Simulating The Urban Canopy's Impact on Wind-Driven Natural Ventilation

TL;DR

Wind-driven natural ventilation in dense urban canopies is highly sensitive to both large-scale canopy geometry and local interference from surrounding buildings. The study uses CharLES-based LES to simulate coupled outdoor–indoor flow for two canopy densities, eight wind directions (parallel/diagonal across four quadrants), and four ventilation configurations, totaling 16 runs; ventilation rates are quantified via the time-averaged non-dimensional metric $Q_n$ and its distribution. Results show that canopy density and wind angle interact to produce domain-scale velocity variations and strong local interference, with ventilation rates varying by 50–85% due to geometry, and higher density canopies reducing angle sensitivity. The findings challenge current natural ventilation parameterizations, highlighting the need for geometry-aware or data-driven approaches that capture both canopy-scale and local wake effects to predict ventilation and cooling accurately at urban scales. The provided LES data form a foundation for developing such models and databases for more reliable, large-scale assessments of natural ventilation and cooling in cities.

Abstract

The urban canopy affects wind in complex ways, making it challenging to predict wind-driven natural ventilation and cooling in buildings. Using large eddy simulations of coupled outdoor and indoor airflow, we study how the surrounding urban canopy and wind angle influence ventilation rates through four ventilation configurations: cross, corner, dual-room, and single-sided. Flow visualizations demonstrate how both large-scale flow patterns and local interference effects can influence ventilation rates by 50-85%. In general, lower density canopies give higher ventilation rates and wind angles that align with a direct path between two openings also lead to higher ventilation rates. However, interference effects from surrounding buildings can significantly change the local wind speed and direction, thus also changing ventilation rates. The magnitude of these interference effects depends on both the wind angle and surrounding building geometries. The effect of wind angle is less pronounced in a higher density canopy, where the urban canopy geometry more strongly guides the flow. The results demonstrate that the canopy's effect on ventilation rates is much more complex than those suggested by existing natural ventilation parameterizations.

Figures (11)

• Figure 1: House placement within the simulation domain. Colors mark interiors across each quadrant.
• Figure 2: Canopy horizontal dimensions with $L_R = 4 \text{ m}$.
• Figure 3: Floor plan of simulated interiors. Arrows indicate possible ventilation pathlines, with solid arrows showing ventilation axes.
• Figure 4: Turbulent flow snapshots from parallel and diagonal wind layered over high density canopy mesh. Plan views at window center (top) and profile through two quadrant centers (bottom).
• Figure 5: Average profiles of mean velocity ($U$) and turbulence intensities ($I$), with $x$ aligned with the mean flow. Lines in the top row show fitted ABL profiles ($U_{fit}$) while subsequent lines plot the empirical turbulence intensities using $\hat{I_u} \cong 1/\log \left((y\!-\!d)/y_0\right)$holmes_wind_2018. Profiles span the domain height.