Radiological Dispersion Device (RDD) Modeling

Comparison of NARAC predicted and measured ground deposition as a function of downwind distance shows the improvement produced by the use of a dynamic cloud rise algorithm rather than a static cloud model. (Courtesy of Kevin Foster)

Modeling of radiological dispersal devices (RDD) requires dispersion models that treat a variety of radionuclides, material types, and particle sizes. NARAC develops more accurate near-source and downwind RDD models are developed through the use of the latest experimental data on explosive dispersal of materials. Examples include:

Implementation of a dynamic cloud rise model that significantly improves the match to experimental deposition data over that resulting from a static cloud rise approach
Improved cloud rise thermal stabilization heights derived from the Greenfield experiments being conducted in Israel
Addition of a "ballistic” particle correction derived from experiments conducted by Sandia National Laboratories (SNL) that show that larger particles (>100 micrometer diameter) produced by some types of RDDs are ejected and leave the influence of the thermally buoyant cloud faster than previously assumed
Investigation of improved methods for coupling particles to the rising buoyant gas cloud
Evaluation of RDD models against data from recent Israeli and Canadian RDD field experiments using radionuclide surrogates

The incorporation of ballistic particle behavior (right panel) produces a dispersion pattern with increased near-source ground contamination but significantly reduced downwind concentrations relative to the original model prediction (left panel).