MeshGraphNets for 3D atmospheric flow in Urban Environment for Atmospheric Dispersion

Urban and industrial areas are vulnerable to accidental releases of pollutants. To accurately determine the pollutant's plume position and affected areas, it is essential to estimate the atmospheric flow around the affected site. This flow can be precisely computed using numerical methods of Computational Fluid Dynamics (CFD). However, CFD computation is expensive and slow, making it unsuitable for emergency response. As reduced order approximations, machine learning surrogates offer a promising alternative as they are usually much faster; but they must first be trained on CFD-generated data. In this study, we propose a database of atmospheric simulations with varying meshes and atmospheric stability conditions. Meshes are built by randomly sampling buildings and positioning them in space. For each mesh, values of the Monin-Obukhov length and of the ground roughness are sampled, leading to different turbulent regimes and overall atmoshperic flow behaviour. We then train a MeshGraphNet on this database, i.e. a graph neural network built on the mesh structure. The performance of the trained neural network on unseen scenarios with different initial conditions has been evaluated and will be presented.