Modelling of distribution of aerosol emission with account of buildings and structures of NPPs

2nd International (15th Regional) Scientific Conference "Technogenic Systems and Environmental Risk"

工业技术（总论）;生态环境科学

Mehdi, M.^1^2 ; Panin, M.P.^1 ; Mohammedi, B.^2

National Research Nuclear University MEPhI, Moscow Engineering Physics Institute, Moscow, Russia^1

Nuclear Research Center of Birine, Algeria^2

关键词: Aerosol emissions;    Atmospheric conditions;    Contaminant dispersion;    Contaminant transport;    Numerical results;    Quantitative values;    Reynolds Averaged Navier-Stokes Equations;    Turbulent flow model;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/487/1/012011/pdf
DOI  :  10.1088/1757-899X/487/1/012011

来源: IOP

【 摘 要 】

In the lack of experimental data and due to the rapid development of numerical modelling and computer hardware, computational fluid dynamics (CFD) is widely used to study wind field and contaminant transport around buildings. The ability to predict quantitative values for these processes is not so clear. CFD simulations work under neutral atmospheric conditions and are validated by the Gauss model. The Gaussian model is a mathematical distribution of the concentration of contaminants from NPP ventilation pipes. It is the most recommended model by the IAEA and most widely used because 1) it gives results consistent with experimental data, 2) it is quite easy to use, and 3) it is consistent with the aleatory nature of turbulence. Reynolds-averaged Navier-Stokes equations is the most widely used method in turbulent flow modeling and simulation. In this paper, the prediction of flow accuracy and propagation around buildings with a stack was examined using k- model. The numerical results were compared to Gauss model results. Contaminant dispersion was well predicted with k- model. It was also confirmed that the concentrations predicted by the CFD models were more diffused than those of the Gauss model and that the results depend on the position of the obstacles.