2014 CISAP6 Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
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Assessment of likely consequences of a potential accident is a major concern for loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler models are used but remain inaccurate especially when turbulence is heterogeneous. The present work aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome these gaps. Two methods are reviewed and compared. An example database was designed from RANS k- ε CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility.
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2014 CISAP6 Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools
- 1. Institut des Sciences des Risques Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Laureta P., Heymesa F., Aprina L., Johanneta A., Dusserrea G., Lapébieb E., Osmontb A. 6th International Conference on Safety & Environment in Process & Power Industry Tuesday, April 15, 2014, Bologna, Italy bCEA, DAM, GRAMAT, F-46500 Gramat, France aLaboratoire de Génie de l’Environnement Industriel (LGEI), Ecole des Mines d’Alès, Alès, France
- 2. Institut des Sciences des Risques (France) Institut des Sciences des Risques Modeling Experimental 15/04/2014 2 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 3. Institut des Sciences using Machine Learning Tools Contents Atmospheric Turbulent Dispersion Modeling Methods Context of the study Artificial Neural Networks Methodology Results Improvements & Conclusion des Risques 3 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 4. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Industrial Site – Flammable/Toxic material storage - Dispersion Leakage accident Petrochemical site, Martigues, France Impact distance < 1 000 m Exposure time < 1 h 4 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 5. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion 2. Turbulence modeling Turbulent Diffusion coefficient estimation Institut des Sciences des Risques Context Artificial Neural Networks Results Main goals of this work 1. From quickness to accuracy CFD RANS LES Accuracy Quickness Gaussian Integrals DNS Closure equations Turbulent diffusion coefficient calculation Direct resolution 5 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 6. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Main goals of this work 1. From quickness to accuracy Gaussian Integrals CFD Developed model RANS LES DNS 2. Turbulence modeling Turbulent Diffusion coefficient estimation Turbulent diffusion coefficient forecasting by Artificial Neural Networks Closure equations Turbulent diffusion coefficient calculation Direct resolution Accuracy Quickness 6 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 7. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Main goals of this work 3. Goals Quickness Developed model Accuracy Developed model Consider cylinder obstacles Real experiments designed Near field No expert knowledge required 7 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 8. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion 1. Re = 0,16 2. Re = 26 4. Re>2 x 104 3. 48<Re<180 Institut des Sciences des Risques Context Artificial Neural Networks Results Flow around cylinder 5. Shape of flow Behind a 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 8 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 9. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Artificial Neural Networks (ANN) – Nonlinear phenomenon approximation Non-linear statistical data modelling tools Phenomenon database Inputs Target Output Training Phase Neural Network Computed Output Error calculation Error minimization algorithm 9 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 10. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Artificial Neural Networks (ANN) – Nonlinear phenomenon approximation Non-linear statistical data modelling tools Parameters modification to minimize the ANN error Database of the phenomenon required Field Experiments Phenomenon database Wind Tunnel Experiments CFD 10 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 11. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Artificial Neural Networks (ANN) – Nonlinear phenomenon approximation Non-linear statistical data modelling tools Parameters modification to minimize the ANN error Database of the phenomenon required Field Experiments Phenomenon database Wind Tunnel Experiments CFD 11 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 12. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results ANN in Atmospheric Dispersion 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 Conclusions Three different variables are used: Spatial inputs Atmospheric conditions inputs Case configuration inputs Database of the phenomenon required 12 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 13. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Using the 2D-Advection Diffusion Equation (ADE) to solve atmospheric dispersion around cylinder ui and Dt are required 휕푐 휕푡 + 푢푖 휕푐 휕푥푗 = 휕 휕푥푗 퐷푡 . 휕푐 휕푥푗 + 푆푖 + 푅푖 Then, ADE can be solve with existing numerical scheme Methodology ui and Dt forecast using ANN Solving ADE: Finite differences scheme Database characteristics: 푢푖 Wind velocity in i direction t Times C Concentration Si Emission source Ri Reaction Dt Turbulent diffusion coefficient Created from CFD model : RANS k-휖 standard with neutral conditions of stability 72 simulations : Diameter ∈ 10; 52 m, velocity ∈ 2; 10 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 13 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 14. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Inputs and outputs variables for the ANN Ux, Uy and Dt are each one the output of an ANN Inputs variables: Location: polar coordinates Configuration: Diameter Flow conditions: Inlet velocity Training of the ANN Dt 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. 14 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 15. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Inputs and outputs variables for the ANN Ux, Uy and Dt are each one the output of an ANN Inputs variables: Location: polar coordinates Configuration: Diameter Flow conditions: Inlet velocity Training of 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. 15 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 16. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Using the ANN for Ux/Uy/Dt determination 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 Ux Uy Dt R²: 0,97 FAC2: 0,99 R²: 0,99 FAC2: 0,52 R²: 0,98 FAC2: 0,99 CFD ANN m.s-1 m.s-1 m2.s-1 CFD ANN CFD ANN 16 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 17. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Flow visualization Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1 CFD ANN Velocity vectors CFD ANN Streamlines 17 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 18. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Using the ADE 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: 훥푘 ≤ 훥푥 푚푎푥 푈푥 Diffusion terms has to respect: 훥푘 ≤ 훥푥2 2퐷푡 Minimum 훥푘 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 of the mesh cells 18 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 19. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Using the ANN for Ux/Uy/Dt determination Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1 CFD ANN 19 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 20. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Using the ANN for Ux/Uy/Dt determination Unlearned test case: D = 12 m ; Uini = 2,5 m.s-1 CFD ANN 20 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 21. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Methodology Conclusion Institut des Sciences des Risques Context Artificial Neural Networks Results Using the ANN for Ux/Uy/Dt determination CFD Flow field and Dt by CFD turbulence model: from 20 minutes to 1 hour 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 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 21 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 22. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Risques Context Artificial Neural Networks Results Methodology Conclusion Institut des Sciences des Conclusion 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. Quickness Accuracy Developed model Consider cylinder obstacles Real experiments designed Near field No expert knowledge required Perspectives 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 Acknowledgements This research was supported by the CEA: French Alternative Energies and Atomic Energy Commission 22 15/04/2014 Institut Mines-Telecom CISAP6 13-16 April, 2014, Bologna, Italy
- 23. Institut des Sciences des Risques Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools Laureta P., Heymesa F., Aprina L., Johanneta A., Dusserrea G., Lapébieb E., Osmontb A. 6th International Conference on Safety & Environment in Process & Power Industry Tuesday, April 15, 2014, Bologna, Italy bCEA, DAM, GRAMAT, F-46500 Gramat, France aLaboratoire de Génie de l’Environnement Industriel (LGEI), Ecole des Mines d’Alès, Alès, France