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Wind flows inside complex urban environments are determined by the mutual effect of large-scale (i.e., wind above the urban boundary layer, UBL) and local-scale forcing (i.e. constraints into the urban canopy layer, UCL). Usually, when the wind field above the UBL is known (e.g. by on-site measurements, OsM), high-resolution microscale models (e.g. CFD) are used to predict the wind inside the UCL. However, standard procedures to map the wind field from an undisturbed position to the UCL are not available yet and several technical aspects need to be investigated. A downscaling method, so-called "static downscaling", of the wind from mesoscale to microscale is innovatively adopted here to evaluate the performance of two CFD microscale models when predicting the flow in a UCL. The methodology is based on OsM transferred into the UCL by means of so-called "transfer coefficients" calculated by 3D steady RANS simulations for two different spatial extents of the explicitly modeled urban texture (Case A and B). It is discussed in detail the way the transfer coefficients work, how they can be used to understand the correlations between wind above and within the UCL as well as to identify the major limitations of the RANS approach. Results are discussed in qualitative terms and quantified using standard metrics. It is also shown that too high flow rates can occur at the entrance of the waterway of the area of interest and in the outer part of the explicitly modeled urban area, which can be mitigated by "buffer zones". [Display omitted] • On-site measurements were performed in Livorno city with LiDAR and anemometers. • RANS simulations on more and less spatially extended cases of Livorno city. • Transferred CFD data were compared to measured data inside the UCL at target points. • The extension of the environment surrounding the target area plays a key role. • Deviations are discussed and effects on wind flow quantified by statistical metrics. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of 3D steady RANS CFD simulations of wind flow in urban environments can be affected by numerical settings including the turbulence model and the imposed roughness heights. In that regard, various k-ε and k-ω turbulence models and roughness height (k s) values are commonly used when predicting wind flow in urban environments. However, it is insufficiently known to which extent the CFD results may be influenced by these settings when simulating wind flows in complex urban environments with large changes in surface roughness. This is the scope of the present paper, for which wind-tunnel (WT) measurements and CFD simulations were performed on a reduced-scale model (1:300) of a district of Livorno (Italy). Mean wind speed (U), turbulent kinetic energy (k) and turbulence dissipation rate (ε) profiles from WT measurements and CFD simulations were compared at 25 positions and deviations between experimental and numerical results were quantified by three metrics: fractional bias, correlation coefficient and fraction of data within a factor of 1.3. The turbulence model selection had a larger impact compared to the surface roughness selection on U , k and ε values. The best and worst performing turbulence models (e.g. for α = 240° at 0.02 m above the bottom) showed a deviation in terms of correlation (0.89 and 0.61, respectively) of about 0.28. Conversely, the best and worst performing roughness set, (e.g. for α = 240° at 0.02 m above the bottom), showed a deviation in terms of correlation (0.77 and 0.78, respectively) of only 0.01. Image 1 [ABSTRACT FROM AUTHOR]