Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools

Abstract

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- e CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility.