Turbulent Structure of a Simplified Urban Fluid Flow Studied Through Stereoscopic Particle Image Velocimetry

Monnier, Bruno; Goudarzi, Sepehr A.; Vinuesa, Ricardo; Wark, Candace

doi:10.1007/s10546-017-0303-9

Cited by 21 publications

(25 citation statements)

References 62 publications

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“…For urban type roughness, similar channeling and skimming flow regimes have been observed (see e.g. [42]) and can have a strong effect on the urban microclimate [43]. The spanwise normal Reynolds stress u 2 u 2 , shown in Fig.…”

Section: Reynolds and Dispersive Stressessupporting

confidence: 62%

Influence of Surface Anisotropy on Turbulent Flow Over Irregular Roughness

Busse

Jelly

2019

Flow Turbulence Combust

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The influence of surface anisotropy upon the near-wall region of a rough-wall turbulent channel flow is investigated using direct numerical simulation (DNS). A set of nine irregular rough surfaces with fixed mean peak-to-valley height, near-Gaussian height distributions and specified streamwise and spanwise correlation lengths were synthesised using a surface generation algorithm. By defining the surface anisotropy ratio (SAR) as the ratio of the streamwise and spanwise correlation lengths of the surface, we demonstrate that surfaces with a strong spanwise anisotropy (SAR <1) can induce an over 200% increase in the roughness function ΔU + , compared to their streamwise anisotropic (SAR >1) equivalent. Furthermore, we find that the relationship between the roughness function ΔU + and the SAR parameter approximately follows an exponentially decaying function. The statistical response of the near-wall flow is studied using a "double-averaging" methodology in order to distinguish form-induced "dispersive" stresses from their turbulent counterparts. Outer-layer similarity is recovered for the mean velocity defect profile as well as the Reynolds stresses. The dispersive stresses all attain their maxima within the roughness canopy. Only the streamwise dispersive stress reaches levels that are comparable to the equivalent Reynolds stress, with surfaces of high SAR attaining the highest levels of streamwise dispersive stress. The Reynolds stress anisotropy also shows distinct differences between cases with strong streamwise anisotropy that stay close to an axisymmetric, rodlike state for all wall-normal locations, compared to cases with spanwise anisotropy where an axisymmetric, disk-like state of the Reynolds stress anisotropy tensor is observed around the roughness mean plane. Overall, the results from this study underline that the drag penalty incurred by a rough surface is strongly influenced by the surface topography and highlight its impact upon the mean momentum deficit in the outer flow as well as the Reynolds and dispersive stresses within the roughness layer.

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Section: Reynolds and Dispersive Stressessupporting

confidence: 62%

Influence of Surface Anisotropy on Turbulent Flow Over Irregular Roughness

Busse

Jelly

2019

Flow Turbulence Combust

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“…The vast majority of canopy flow experiments provide Eulerian-based measurements, estimating statistics at fixed points in space, for example, by hot wire or LDA 3,[7][8][9][10][11][12][13][14][15] , by PIV [16][17][18][19][20] or stereoscopic PIV 21 . Obtaining Lagrangian flow parameters from Eulerian measurements requires assumptions with questionable validity in inhomogeneous flows.…”

Section: Introductionmentioning

confidence: 99%

Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

Shnapp

Shapira²,

Peri

et al. 2019

Sci Rep

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Direct estimation of Lagrangian turbulence statistics is essential for the proper modeling of dispersion and transport in highly obstructed canopy flows. However, Lagrangian flow measurements demand very high rates of data acquisition, resulting in bottlenecks that prevented the estimation of Lagrangian statistics in canopy flows hitherto. We report on a new extension to the 3D Particle Tracking Velocimetry (3D-PTV) method, featuring real-time particle segmentation that outputs centroids and sizes of tracer particles and performed on dedicated hardware during high-speed digital video acquisition from multiple cameras. The proposed extension results in four orders of magnitude reduction in data transfer rate that enables to perform substantially longer experimental runs, facilitating measurements of convergent statistics. The extended method is demonstrated through an experimental wind tunnel investigation of the Lagrangian statistics in a heterogeneous canopy flow. We observe that acceleration statistics are affected by the mean shear at the top of the canopy layer and that Lagrangian particle dispersion at small scales is dominated by turbulence in the wake of the roughness elements. This approach enables to overcome major shortcomings from Eulerian-based measurements which rely on assumptions such as the Taylor’s frozen turbulence hypothesis, which is known to fail in highly turbulent flows.

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“…where D is a small integration domain centered around point x p , u(x) is the velocity vector, × denotes vector product and | • | is the vector modulus. This method was used by Monnier et al [87] to identify the various relevant regions in the flow between two obstacles, as can be observed in Figure 10. On the leeward side of the upstream obstacle the flow exhibits the arch vortex, with the two legs having flow rotating in the wall-normal direction and the roof exhibiting flow rotating in the spanwise direction.…”

Section: Flow Structures and Data-driven Methodsmentioning

confidence: 99%

“…The characterization of the flow in realistic urban environments is mainly motivated by studying the influence on small-scale fluctuations of atmospheric flow. One important aspect of flow characterization in realistic urban environments is the effect of small-scale fluctuations, which could be produced (among other factors) by the presence of high-rise buildings; it is important to note that in some cases these can produce gusty winds (Monnier et al [87]). The origin of such turbulence fluctuations could be produced (among other factors) by the presence of high-rise buildings, which in some cases produce gusty winds.…”

Section: Numerical Studiesmentioning

confidence: 99%

On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

2021

Self Cite

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Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

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Turbulent Structure of a Simplified Urban Fluid Flow Studied Through Stereoscopic Particle Image Velocimetry

Cited by 21 publications

References 62 publications

Influence of Surface Anisotropy on Turbulent Flow Over Irregular Roughness

Influence of Surface Anisotropy on Turbulent Flow Over Irregular Roughness

Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

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