Near Field Atmospheric Dispersion Modeling on an Industrial Site Using Neural Networks
Résumé
Assessment of likely consequences of a potential accident is a major concern of loss prevention and
safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial
sites may 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 in near field of the gas source. The present work aims to
study if Neural Networks and Cellular Automata could be relevant to overcome these gaps. These tools
were investigated on steady state and dynamic state. A database was designed from RANS k-İ CFD and
Gaussian plume models. Both methods were then applied. Their efficiencies are compared and discussed
in terms of quality, real-time applicability and real-life plausibility