A fast and automated approach for urban CFD simulations: validation with meteorological predictions and its application to drone flights

In past years, several studies have proposed new methods and applications for urban wind simulations, including geometry reconstruction from urban data sources or improved boundary condition definition. In this article, we present a fast and automated methodology for reconstructing airflows within urban environments using LiDAR and cadastral data coupled with Computational Fluid Dynamics (CFD) simulations. Our approach integrates meteorological predictions with computational techniques to simulate the complex interactions between wind currents, buildings, vegetation, water zones and terrain morphology within urban environments. Accurate boundary conditions based on meteorological predictions are introduced into a coupled methodology that directly creates the terrain shape inside the simulation environment, simplifying the geometry creation process, which is one of the most prevalent problems in CFD urban simulations. The simulation results are confronted against ground-truth real data obtained from a meteorological station, showing strong agreement with the outcomes generated by the proposed CFD model, with a concordance correlation coefficient up to ρ c = 0.985 for the wind direction and ρ c = 0.853 for the wind speed. The results from these simulations are then used for validating a wind tunnel approach that mimics the interaction between a moving drone and the extracted wind currents, demonstrating a great improvement in computation times when compared to the most straightforward approach that consists in embedding the drone within the full urban landscape. This research contributes to the advancement of urban CFD modeling, and it has significant implications for various applications, providing valuable insights for urban development. • Automated CFD pipeline integrates LiDAR and cadastral data for urban modeling. • Vegetation effects modeled using spatially variable roughness values. • Boundary conditions derived from meteorological forecasts. • Validated with real data; correlation up to 0.985 for wind direction accuracy. • Wind tunnel drone test matches full CFD reconstruction with 10× faster simulations.