Three-dimensional computational fluid dynamics investigation of the dispersion of radioactive cloud

The evaluation of spatial distributions of plume dispersion into the atmosphere is an important task for estimating the release of radioactive gas. The Gaussian Plume Model represents the most adopted implementation for submersion dose evaluations from an emission stack. The radioactive cloud dispersion is obtained by calculating the Briggs’ coefficients that varies with the meteorological conditions, mainly the wind speed and the atmosphere stability. The ideal scenarios for these models are installations located far away from urban areas, such as nuclear power or big industrial plants. On the other hand, healthcare facilities, such as nuclear medicine, radiotherapy suites or hadrontherapy accelerators, are usually situated in populated areas and in close vicinity to other buildings. For this reason, the hypotheses of the GPM cannot be applied without corrections, since the pollutant transport is affected by several phenomena (buoyancy, downwash) due to the buildings. CFD model can provide a reliable estimate of the pollutant distribution that take into account all these effects. In this work, comparisons between Gaussian plume and fluid dynamic models are performed in order to make comparison at short and long distances. Fluid dynamic results have been obtained by solving the steady-state Reynolds Averaged Navier-Stokes equations using the k-ω turbulence closure model, which has been modified to account for atmospheric stability, thermal stratification, and ground roughness effects. The Monin-Obukhov Similarity Theory is employed to define consistent inflow conditions to simulate different levels of atmospheric stability. Numerical results have been obtained by considering different stability atmospheric conditions and comparisons and differences between models are presented and discussed. Once a reliable distribution of the radioactive pollutant is known, several dosimetric approaches can be adopted in order to evaluate the doses received by population (e.g. Monte Carlo evaluation of the submersion dose, multiplication of the concentration by the screening factors).