Real-time Wind Predictions for Safe Drone Flights in Toronto

Abstract

Tall buildings in urban environments can negatively affect drone navigation, by creating turbulence and local regions of high-velocity winds. This jeopardizes the safety of the public in addition to the security of any items being transported by the drone such as medical equipment, packages, or expensive sensors. This study focuses on studying the effect of buildings located in the downtown area of the City of Toronto on the wind field. The City of Toronto was chosen due to the recent structures' development of and the increased usage of drones by hospitals and other businesses. The city geometry was divided into 20 zones and Computational Fluid Dynamics was utilized to study the effects of the local building geometry on the wind field. Wind velocities were extracted from the CFD data and coupled with historical meteorological data so that they could be compared to local weather station measurements. The coupled velocities and wind bearings from CFD were found to be within 12% and 8% RMSE respectively of the recorded data at the wind station for 85% of the sampled data. Wind velocities were then extracted at regular elevation intervals within the flight environment and coupled with meteorological weather data to generate historical, real-time, forecast, and statistical visualization of local wind fields. This study proposes a technique for obtaining real-time, historical, forecast, and statistical wind speeds and wind bearings in the drone flight environment or along a specified path. Visualization of flow fields is also presented to improve drone flight safety and efficiency.