Institut des Sciences des Risques Lauret a P. , Heymes a F., Aprin a L., Johannet a A., Dusserre a G., Lapébie b E., Osmont b A. Atmospheric Turbulent Dispersion Modeling Methods using Machine Learning Tools 6 th International Conference on Safety & Environment in Process & Power Industry Tuesday, April 15, 2014, Bologna, a Laboratoire de Génie de l’Environnement Industriel (LGEI), Ecole des Mines d’Alès, Alès, France b CEA, DAM, GRAMAT, F-46500 Gramat, France
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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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
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
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
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.
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