Sensor driven feedback for puff estimation using unmanned aerial vehicles

Abstract

The puff (or the pollutant puff) represents an instantaneous pollution cloud released in the ambient atmosphere. This paper describes, and validates a complete dynamic data driven application system (DDDAS) for measuring and simulating a puff in a dynamic, urban environment. Unmanned aerial vehicles (UAVs) are used as mobile sensors to collect data from the concentration field which is then assimilated into a running advection diffusion simulation to predict the puff motion. In turn, the running simulation is used to determine desirable locations to place the sensors based on the previously collected data. We directly compare the error achieved in a real-time, low resolution simulation by using both static and mobile sensors in a the DDDAS. The scenario investigated here is analogous to a chemical puff that is released in an urban environment and travels downstream according to the advection diffusion equation. We find that a single mobile sensor in the DDDAS outperforms an array of several static sensors in this scenario. Additionally, groups of mobile sensors are able to further decrease the error levels in the simulation.