Fire and smoke digital twin – A computational framework for modeling fire incident outcomes

• Published in: Computers, environment and urban systems Vol. 110; p. 102093
• Main Authors: Lewis, Ryan Hardesty, Jiao, Junfeng, Seong, Kijin, Farahi, Arya, Navrátil, Paul, Casebeer, Nate, Niyogi, Dev
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Digital twin; Physical simulation; Smoke prediction; Urban fire; Smoke prediction; Digital twin; Physical simulation; Urban fire
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2024.102093

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens. Computer systems organization → Embedded systems; Real- time systems; • Computing methodologies → Modeling and simu- lation; • Applied computing → Physical sciences and engineering. •Developed a live fire tracking platform, predicting smoke paths and impacts on air quality across U.S. cities.•Built a close to real-time fire and smoke digital twin to inform and protect public health.•Utilized Austin's 3D building footprints to simulate smoke outputs and predict fire incident smoke falloffs.•Cooperated with Austin Fire Department, confirming forecast accuracy and highlighting insufficient air quality sensor density.•Released replicable code and methodology, paving the path for worldwide digital twin models to protect citizens.