Screening Canopy-Induced Wind with a Lightweight 2-D RANS Quadratic-Drag Model

Vegetation belts are widely used in urban planning to manage pedestrian wind, dust dispersion, and outdoor thermal comfort. This paper presents a compact method for predicting canopy-induced flow using a steady two-dimensional RANS model with Spalart–Allmaras closure and a quadratic drag model for vegetation. Each planting zone is defined by a single leaf area index (LAI). The workflow maps LAI to porosity using an exponential coefficient of 0.5, converts porosity to a local LAI per cell, divides by the grid-measured canopy thickness to form a leaf area density, and applies a quadratic drag term. The model is benchmarked against streamwise-mean velocity profiles reported by Liang et al. [4] at heights 0.25h, 0.75h, and 1.25h (where h is canopy height) for a belt spanning x/H = 0 to 5. Results accurately reproduce the three canonical regions—approach, in-canopy deficit, and leeward wake—and show strong agreement with experimental data in the leeward wake, critical for siting homes and streets near planting. The method is computationally efficient, faster than 3D CFD, uses designer-friendly inputs, and is transparent for early design screening. Implementation details, including LAI-to-porosity mapping, grid-based canopy thickness, quadratic drag, and boundary sponges, are provided to enable reproduction in AirSketcher (Polar Dynamix) or comparable solvers.