Browsing by Subject "dirty bomb"
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Item Bayesian Network Analysis of Radiological Dispersal Device Acquisitions(2012-02-14) Hundley, Grant RichardIt remains unlikely that a terrorist organization could produce or procure an actual nuclear weapon. However, the construction of a radiological dispersal device (RDD) from commercially produced radioactive sources and conventional explosives could inflict moderate human casualties and significant economic damage. The vast availability of radioactive sources and the nearly limitless methods of dispersing them demand an inclusive study of the acquisition pathways for an RDD. A complete network depicting the possible acquisition pathways for an RDD could be subjected to predictive modeling in order to determine the most likely pathway an adversary might take. In this work, a comprehensive network of RDD acquisition pathways was developed and analyzed utilizing the Bayesian network analysis software, Netica. The network includes variable inputs and motivations that can be adjusted to model different adversaries. Also, the inclusion of evidence nodes facilitates the integration of real-time intelligence with RDD plot predictions. A sensitivity analysis was first performed to determine which nodes had the greatest impact on successful completion of RDD acquisition. These results detail which portions of the acquisition pathways are most vulnerable to law enforcement intervention. Next, a series of case studies was analyzed that modeled specific adversarial organizations. The analysis demonstrates various features of the constructed Bayesian RDD acquisition network and provides examples of how this tool can be utilized by intelligence analysts and law enforcement agencies. Finally, extreme cases were studied in which the adversary was given the maximum and minimum amount of resources in order to determine the limitations of this model. The aggregated results show that successful RDD acquisition is mostly dependent on the adversary?s resources. Furthermore, the network suggests that securing radiological materials has the greatest effect on interdicting possible RDD plots. Limitations of this work include a heavy dependence on conditional probabilities that were derived from intuition, as opposed to actual historical data which does not exist. However, the model can be updated as attempted or successful RDD plots emerge in the future. This work presents the first probabilistic model of RDD acquisition pathways that integrates adversary motivations and resources with evidence of specific RDD threats.Item Forward model calculations for determining isotopic compositions of materials used in a radiological dispersal device(Texas A&M University, 2005-08-29) Burk, David EdwardIn the event that a radiological dispersal device (RDD) is detonated in the U.S. or near U.S. interests overseas, it will be crucial that the actors involved in the event can be identified quickly. If irradiated nuclear fuel is used as the dispersion material for the RDD, it will be beneficial for law enforcement officials to quickly identify where the irradiated nuclear fuel originated. One signature which may lead to the identification of the spent fuel origin is the isotopic composition of the RDD debris. The objective of this research was to benchmark a forward model methodology for predicting isotopic composition of spent nuclear fuel used in an RDD while at the same time optimizing the fidelity of the model to reduce computational time. The code used in this study was Monteburns-2.0. Monteburns is a Monte Carlo based neutronic code utilizing both MCNP and ORIGEN. The size of the burnup step used in Monteburns was tested and found to converge at a value of 3,000 MWd/MTU per step. To ensure a conservative answer, 2,500 MWd/MTU per step was used for the benchmarking process. The model fidelity ranged from the following: 2-dimensional pin cell, multiple radial-region pin cell, modified pin cell, 2D assembly, and 3D assembly. The results showed that while the multi-region pin cell gave the highest level of accuracy, the difference in uncertainty between it and the 2D pin cell (0.07% for 235U) did not warrant the additional computational time required. The computational time for the multiple radial-region pin cell was 7 times that of the 2D pin cell. For this reason, the 2D pin cell was used to benchmark the isotopics with data from other reactors. The reactors from which the methodology was benchmarked were Calvert Cliffs Unit #1, Takahama Unit #3, and Trino Vercelles. Calvert Cliffs is a pressurized water reactor (PWR) using Combustion Engineering 14??14 assemblies. Takahama is a PWR using Mitsubishi Heavy Industries 17??17 assemblies. Trino Vercelles is a PWR using non-standard lattice assemblies. The measured isotopic concentrations from all three of the reactors showed good agreement with the calculated values.