Influence of neighbouring structures on building façade pressures: comparison between full-scale, wind-tunnel, CFD and practitioner guidelinesGough, H., King, M.-F., Nathan, P., Grimmond, C. S. B. ORCID: https://orcid.org/0000-0002-3166-9415, Robins, A., Noakes, C. J., Luo, Z. ORCID: https://orcid.org/0000-0002-2082-3958 and Barlow, J. F. (2019) Influence of neighbouring structures on building façade pressures: comparison between full-scale, wind-tunnel, CFD and practitioner guidelines. Journal of Wind Engineering and Industrial Aerodynamics, 189. pp. 22-33. ISSN 0167‐6105
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1016/j.jweia.2019.03.011 Abstract/SummaryFaçade pressure coefficients are widely used to determine wind-driven ventilation potential for buildings at the design stage. Over nine months we measured façade pressure coefficients under full-scale conditions using the 6 m Silsoe cube in isolation and in a staggered array. Results are compared against a 1:300 wind-tunnel model, a time-dependent computational fluid dynamics (CFD) model at full-scale and to published pressure coefficients in ventilation design guidance for a range of wind angles. Across all wind angles, wind-tunnel, CFD and published models tended to underestimate full-scale experimental pressure coefficients in magnitude but replicated trends well for a single face on the isolated cube. Agreement was weaker for the array; pressure coefficients are asymmetric with wind direction and results sensitive to model set up and measurement strategies. Differences in pressure coefficient across the building compared well in both isolated and array cases, suggesting this is a more robust parameter for models than individual facet data. It is recommended that building symmetry and surrounding areas should be considered when relying on ventilation guidelines. Scale and computational models are effective to support design for more complex cases; however, it is important to ensure measurement locations are representative and that uncertainties are quantified.
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