Fast flow field prediction of pollutant leakage diffusion based on deep learning.
Environ Sci Pollut Res Int
; 31(36): 49393-49412, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-39073715
ABSTRACT
Predicting pollutant leakage and diffusion processes is crucial for ensuring people's safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Hydrodynamics
/
Deep Learning
Language:
En
Journal:
Environ Sci Pollut Res Int
Journal subject:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Year:
2024
Document type:
Article
Affiliation country: