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A MCREXS modelling approach for the simulation of a radiological dispersal device

Ivan, Lucian, Hummel, David, Lebel, Luke
Journal of environmental radioactivity 2018 v.192 pp. 551-564
air, mechanistic models, particle size, particulates, prediction, radioactivity, wind
Assessing the risks of radioactive dose in a radiological dispersal device (RDD) attack requires knowledge of how the radiological materials will be spread through the air surrounding the site of the detonation. Two essential parts of the accurate prediction of the behaviour of this dispersion are a characterization of the initial cloud size, directly after the blast, and detailed modelling of the behaviour of different size particulates. Capturing the transport of contaminants from the initial blast wave is integral to achieving accurate predictions, especially for regions where the blast dynamics dominates, but performing such calculations over a wide range of particle sizes and spatial scales is computationally challenging. Formulation of efficient computational techniques for such advanced models is required to provide predictive tools useful to first responders and emergency planners. In this work, a Multi-Cloud Radiological EXplosive Source (MCREXS) modelling approach for RDD is investigated. This approach combines a stochastic, particle-based, mechanistic model with a standard atmospheric dispersion model. The former is used to characterize the distribution of radioactive material near the source of the explosion, where the blast wind effects are important, while the latter is used to model the transport of the contaminant in the environment over large areas. The particle transport in the near-field of the explosion site is computed based on a Lagrangian description of the particle phase and a reconstructed-Eulerian field for the carrier phase. The information inferred from this physics-based model is then used as a starting point for a subsequent standard Gaussian puff model to calculate the dispersion of the radioactive contaminant. The predictive capabilities of the MCREXS model are assessed against the 2012 DRDC Suffield full-scale RDD experiments. The results demonstrate improved predictions relative to those performed using only a Gaussian puff calculation from an empirical initial cloud distribution.