Exceedance Probability Assessment of Pedestrian Wind Environment Based on Multiscale Coupling Numerical Simulation
Publication: Journal of Aerospace Engineering
Volume 33, Issue 4
Abstract
This paper presents exceedance probability assessment, which is a statistical evaluation method for pedestrian wind environment based on numerical simulations with multiscale coupling techniques. The Meixi Lake community in Changsha, China, was selected as a case study to demonstrate the assessment procedure. First, two numerical models, the large domain model (LDM) and small domain model (SDM), were established for large eddy simulations (LES). Second, the multiscale coupling technique, which combined weather research forecasting (WRF) and computation fluid dynamics (CFD), was used to obtain the wind inlet boundary for LDM. Further coupling between LDM and SDM was conducted to obtain the detailed wind field distribution at the inlet boundary for SDM by using the polynomial fitting method. Finally, the exceedance probability assessment of the pedestrian wind environment in the sample community was demonstrated on the basis of joint probability density distribution function of the wind speed and wind direction obtained from historical meteorological data. Results show that multiscale coupling and high-precision interpolation techniques can provide reasonable inlet velocity boundary conditions, and the rationality was successfully verified by the measured data. Furthermore, reasonable inlet boundaries can be obtained to evaluate the pedestrian wind environment using the multiscale coupling technique. The uncomfortable wind environment usually occurs outside groupings of buildings and near high-rise buildings. This assessment framework can serve as a practical tool to evaluate pedestrian safety and comfort among grouped buildings in communities.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The work described in this paper was supported by the National Science Foundation of China (No. 51808059). The authors gratefully acknowledge the support from the Hunan Provincial Natural Science Foundation of China (Nos. 2019JJ50688 and 2018JJ1027), China Postdoctoral Science Foundation (2018M642975), and Changsha Science and Technology Bureau Project (kq195004, kc1809017).
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Received: Mar 8, 2019
Accepted: Nov 8, 2019
Published online: Mar 28, 2020
Published in print: Jul 1, 2020
Discussion open until: Aug 28, 2020
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