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PIV-based pressure estimation in the canopy of urban-like roughness

PIV-based pressure estimation in the canopy of urban-like roughness
PIV-based pressure estimation in the canopy of urban-like roughness
In-plane velocity measurements from PIV are used to estimate the pressure field above and within the canopy of two staggered arrays of cuboids, with distinct height distributions, via 2D-RANS and 2D-TH. The viability of this approach is examined by first comparing the mean drag profiles against reported wind-tunnel measurements that were carried out under similar test conditions and numerical simulations (LES and DNS). The surface drag is extrapolated from the nearest data point surrounding the roughness elements. Second, estimates of the friction velocity and the zero-plane displacement height are obtained by integrating the axial pressure difference across each individual obstacle, assuming it is spanwise uniform. These are compared against direct measurements of the wall-shear stress from a floating-element balance and a pressure-tapped cube, as well as against estimates from indirect methods. In addition to mean pressure maps, snapshots of the pressure field are obtained via 2D-TH, based on Taylor’s Hypothesis, which are used to compute the RMS of the pressure fluctuations on the surface of a cube. The results indicate that 2D-RANS and 2D-TH perform adequately, providing reasonable estimates of the mean pressure distribution and of the boundary-layer flow parameters, outperforming indirect methods which rely on equilibrium assumptions that are often not verified.
Urban roughness, particle image velocimetry (PIV), pressure
0723-4864
Amaro Matoso Aguiar Ferreira, Manuel
bdfedfb9-85f6-4dbb-b031-a977cb8f145d
Ganapathisubramani, Bharathram
5e69099f-2f39-4fdd-8a85-3ac906827052
Amaro Matoso Aguiar Ferreira, Manuel
bdfedfb9-85f6-4dbb-b031-a977cb8f145d
Ganapathisubramani, Bharathram
5e69099f-2f39-4fdd-8a85-3ac906827052

Amaro Matoso Aguiar Ferreira, Manuel and Ganapathisubramani, Bharathram (2020) PIV-based pressure estimation in the canopy of urban-like roughness. Experiments in Fluids, 61 (70), [70]. (doi:10.1007/s00348-020-2904-1).

Record type: Article

Abstract

In-plane velocity measurements from PIV are used to estimate the pressure field above and within the canopy of two staggered arrays of cuboids, with distinct height distributions, via 2D-RANS and 2D-TH. The viability of this approach is examined by first comparing the mean drag profiles against reported wind-tunnel measurements that were carried out under similar test conditions and numerical simulations (LES and DNS). The surface drag is extrapolated from the nearest data point surrounding the roughness elements. Second, estimates of the friction velocity and the zero-plane displacement height are obtained by integrating the axial pressure difference across each individual obstacle, assuming it is spanwise uniform. These are compared against direct measurements of the wall-shear stress from a floating-element balance and a pressure-tapped cube, as well as against estimates from indirect methods. In addition to mean pressure maps, snapshots of the pressure field are obtained via 2D-TH, based on Taylor’s Hypothesis, which are used to compute the RMS of the pressure fluctuations on the surface of a cube. The results indicate that 2D-RANS and 2D-TH perform adequately, providing reasonable estimates of the mean pressure distribution and of the boundary-layer flow parameters, outperforming indirect methods which rely on equilibrium assumptions that are often not verified.

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Accepted/In Press date: 27 January 2020
Published date: 21 February 2020
Keywords: Urban roughness, particle image velocimetry (PIV), pressure

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Local EPrints ID: 438453
URI: http://eprints.soton.ac.uk/id/eprint/438453
ISSN: 0723-4864
PURE UUID: ed0f8094-7ac1-4ad4-bfa7-5dfb4e69cc02
ORCID for Manuel Amaro Matoso Aguiar Ferreira: ORCID iD orcid.org/0000-0002-2428-0284
ORCID for Bharathram Ganapathisubramani: ORCID iD orcid.org/0000-0001-9817-0486

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Date deposited: 10 Mar 2020 17:32
Last modified: 28 Apr 2022 02:27

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Author: Manuel Amaro Matoso Aguiar Ferreira ORCID iD

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