Year
2010
Abstract
The final assessment and detection of a diversion of nuclear material still falls firmly on the shoulders of the human analyst. However, the combined use of statistical analysis with a 3-D representation that places physical events in context improves the likelihood that a diversion of material would be quickly detected. Communications between applications can serve as the basis for expanding the breadth and quality of the information provided to an end user. For purposes of improving the detection of the diversion of nuclear material, Los Alamos National Laboratory integrated 3-D models with a Bayesian network-based computational engine that evaluates seemingly disparate events at a nuclear facility. Using simple messaging protocols, we established communications between a 3-D dynamic interactive model of a nuclear facility and the Integrated Knowledge Engine (IKE) software. The integration of these two applications has resulted in a greatly improved interface for events of interest – providing not only a statistically based assessment, but also a 3-D situational-based context for evaluation. The output shows the physical context of relevant activities occurring at a facility thereby supporting an expedited understanding and assessment of potential material diversion activities by inspectors. We will demonstrate a series of diversion scenarios at a reprocessing facility, showing both statistical analysis output coupled with 3-D context for the perceived predicted events.