A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments

• Published in: Computer methods in applied mechanics and engineering Vol. 363; pp. 112907 - 19
• Main Author: Zohdi, T.I.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2020; Elsevier BV
• Subjects: Adaptive systems; Algorithms; Computer simulation; Digital twins; Digital-twin; Ember flow; Fire-propagation; Flammability; Machine learning; Machine-learning algorithms; Meshless methods; Outdoor air quality; Propagation; Quality assessment; Real time; Soot; Upgrading; Vertical air currents; Wind speed; Digital-twin; Fire-propagation; Ember flow; Machine-learning algorithms

The objective of this work is to illustrate how to algorithmically integrate Machine-Learning Algorithms (MLA’s) with multistage/multicomponent fire spread models. In order to tangibly illustrate this process, this work develops a framework for a specific model problem combining: (I) a meshless disc...