Near Field Atmospheric Dispersion Modelling on an Industrial Site Using Combination of Cellular Automata and Artificial Neural Networks

1. Loss Prevention 2013 – 12-15 Mai - Firenze
Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling on an Industrial Site Using
Combination of Cellular Automata and Artificial Neural Networks
-
Pierre Lauret, Ph.D. Student, 2nd year
Frédéric Heymesa, Laurent Aprina, Anne Johannetb, Gilles Dusserrea, Laurent
Munierc, Emmanuel Lapébiec
a: Ales Shool of Mines, France
b: Commissariat à l’Energie Atomique et aux Energies Alternatives

2. Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling –
Cellular Automata and Artificial Neural Networks
Summary






10/17/2013
Context of the study
Machine learning tools
Methodology
Results
Improvements
Conclusion
Loss Prevention 2013 – 12-15 Mai - Firenze
2

3. Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling –
Cellular Automata and Artificial Neural Networks
Summary

Context of the study







10/17/2013
Industrial site
Existing models
Machine learning tools
Methodology
Results
Improvements
Conclusion
Loss Prevention 2013 – 12-15 Mai - Firenze
3

4. Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling –
Cellular Automata and Artificial Neural Networks
Summary


Context of the study
Machine learning tools







10/17/2013
Cellular Automata - CA
Artificial Neural Networks - ANN
Combination
Methodology
Results
Improvements
Conclusion
Loss Prevention 2013 – 12-15 Mai - Firenze
4

5. Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling –
Cellular Automata and Artificial Neural Networks
Summary



Context of the study
Machine learning tools
Methodology









10/17/2013
CFD case
Database creation
Scalar transport equation
discretisation
ANN - Learning
ANN – Optimisation
CA-ANN – using the model
Results
Improvements
Conclusion
Loss Prevention 2013 – 12-15 Mai - Firenze
5

6. Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling –
Cellular Automata and Artificial Neural Networks
Summary




Context of the study
Machine learning tools
Methodology
Results




10/17/2013
Test cases
Discussion
Improvements
Conclusion
Loss Prevention 2013 – 12-15 Mai - Firenze
6

7. Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling –
Cellular Automata and Artificial Neural Networks
Summary






10/17/2013
Context of the study
Machine learning tools
Methodology
Results
Improvements
Conclusion
Loss Prevention 2013 – 12-15 Mai - Firenze
7

8. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Industrial Site – Flammable/Toxic material storage - Dispersion
Leakage accident
Industrial site, Martigues, France
Impact distance < 1 000 m
Exposure time < 1 h
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
8

9. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Existing models / Developped model
From quickness to completeness



Gaussian models (Solving the AdvectionDiffusion Equation)
Integral models
Computational Fluid Dynamics (CFD) models



Reynolds Averaged Navier-Stockes equations (RANS)
Large Eddy Simulation (LES)
Direct Numerical Simulation (DNS)
CA-ANN




Methodology development
Experimental validation
Uncertainty Quantification and Limits of used
Aim : Providing operational model with several
goals :
Quickness
Completeness
Near field
Quickness
Developed
model
Completeness
Real
experiments
designed
Short time
dispersion
Consideration of
obstacles
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
9

10. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata (CA) – Spatial and temporal representation

Cellular Automata are designed by :




Regular mesh
Finite possible states for each cell
Transition rules
State evolution is done by applying local and deterministic rules, uniformly and synchronously for all
cells.
Transition rules example
t
t+dt
Cellular Automata evolution
t0
t1
« 0 » States
t2
t3
t
t+dt
t4
« 1 » States
t5
Monodimensional Cellular Automata Example
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
10

11. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata (CA) – Spatial and temporal representation

Cellular Automata are designed by :




Regular mesh
Finite possible states for each cell
Transition rules
State evolution is done by applying local and deterministic rules, uniformly and synchronously for all
cells.
Transition rules example
t
t+dt
Cellular Automata evolution
t0
t1
« 0 » States
t2
t3
t
t+dt
t4
« 1 » States
t5
Monodimensional Cellular Automata Example
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
11

12. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata (CA) – Spatial and temporal representation

Cellular Automata are designed by :




Regular mesh
Finite possible states for each cell
Transition rules
State evolution is done by applying local and deterministic rules, uniformly and synchronously for all
cells.
Transition rules example
t
t+dt
Cellular Automata evolution
t0
t1
« 0 » States
t2
t3
t
t+dt
t4
« 1 » States
t5
Monodimensional Cellular Automata Example
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
12

13. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata (CA) – Spatial and temporal representation

Cellular Automata are designed by :




Regular mesh
Finite possible states for each cell
Transition rules
State evolution is done by applying local and deterministic rules, uniformly and synchronously for all
cells.
Transition rules example
t
t+dt
Cellular Automata evolution
t0
t1
« 0 » States
t2
t3
t
t+dt
t4
« 1 » States
t5
Monodimensional Cellular Automata Example
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
13

14. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata (CA) – Spatial and temporal representation

Cellular Automata are designed by :




Regular mesh
Finite possible states for each cell
Transition rules
State evolution is done by applying local and deterministic rules, uniformly and synchronously for all
cells.
Transition rules example
t
t+dt
Cellular Automata evolution
t0
t1
« 0 » States
t2
t3
t
t+dt
t4
« 1 » States
t5
Monodimensional Cellular Automata Example
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
14

15. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata (CA) – Spatial and temporal representation

Cellular Automata are designed by :




Regular mesh
Finite possible states for each cell
Transition rules
State evolution is done by applying local and deterministic rules, uniformly and synchronously for all
cells.
Transition rules example
t
t+dt
Cellular Automata evolution
t0
t1
« 0 » States
t2
t3
t
t+dt
t4
« 1 » States
t5
Monodimensional Cellular Automata Example
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
15

16. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Artificial Neural Networks (ANN) – Non linear phenomenon approximation

Non-linear statistical data modelling tools
Phenomenon database
Inputs
Target Output
Neural Network
Computed Output
Error calculation
Error minimization algorithm
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
16

17. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Artificial Neural Networks (ANN) – Non linear phenomenon approximation



Non-linear statistical data modelling tools
Parameters modification to minimize the ANN error
Database of the phenomenon required
Phenomenon database
In Field
Experiments
CFD
Wind Tunnel
Experiments
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
17

18. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Cellular Automata Artificial Neural Networks ruled (CA-ANN)
Cellular Automata
Spatial and
temporal
representation
NavierStokes
equations
emulation
Artificial Neural Networks
Cellular Automata
Artificial Neural Networks ruled
Non linear
phenomenon
approximation
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
18

19. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
CFD Database constitution


Free Field Methane Atmospheric dispersion
Puff evolution




Sequence: ejection of CH4, self evolution until exiting the numerical domain
Each time step, case is saved (Velocity field/ CH4 Concentration)
Time step duration is related to Courant number
Intervals:




Wind velocity: 2-20 m.s-1
Ejection velocity: 2-20 m.s-1
CH4 Mass Fraction: 0.2-1
Time step : 20
Wind:
velocity inlet
CH4: velocity
inlet
Wind:
velocity inlet
Methane ejection with initial velocity – RANS k-ε realizable model
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
19

20. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Ux
For each cell i,j:
Uy
t0
C
t0+dt
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
20

21. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Ux
For each cell i,j:
Uy
t0
C
t0+dt
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
21

22. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Ux
For each cell i,j:
Uy
t0
C
t0+dt
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
22

23. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Ux
For each cell i,j:
Uy
t0
C
t0+dt
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
23

24. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Ux
For each cell i,j:
Uy
t0
C
t0+dt
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
24

25. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Ux
For each cell i,j:
Uy
t0
C
t0+dt
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
25

26. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
For each cell i,j:
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
26

27. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Formatting data
Starting from advection-diffusion equation (Navier-Stokes) :
Collected
Target
Training
and
optimization
10/17/2013
Computed
ANN Inputs
ANN Parameters Initialization
Sampling Database
ANN Architecture
Loss Prevention 2013 – 12-15 Mai - Firenze
27

28. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Simulating a known case and evaluating the CA-ANN

C0 initialization from an existing case
t0 : Initialization
Iterative algorithm of
the Cellular Automata
Artificial Neural Network
ruled (CA-ANN)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
28

29. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Using and evaluating the CA-ANN

C0 initialization from an existing case
t0 : Initialization
Iterative algorithm of
the Cellular Automata
Artificial Neural Network
ruled (CA-ANN)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
29

30. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Using and evaluating the CA-ANN

C0 initialization from an existing case
t0 : Initialization
Iterative algorithm of
the Cellular Automata
Artificial Neural Network
ruled (CA-ANN)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
30

31. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Using and evaluating the CA-ANN

C0 initialization from an existing case
t0 : Initialization
Iterative algorithm of
the Cellular Automata
Artificial Neural Network
ruled (CA-ANN)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
31

32. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Using and evaluating the CA-ANN

C0 initialization from an existing case
t0 : Initialization
Iterative algorithm of
the Cellular Automata
Artificial Neural Network
ruled (CA-ANN)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
32

33. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Using and evaluating the CA-ANN

C0 initialization from an existing case
t0 + dt…
Iterative algorithm of
the Cellular Automata
Artificial Neural Network
ruled (CA-ANN)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
33

34. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Unlearned case simulation and evaluation
Initialization: using a CFD case (Initial Mass Fraction: 0.89; Wind velocity: 10.2 m.s-1)
Mass conservation trough time steps:
Mass evolution for CFD simulation and CA-ANN model
Mass (CA-ANN)
Total mass
Mass (CFD)
Time steps
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
34

35. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Unlearned case simulation and evaluation
Initialization: using a CFD case (Initial Mass Fraction: 0.89; Wind velocity: 10.2 m.s-1)
Mass conservation trough time steps
Uncertainty visualisation: Model Vs CFD reality
CA-ANN concentrations (kg.m-3)
Model concentrations Vs CFD concentrations: at time step
CFD concentrations (kg.m-3)
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
35

36. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Unlearned case simulation and evaluation
Initialization: using a CFD case (Initial Mass Fraction: 0.89; Wind velocity: 10.2 m.s-1)
Mass conservation trough time steps
Uncertainty visualisation: Model Vs CFD reality
Absolute error at time step
Model concentrations Vs CFD concentrations: at time step
dy
CA-ANN concentrations (kg.m-3)
and CA-ANN concentrations
(>0: over estimation ; <0 under estimation)
CFD concentrations (kg.m-3)
10/17/2013
dx
Loss Prevention 2013 – 12-15 Mai - Firenze
36

37. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Improvements

Wind field determination






Experimental (wind tunnel) and numerical database
(CFD)
Cylindrical obstacles (storages)
ANN modelisation
Several obstacles dimensions
Several inlet velocity
Coupling wind field model (ANN) and atmospheric
dispersion model (CA-ANN)
Preliminary tests
Conclusion

38. Institut des
Sciences
des Risques
Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks
Context
Machine learning
Methodology
Results
Improvements
Conclusion
Conclusions







Existing forecasting models are slow but accurate or fast but not enough appropriate.
A method using Cellular Automata and Artificial Neural Networks is presented.
Comparison with CFD software gives good agreement and faster processing.
Computing optimization is not yet done.
CA-ANN lightly overestimates the higher concentrations.
A decay in the determination coefficient trough time steps is noted.
ANN training optimization process is in progress.
10/17/2013
Loss Prevention 2013 – 12-15 Mai - Firenze
38

39. Loss Prevention 2013 – 12-15 Mai - Firenze
Institut des Sciences
des Risques
Near Field Atmospheric Dispersion Modelling on an Industrial Site Using
Combination of Cellular Automata and Artificial Neural Networks
Contact: pierre.lauret@mines-ales.fr