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

TL;DR

This paper introduces a rapid, automated urban CFD simulation method integrating meteorological data, validated against real data, and applied to drone wind interaction studies, significantly improving computational efficiency.

Contribution

The authors present a novel automated CFD approach that simplifies urban geometry creation and integrates meteorological predictions, enhancing simulation speed and accuracy.

Findings

Strong agreement with real wind data (up to 0.985 correlation)

Significant reduction in computation time for drone-wind interaction simulations

Validated CFD model effectively captures complex urban wind patterns

Abstract

In past years, several studies have proposed new methods and applications for urban wind simulations. In this article, we present a fast and automatic 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 $\rho_c = 0.985$ for the wind direction and $\rho_c = 0.853$ 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.