A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety

DOI10.1016/j.cma.2020.113446zbMath1506.76028OpenAlexW3091823377MaRDI QIDQ2020726

Tarek I. Zohdi

Publication date: 26 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.113446

zbMATH Keywords

optimizationmachine-learningaerial fire-fightingfire retardants

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Kinematics of a rigid body (70B10) Stokes and related (Oseen, etc.) flows (76D07)

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Uses Software

• FARSITE
• FlamMap

Cites Work

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• A combined penalty function and gradient projection method for nonlinear programming
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• Modeling the spatio-thermal fire hazard distribution of incandescent material ejecta in manufacturing
• Advances in the particle finite element method for the analysis of fluid-multibody interaction and bed erosion in free surface flows
• High-density sphere packing for discrete element method simulations

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