CPS: Medium: Data-Driven Adaptive Real-Time (DART) Flow-Field Estimation Using Deployable UAVs

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The Fukushima Daiichi nuclear disaster and the Aliso Canyon natural gas leak are recent high-profile examples of emergency situations that resulted from the unplanned release of an airborne contaminant. In such emergency scenarios, accurate real-time prediction of contaminant movement is invaluable for planning emergency response, protecting emergency workers, and assessing environmental impact. However, accurate prediction of contaminant dispersion is challenging because of atmospheric turbulence, ground terrain topology, and changing wind conditions. This project addresses the problem of predicting atmospheric contaminant dispersion in real time by using a fleet of autonomous unmanned air vehicles (UAVs) to obtain sparse physical measurements of the atmospheric flow and contaminant concentrations. Then, these sparse physical measurements are used in real time to continually improve a computational fluid dynamic model in order to produce an accurate real-time prediction of the contaminant dispersion. This represents a tight integration of real-time sensing of airborne contamination with multi-vehicle swarm control and cloud dispersion prediction to generate optimal vehicle paths. The primary aim of this project is to develop and demonstrate a new data-driven adaptive real-time (DART) system that produces accurate real-time micro-meteorological estimates and forecasts contaminant dispersion near its source. The DART system will consist of a computational-fluid-dynamic cyber system and a physical system of autonomous UAVs instrumented with flow sensors and contaminant-concentration sensors. Together, this DART system will produce accurate flow-field estimates, which can be used to predict contaminant dispersion. Developing the DART system requires new techniques for real-time data-driven model adaption, advances in computational turbulence modeling, improvements in UAV-based sensing and data processing, and new UAV formation flying methods that use cyber-feedback from the computational-fluid-dynamic cyber system. The project includes multiple levels of experimentation including simulation, wind tunnel, and live flight demonstration to provide proof of concept. This project is jointly funded by the Cyber Physical System Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.