loss prevention 2013 - atmospheric dispersion modelling by cellular automata and artificial neural...

Loss Prevention 2013 – 12-15 Mai - Firenze

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

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Pierre Lauret, Ph.D. Student, 2nd year

Frédéric Heymesa, Laurent Aprina, Anne Johannetb, Gilles Dusserrea, Laurent Munierc, Emmanuel Lapébiec

a: Ales Shool of Mines, Franceb: Commissariat à l’Energie Atomique et aux Energies Alternatives

Institut des Sciences des Risques

Context of the study Machine learning tools Methodology Results Improvements Conclusion

Summary

04/12/2023 2Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des Sciences des

Risques

Near Field Atmospheric Dispersion Modelling –Cellular Automata and Artificial Neural Networks

Context of the study Industrial site Existing models

Machine learning tools Methodology Results Improvements Conclusion

Summary

04/12/2023 3Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des Sciences des

Risques

Near Field Atmospheric Dispersion Modelling –Cellular Automata and Artificial Neural Networks

Context of the study Machine learning tools

Cellular Automata - CA Artificial Neural Networks - ANN Combination

Methodology Results Improvements Conclusion

Summary

04/12/2023 4Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des Sciences des

Risques

Near Field Atmospheric Dispersion Modelling –Cellular Automata and Artificial Neural Networks

Context of the study Machine learning tools Methodology

CFD case Database creation Scalar transport equation

discretisation ANN - Learning ANN – Optimisation CA-ANN – using the model

Results Improvements Conclusion

Summary

04/12/2023 5Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des Sciences des

Risques

Near Field Atmospheric Dispersion Modelling –Cellular Automata and Artificial Neural Networks

Context of the study Machine learning tools Methodology Results

Test cases Discussion

Improvements Conclusion

Summary

04/12/2023 6Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des Sciences des

Risques

Near Field Atmospheric Dispersion Modelling –Cellular Automata and Artificial Neural Networks

Context of the study Machine learning tools Methodology Results Improvements Conclusion

Summary

04/12/2023 7Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des Sciences des

Risques

Near Field Atmospheric Dispersion Modelling –Cellular Automata and Artificial Neural Networks

Machine learning MethodologyContext

Impact distance < 1 000 m

Exposure time < 1 h

Industrial site, Martigues, France

Leakage accident

Industrial Site – Flammable/Toxic material storage - Dispersion

Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural Networks

04/12/2023 8Loss Prevention 2013 – 12-15 Mai - Firenze

Institut des

Sciences des

Risques

Methodology development Experimental validation Uncertainty Quantification and Limits of used Aim : Providing operational model with several

goals :

Gaussian models (Solving the Advection-Diffusion Equation)

Integral models Computational Fluid Dynamics (CFD) models

Reynolds Averaged Navier-Stockes equations (RANS) Large Eddy Simulation (LES) Direct Numerical Simulation (DNS)

Quickness

Complete ness

Considera tion of

obstacles

Real experiments

designed

Short time dispersion

Near field

Developed modelQuickness

Completeness

Existing models / Developped model

From quickness to completeness CA-ANN

04/12/2023 9Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Cellular Automata (CA) – Spatial and temporal representation

tt+dt

Transition rules example

tt+dt

« 0 » States

« 1 » States

Monodimensional Cellular Automata Example

t0

t1

t2

t3

t4

t5

Cellular Automata evolution

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.

04/12/2023 10Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Cellular Automata (CA) – Spatial and temporal representation

tt+dt

Transition rules example

tt+dt

« 0 » States

« 1 » States

Monodimensional Cellular Automata Example

t0

t1

t2

t3

t4

t5

Cellular Automata evolution

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.

04/12/2023 11Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Cellular Automata (CA) – Spatial and temporal representation

tt+dt

Transition rules example

tt+dt

« 0 » States

« 1 » States

Monodimensional Cellular Automata Example

t0

t1

t2

t3

t4

t5

Cellular Automata evolution

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.

04/12/2023 12Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Cellular Automata (CA) – Spatial and temporal representation

tt+dt

Transition rules example

tt+dt

« 0 » States

« 1 » States

Monodimensional Cellular Automata Example

t0

t1

t2

t3

t4

t5

Cellular Automata evolution

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.

04/12/2023 13Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Cellular Automata (CA) – Spatial and temporal representation

tt+dt

Transition rules example

tt+dt

« 0 » States

« 1 » States

Monodimensional Cellular Automata Example

t0

t1

t2

t3

t4

t5

Cellular Automata evolution

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.

04/12/2023 14Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Cellular Automata (CA) – Spatial and temporal representation

tt+dt

Transition rules example

tt+dt

« 0 » States

« 1 » States

Monodimensional Cellular Automata Example

t0

t1

t2

t3

t4

t5

Cellular Automata evolution

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.

04/12/2023 15Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

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

04/12/2023 16Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Non-linear statistical data modelling tools Parameters modification to minimize the ANN error Database of the phenomenon required

Phenomenon database

In Field Experiments

Wind Tunnel Experiments

CFD

04/12/2023 17Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Artificial Neural Networks (ANN) – Non linear phenomenon approximation

Cellular Automata Artificial Neural Networks ruled (CA-ANN)

Cellular Automata

Spatial and temporal representation

Non linear phenomenon approximation

Navier-Stokes equations emulation

Cellular Automata

Artificial Neural Networks

Cellular Automata Artificial Neural Networks ruled

Artificial Neural Networks

04/12/2023 18Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

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

Methane ejection with initial velocity – RANS k-ε realizable model

04/12/2023 19Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

CFD Database constitution

Wind:velocity inlet

Wind:velocity inlet

CH4: velocity inlet

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 20Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Ux

Uy

t0+dt

C

t0

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 21Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Ux

Uy

t0+dt

C

t0

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 22Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Ux

Uy

t0+dt

C

t0

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 23Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Ux

Uy

t0+dt

C

t0

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 24Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Ux

Uy

t0+dt

C

t0

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 25Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Ux

Uy

t0+dt

C

t0

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 26Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

For each cell i,j:

Formatting data

Starting from advection-diffusion equation (Navier-Stokes) :

04/12/2023 27Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

TargetANN Inputs

Collected Computed

Training and optimization

ANN Parameters Initialization

Sampling Database

ANN Architecture

C0 initialization from an existing case

04/12/2023 28Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Simulating a known case and evaluating the CA-ANN

Iterative algorithm ofthe Cellular Automata

Artificial Neural Network ruled (CA-ANN)

t0 : Initialization

C0 initialization from an existing case

04/12/2023 29Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Using and evaluating the CA-ANN

Iterative algorithm ofthe Cellular Automata

Artificial Neural Network ruled (CA-ANN)

t0 : Initialization

C0 initialization from an existing case

04/12/2023 30Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Using and evaluating the CA-ANN

Iterative algorithm ofthe Cellular Automata

Artificial Neural Network ruled (CA-ANN)

t0 : Initialization

C0 initialization from an existing case

04/12/2023 31Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Using and evaluating the CA-ANN

Iterative algorithm ofthe Cellular Automata

Artificial Neural Network ruled (CA-ANN)

t0 : Initialization

C0 initialization from an existing case

04/12/2023 32Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Using and evaluating the CA-ANN

Iterative algorithm ofthe Cellular Automata

Artificial Neural Network ruled (CA-ANN)

t0 : Initialization

C0 initialization from an existing case

04/12/2023 33Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Using and evaluating the CA-ANN

Iterative algorithm ofthe Cellular Automata

Artificial Neural Network ruled (CA-ANN)

t0 + dt…

Unlearned case simulation and evaluation

04/12/2023 34Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

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

Tota

l mas

s

Time steps

Mass (CA-ANN)

Mass (CFD)

Unlearned case simulation and evaluation

04/12/2023 35Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Initialization: using a CFD case (Initial Mass Fraction: 0.89; Wind velocity: 10.2 m.s-1)Mass conservation trough time stepsUncertainty visualisation: Model Vs CFD reality

Model concentrations Vs CFD concentrations: at time step

CA-A

NN

con

cent

ratio

ns (k

g.m

-3)

CFD concentrations (kg.m-3)

Unlearned case simulation and evaluation

04/12/2023 36Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

Initialization: using a CFD case (Initial Mass Fraction: 0.89; Wind velocity: 10.2 m.s-1)Mass conservation trough time stepsUncertainty visualisation: Model Vs CFD reality

Model concentrations Vs CFD concentrations: at time step Absolute error at time step

CA-A

NN

con

cent

ratio

ns (k

g.m

-3)

CFD concentrations (kg.m-3)

and CA-ANN concentrations(>0: over estimation ; <0 under estimation)

dx

dy

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

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

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.

04/12/2023 38Loss Prevention 2013 – 12-15 Mai - Firenze

Machine learning MethodologyContext Results Improvements Conclusion

Near Field Atmospheric Dispersion Modelling - Cellular Automata and Artificial Neural NetworksInstitut

des Sciences

des Risques

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

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Contact: pierre.lauret@mines-ales.fr