Fire and Smoke Digital Twin – a Computational Framework for Modeling Fire Incident Outcomes
Abstract
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. CCS concepts: Computer systems organization → Embedded systems; Real- time systems; • Computing methodologies → Modeling and simu- lation; • Applied computing → Physical sciences and engineering.
Recommended Citation
R. H. Lewis et al., "Fire and Smoke Digital Twin – a Computational Framework for Modeling Fire Incident Outcomes," Computers, Environment and Urban Systems, vol. 110, article no. 102093, Elsevier, Jun 2024.
The definitive version is available at https://doi.org/10.1016/j.compenvurbsys.2024.102093
Department(s)
Biological Sciences
Keywords and Phrases
Digital twin; Physical simulation; Smoke prediction; Urban fire
International Standard Serial Number (ISSN)
0198-9715
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
01 Jun 2024
Comments
University of Texas at Austin, Grant 1952193