Concentration and flow distributions in urban street canyons: wind tunnel and computational data
The goal of this paper is to present bluff body flow and transport from steady point sources of pollutants, or chemical and biological agents in an idealized urban environment This paper includes ventilation behavior in different street canyon configurations. To evaluate dispersion in a model urban...
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Veröffentlicht in: | Journal of wind engineering and industrial aerodynamics 2003-09, Vol.91 (9), p.1141-1154 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The goal of this paper is to present bluff body flow and transport from steady point sources of pollutants, or chemical and biological agents in an idealized urban environment This paper includes ventilation behavior in different street canyon configurations. To evaluate dispersion in a model urban street canyon, a series of tests with various street canyon aspect ratios (
B/
H) are presented. Both open-country roughness and urban roughness cases are considered. The flow and dispersion of gases emitted by a point source located between two buildings inside an urban street canyon were determined by the prognostic model FLUENT using four different RANS turbulent closure approximations and in the model fire dynamics simulator using a large eddy simulation methodology. Calculations are compared against fluid modeling in the Industrial Meteorological Wind Tunnel at Colorado State University. A basic building shape, the Wind Engineering Research Field Laboratory building (WERFL) at Texas Tech University, was used for this study. The urban street canyon was represented by a 1:50 scale WERFL model surrounded by models of similar dimensions. These buildings were arranged in various symmetric configurations with different separation distances and different numbers of up- or downwind buildings. Measurements and calculations reveal the dispersion of gases within the urban environment are essentially unsteady, and they are not always well predicted by the use of steady-state prediction methodologies. |
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ISSN: | 0167-6105 1872-8197 |
DOI: | 10.1016/S0167-6105(03)00056-4 |