Innovative methods for the location of a radioactive pollutant source in air

This work deals with modeling and prediction of emissions of gaseous or colloidal radioactive pollutants in air, coming from different sources close to the ground such as: the operation of a nuclear reactor and nuclear facilities such as spent nuclear processing plant, the disposal of radioactive waste, the combustion of fossil fuels, any other accidental event that give rise to a release radioactive contaminants in the air such as the opening of a dry storage cask system. In particular, the present study is focused in the feasibility analysis of the adoption of the reduced order modeling approach to describe and predict the dispersion of contaminants. To this aim, a simulation on a study case has been performed with a rigorous approach by using techniques of Computational Fluid Dynamics (CFD), and taking into account normal reference conditions of the Atmospheric Boundary Layer (ABL). In order to obtain a reduced order model that can describe the transport of the pollutant in the environment induced mainly by the surrounding air flow, it is desired that the reduced order model possess the capacity to reproduce the dynamic behavior under the variation of different external parameters such as the intensity of the source term or the influence of the reference speed on the atmospheric profile, without having to perform again a complete and rigorous simulation. This goal, may be achieved by coupling a reduced order model with a control algorithm, which can be implemented in a practical way with a low computational cost in a probe motion control algorithm, based on measurements of the air speed and concentration in air of a specific pollutant. In this regard the use of mathematical tools such as Model Predictive Control (MPC) or Kalman Filtering (KF) has been proven to be effective on a ``pseudo-experimental" case study scenario influenced by different phenomena that induce considerable deviations from the ideal behavior.