The results provide clear evidence for exponential behaviour of the mean flow with respect to the wall normal distance. Such behaviour has been proposed before (see, e.g., Cionco, 1966 Tech. Rep. DTIC document), and is represented asis the spatially/temporally averaged fluid velocity, z is the wall normal distance, h represents the height of the roughness elements and U h is the velocity at z = h. The attenuation factor a depends on the density of the roughness element distribution and details of the roughness distribution on the wall. Once established, the generic velocity profile shape is used to formulate a fully analytical model for the effective drag exerted by turbulent flow on a surface covered with arrays of rectangular-prism roughness elements. The approach is based on the von Karman-Pohlhausen integral method, in which a shape function is assumed for the mean velocity profile and its parameters are determined based on momentum conservation and other fundamental constraints. In order to determine the attenuation parameter a, wake interactions among surface roughness elements are accounted for by using the concept of flow sheltering. The model transitions smoothly between 'k' and 'd' type roughness conditions depending on the surface coverage density and the detailed geometry of roughness elements. Comparisons between model predictions and experimental/numerical data from the existing literature as well as LES data from this study are presented. It is shown that the analytical model provides good predictions of mean velocity and drag forces for the cases considered, thus raising the hope that analytical roughness modelling based on surface geometry is possible, at least for cases when the location of flow separation over surface elements can be easily predicted, as in the case of wall-attached rectangular-prism roughness elements.

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Section: Assumed Shape Function For Mean Velocity Profilementioning

confidence: 97%

Exponential roughness layer and analytical model for turbulent boundary layer flow over rectangular-prism roughness elements

et al. 2016

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“…Using typically measured values for C d = 1 [15] and C dh = 0.7 [62][63][64], the attenuation factor for an isolated roughness element is a 0 ≈ 0.4, as shown in [17]. Substituting into equation (3.17) leads to…”

Section: (A) Flow Shelteringmentioning

confidence: 98%

“…Substituting equation (3.16) into equation (3.18), the attenuation coefficient for an unsheltered rectangular roughness element can be solved for. Using typically measured values for C d = 1 [15] and C dh = 0.7 [62][63][64], the attenuation factor for an isolated roughness element is a 0 ≈ 0.4, as shown in [17]. Substituting into equation For an estimate of β n , we use equation (3.16) with a = a 0 : (d) Sheltering among roughness elements of the same size So far we have neglected the sheltering among roughness elements of the same size.…”

Section: (C) Flow In the Near-wall Regionmentioning

confidence: 99%

Modelling turbulent boundary layer flow over fractal-like multiscale terrain using large-eddy simulations and analytical tools

Yang

Meneveau

2017

Phil. Trans. R. Soc. A.

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In recent years, there has been growing interest in large-eddy simulation (LES) modelling of atmospheric boundary layers interacting with arrays of wind turbines on complex terrain. However, such terrain typically contains geometric features and roughness elements reaching down to small scales that typically cannot be resolved numerically. Thus subgrid-scale models for the unresolved features of the bottom roughness are needed for LES. Such knowledge is also required to model the effects of the ground surface ‘underneath’ a wind farm. Here we adapt a dynamic approach to determine subgrid-scale roughness parametrizations and apply it for the case of rough surfaces composed of cuboidal elements with broad size distributions, containing many scales. We first investigate the flow response to ground roughness of a few scales. LES with the dynamic roughness model which accounts for the drag of unresolved roughness is shown to provide resolution-independent results for the mean velocity distribution. Moreover, we develop an analytical roughness model that accounts for the sheltering effects of large-scale on small-scale roughness elements. Taking into account the shading effect, constraints from fundamental conservation laws, and assumptions of geometric self-similarity, the analytical roughness model is shown to provide analytical predictions that agree well with roughness parameters determined from LES. This article is part of the themed issue ‘Wind energy in complex terrains’.

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“…where C D is a drag coefficient associated with the roughness elements, and is here estimated as C D ≈ 1.4 (Akins, Peterka & Cermak 1977;Hussain & Lee 1980;Curley & Uddin 2015). In the absence of a pre-defined C D , it would also be acceptable to impose C D → 1 as per the inertia-dominated (fully rough) flow conditions.…”

Section: This Studymentioning

confidence: 99%