Atmospheric dispersion modeling using Artificial Neural Network based cellular automata

Forecasting atmospheric dispersion in complex configurations is a current challenge in fluid dynamics in terms of calculation time and accuracy. CFD models provide good accuracy but require a great computation time. Simplified or empirical models are designed to quickly evaluate the dispersion but a...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2016-11, Vol.85, p.56-69
Main Authors: Lauret, Pierre, Heymes, Frédéric, Aprin, Laurent, Johannet, Anne
Format: Article
Language:English
Subjects:
Accuracy
Advection-diffusion equation
Artificial Neural Network
Atmospheric diffusion
Atmospheric dispersion
Atmospheric models
Cellular Automata
Computational fluid dynamics
Computer simulation
Fluid mechanics
Machine Learning
Mathematical models
Mechanics
Neural networks
Physics
Statistics
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Summary:Forecasting atmospheric dispersion in complex configurations is a current challenge in fluid dynamics in terms of calculation time and accuracy. CFD models provide good accuracy but require a great computation time. Simplified or empirical models are designed to quickly evaluate the dispersion but are not adapted to complex geometry. Cellular Automata coupled with an Artificial Neural Network (CA-ANN) are developed here to calculate the atmospheric dispersion of methane (CH4) in 2D. Efforts are made in reducing computation time while keeping an acceptable accuracy. A CFD simulations database is created and the Advection-Diffusion Equation is discretized to provide variables for the ANN. Neural network design is made thanks to best sampling selection, architecture selection and optimized initialization. The coefficient of determination is over 0.7 for most cases of the test set despite small errors accumulated through time steps. CA-ANN is faster than CFD models by a factor from 1.5 to 120. [Display omitted] •A new atmospheric dispersion model is developed based on combination of Cellular Automata and Artificial Neural Networks.•Comparisons are made with CFD RANS standard k-ɛ model on 2D free field dispersion of methane.•CA-ANN is faster than CFD standard k-ɛ by a factor from 1.5 to 120 in the modeled simulations while keeping accuracy.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2016.08.001