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Improving the CFD modelling of cross-ventilation in highly-packed urban areas

Shirzadi, Mohammadreza; Naghashzadegan, Mohammad; Mirzaei, Parham

Authors

Mohammadreza Shirzadi

Mohammad Naghashzadegan



Abstract

Computational Fluid Dynamics (CFD) simulations are widely used in many wind-related urban morphology studies in urban areas, including cross-ventilation. The accuracy of the CFD models, however, is still a challenging issue for accurate prediction of the complex flow behavior around and inside the buildings. The RANS models, although broadly preferred, provide poor results in predicting the cross-ventilation in street canyons.

Thus, this study aims to understand and quantify limitations of the steady RANS models for cross-ventilation applications in highly-packed urban areas. To this end, a series of experimentally validated CFD simulations were conducted for a group of buildings, which were arranged in regular and staggered orders with different urban area densities. The improvement possibility of the RANS model accuracy was investigated using a parametric sensitivity study over the closure coefficients, and consequently a series of new closure coefficients were found for urban area densities between 0.25 and 0.4. Furthermore, as an interesting finding of this study, CFD results for urban area densities above 0.4 showed a high discrepancy compared to the expected measurement ranges for surface wind pressure; this implies that the CFD modelling of higher density urban areas should be treated with more caution and thus further studies are required to develop a guideline for such applications.

Journal Article Type Article
Publication Date 2018-02
Journal Sustainable Cities and Society
Print ISSN 2210-6707
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 37
Pages 451-465
APA6 Citation Shirzadi, M., Naghashzadegan, M., & Mirzaei, P. (2018). Improving the CFD modelling of cross-ventilation in highly-packed urban areas. Sustainable Cities and Society, 37, 451-465. https://doi.org/10.1016/j.scs.2017.11.020
DOI https://doi.org/10.1016/j.scs.2017.11.020
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S2210670717312891

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