Revolutionizing Firefighting Training With Digital Twins: Real-time Fire Spread Simulation and Escape Route Optimization
Year: 2024
Type: article
Abstract: The advancement of digital twin (DT) systems had revolutionized various industrial and safety applications, offering virtual replicas of physical processes for improved monitoring and training. Fire scenarios are highly dynamic, with conditions changing rapidly due to factors like wind direction, material flammability, and structural integrity. This study explored the application of a smart fire scene DT system (SFSDTS) for firefighting training and safety management. The novelty and contributions of this study was the proposed SFSDTS integrated machine learning models to predict fire spreading paths and estimate escape routes in real-time, providing an immersive and interactive training environment for firefighters.
Authors: Zhang, Ying, Lee, Boon Giin
Institution: University of Nottingham Ningbo China
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930756495
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