Institut des Sciences des
Risques
Laureta P., Heymesa F., Aprina L., Johanneta A., Dusserrea G., Lapébieb E., Osmontb A.
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
6th International Conference on Safety& Environment in Process & Power Industry
Tuesday, April 15, 2014, Bologna, Italy
aLaboratoire de Génie de l’Environnement Industriel (LGEI), Ecole des Mines d’Alès, Alès, France
bCEA, DAM, GRAMAT, F-46500 Gramat, France
Institut des Sciences des Risques (France)Institut des
Sciences des Risques
Modeling Experimental15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom2
Context of the study Artificial Neural Networks Methodology Results Improvements & Conclusion
Contents
Atmospheric Turbulent Dispersion Modeling Methodsusing Machine Learning ToolsInstitut des
Sciences des Risques
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom3
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
Industrial Site – Flammable/Toxic material storage - Dispersion
Leakage accident
Impact distance < 1 000 m
Exposure time < 1 h
Petrochemical site, Martigues, France
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom4
Turbulent Diffusion coefficient estimation
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom5
Main goals of this work
1. From quickness to accuracy
Accuracy
Qui
ckne
ss
Gaussian
Integrals
CFD
RANSLES
DNS
Quickness
Accuracy
Closure equations
2. Turbulence modeling
Turbulent diffusion
coefficient calculation
Direct resolution
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom6
Main goals of this work
1. From quickness to accuracy
Gaussian
Integrals
CFD
RANSLES
DNS
Quickness
Accuracy
Turbulent Diffusion coefficient estimation
Developed model
2. Turbulence modeling
Closure equations
Turbulent diffusion
coefficient calculation
Direct resolution
Accuracy
Qui
ckne
ss Turbulent diffusion coefficient forecasting
by Artificial Neural Networks
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom7
Main goals of this work
3. Goals
Developed model
Quickness
Accuracy
Consider cylinder
obstacles
Real experiments
designed
No expert knowledge
required
Near field
Developed model
4. Re>2 x 104
2. Re = 261. Re = 0,16
3. 48<Re<180
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom8
Flow around cylinder
1,2,3 from Taneda – 4,5 from Mines Alès
Atmospheric flow: Re > 106
Turbulence modeling is required Unsteady behavior at Re >
2.104
Generally considered as steady in modeling due to random initialization of vortex
Modeling dispersion around cylinder Once wind flow and turbulence
are solved Eulerian: Advection Diffusion
Equation Lagrangian: Particle tracking
5. Shape of flow Behind a cylinder
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom9
Artificial Neural Networks (ANN) – Nonlinear phenomenon approximation
Non-linear statistical data modelling tools
Phenomenon database
Inputs Target Output
Neural Network Computed Output Error calculation
Error minimization algorithm
Training Phase
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom10
Non-linear statistical data modelling tools Parameters modification to minimize the ANN error Database of the phenomenon required
Field Experiments
Wind Tunnel Experiments
CFD
Artificial Neural Networks (ANN) – Nonlinear phenomenon approximation
Phenomenon database
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom11
Non-linear statistical data modelling tools Parameters modification to minimize the ANN error Database of the phenomenon required
Field Experiments
Wind Tunnel Experiments
CFD
Artificial Neural Networks (ANN) – Nonlinear phenomenon approximation
Phenomenon database
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom12
Determination of important parameters (Cao, 2007) Position of a plume forecast of continuous standard deviation for gaussian plume
Filter for a gaussian model (Pelliccioni, 2006) Concentrations levels predicted by gaussian model as an input of ANN Other inputs used to refine results are atmospheric conditions parameters Gaussian model improvement
ANN in Atmospheric Dispersion
Conclusions
Three different variables are used: Spatial inputs Atmospheric conditions inputs Case configuration inputs
Database of the phenomenon required
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom13
ui and Dt are required Then, ADE can be solve with existing numerical scheme
Using the 2D-Advection Diffusion Equation (ADE) to solve atmospheric dispersion around cylinder
Methodology
ui and Dt forecast using ANN Solving ADE: Finite differences scheme Database characteristics:
Created from CFD model : RANS k- standard with neutral conditions of stability 72 simulations : Diameter m, velocity m.s-1
Domain dimensions: 34 diameters long, 7 diameters large Mesh: from 112 000 to
448 000 nodes Time consuming Sampling is required
to train the ANN
𝜕𝑐𝜕𝑡
+𝑢𝑖𝜕𝑐𝜕𝑥 𝑗
=𝜕𝜕 𝑥 𝑗
(𝐷 𝑡 .𝜕𝑐𝜕 𝑥 𝑗
)+𝑆𝑖+𝑅𝑖
Wind velocity in i directiont TimesC ConcentrationSi Emission source
Ri Reaction
Dt Turbulent diffusion coefficient
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom14
Ux, Uy and Dt are each onethe output of an ANN
Inputs variables: Location: polar coordinates Configuration: Diameter Flow conditions: Inlet velocity
Inputs and outputs variables for the ANN
Several ANN models are trained with variations on:
Sampling Number of neurons in hidden layer Parameters initialization
Best model is selected using mean squared error quality indicator.
Training of the ANNDt
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom15
Ux, Uy and Dt are each onethe output of an ANN
Inputs variables: Location: polar coordinates Configuration: Diameter Flow conditions: Inlet velocity
Inputs and outputs variables for the ANN
Several ANN models are trained with variations on:
Sampling Number of neurons in hidden layer Parameters initialization
Best model is selected using mean squared error quality indicator.
Training of the ANN
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom16
Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1
Coefficient of determination (R²) and FACtor of two (FAC2) are used to qualify the model
Using the ANN for Ux/Uy/Dt determination
Ux Uy Dt
R²: 0,97 FAC2: 0,99 R²: 0,99 FAC2: 0,52 R²: 0,98 FAC2: 0,99
CFD
ANN
CFD
ANN
m.s-1 m.s-1 m2.s-1
CFD
ANN
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom17
Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1
Flow visualization
CFD
ANN
Velocity vectors
CFD
ANN
Streamlines
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom18
Wind flow and Turbulent diffusion coefficient are used to solve the ADE Finite differences are used Explicit resolution for advection and diffusion terms Stability criteria has to be set :
Courant number is used for the advection terms:
Diffusion terms has to respect:
Minimum is selected
Cylinder obstacle is detected and convert on a rectangular mesh Boundary conditions are set as in CFD model Comparison is made from same initial concentrations
CFD Wind flow and Dt are interpolated on the new mesh
ANN Wind flow and Dt are calculated on the center ofthe mesh cells
Using the ADE
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom19
Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1
Using the ANN for Ux/Uy/Dt determination
CFD
ANN
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom20
Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1
Using the ANN for Ux/Uy/Dt determination
CFD
ANN
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom21
Using the ANN for Ux/Uy/Dt determination
CFD
ANN
Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1
Computing time: Flow field and Dt by ANN model: less than 2 seconds Flow field and Dt by CFD turbulence model: from 20 minutes to 1 hour But with different resolutions
Advection diffusion equation ~3 minutes for 1 minute in simulation time With spatial resolution of 0.5 m Optimization has to be made
Computer used : Classical workstation Processor: Intel® Core™2 Duo CPU: E7500-2,93 GHz RAM: 4 Go Windows 7 Professionnal CFD software: Ansys® Fluent 14 Academic Research
Wind flow and turbulent diffusion coefficient modeling is very fast Accuracy is evaluated through CFD comparison Model has to be confront to experimental data Turbulent dispersion is correctly modeled around a cylinder Data needed are only diameter and inlet velocity to compute
turbulence in neutral stability conditions ANN in combination with ADE resolution act as a grey box.
ConclusionMethodology
Institut des Sciences des
Risques Context Artificial Neural Networks Results
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
15/04/2014 CISAP6 13-16 April, 2014, Bologna, ItalyInstitut Mines-Telecom22
Conclusion
Quickness
Accuracy
Consider cylinder
obstacles
Real experiments
designed
No expert knowledge
required
Near field
Developed model
Experimental data acquisition are needed: Comparison with current model Training on real life data
Future work will be focused on dispersion over multiple obstacles Tridimensional modeling of the flow field and Dt will be implement Numerical optimization has to be done
Perspectives
This research was supported by the CEA: French Alternative Energies and Atomic Energy Commission
Acknowledgements
Institut des Sciences des
Risques
Laureta P., Heymesa F., Aprina L., Johanneta A., Dusserrea G., Lapébieb E., Osmontb A.
Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
6th International Conference on Safety& Environment in Process & Power Industry
Tuesday, April 15, 2014, Bologna, Italy
aLaboratoire de Génie de l’Environnement Industriel (LGEI), Ecole des Mines d’Alès, Alès, France
bCEA, DAM, GRAMAT, F-46500 Gramat, France