Effects of inflow conditions on mountainous/urban wind environment simulation

Abstract

Inflow conditions play a key role in the Computational Fluid Dynamics (CFD) simulation of wind environment. Taking the micro wind climate of Hong Kong Kowloon Bay costal town as a research object, two kinds of widely used inflow condition determination methods are adopted to test their performances. One is to fit the velocity profile into the empirical (logarithmic/exponential) law, hereafter referred to as the Fitted Empirical Profile (FEP) method. The other is to interpolate the outflow velocities and turbulence properties from a pre-simulation of the upstream region, hereafter referred to as the Interpolated Multiscale Profile (IMP) method. The GIS data of this mountainous/ urban area are digitalized and simplified into the CFD geometry model. Computational treatments for numerical algorithms, domain size, grid systems and boundary conditions are carefully configured according to the published CFD Best Practice Guidelines (BPGs). By validating with one year of real scale wind measurement data from two meteorological stations, it is found that these two inflow conditions lead to considerably different results. Having less consideration for the blockage effects of terrain/buildings, the FEP method tends to predict higher wind speed. As more thermal effects are removed by increasing wind speed thresholds, the results of IMP method demonstrate an incremental agreement with the measurement data. Finally, the validated simulation results are applied to the spatial representativeness assessment of two meteorological stations. Both the point-to-surface consistency indicator and point-centered semivariance are employed. The results show that the meteorological stations show a good representation within a range of 400 m.