Anchored-Branched Steady-state WInd Flow Transformer (AB-SWIFT): a metamodel for 3D atmospheric flow in urban environments

Abstract

Air flow modeling at a local scale is essential for applications such as pollutant dispersion modeling or wind farm modeling. To circumvent costly Computational Fluid Dynamics (CFD) computations, deep learning surrogate models have recently emerged as promising alternatives. However, in the context of urban air flow, deep learning models struggle to adapt to the high variations of the urban geometry and to large mesh sizes. To tackle these challenges, we introduce Anchored Branched Steady-state WInd Flow Transformer (AB-SWIFT), a transformer-based model with an internal branched structure uniquely designed for atmospheric flow modeling. We train our model on a specially designed database of atmospheric simulations around randomised urban geometries and with a mixture of unstable, neutral, and stable atmospheric stratifications. Our model reaches the best accuracy on all predicted fields compared to state-of-the-art transformers and graph-based models. Our code and data is available at https://github.com/cerea-daml/abswift.

Figures (5)

• Figure 1: Our proposed model architecture. AB-SWIFT separately encodes the terrain and obstacles to yield one embedded geometry sequence, and encodes the physical context and the volume prediction points. The geometry and encoded prediction points are then processed together. Finally the decoder predicts physical fields from processed latent states of volume points.
• Figure 2: Left: $4$ building configuration, taken from the $210$ different configurations present in our dataset. Right: used values of $1/L_\mathrm{mo}$ and $z0$. Purple points represent the training split, blue the validation split, and yellow the test split.
• Figure 3: Norm of the velocity field predicted by all models, $\qty{2}{\m}$ above ground, for an unstable stratification (Left), a neutral stratification (Middle left), and two stable stratifications (Middle right and Right). All shown geometries and stability parameters are from the test split of the dataset.
• Figure 4: Horizontal slice at height $h=\qty{2}{\m}$ of the fields predicted by AB-SWIFT for a stable stratification ($1/L_\mathrm{mo}=\qty{0.15}{\per\m}, z_0=\qty{0.06}{\m})$. Buildings are shown in teal color.
• Figure 5: Mesh arrangements used to generate database samples. Buildings are clustered in the black area. For machine learning, only the dark blue zone of interest with an horizontal refinement up to 5 is kept and up to a height of 50 to reduce the mesh size.