Detection of special nuclear material (SNM) being smuggled into the US through ports of entry has been identified as a crucial capability for ensuring the safety and security of the US from radiological threats. Programs such as the NNSA's Second Line of Defense aim to deploy detection systems, both domestically and abroad, in an attempt to interdict the SNM before it reaches its destination. Active interrogation (AI) is a technique that relies on the detection of emitted particles which are produced when SNM is bombarded with a source of high energy photons or neutrons. This work presents a general framework that allows for fast radiation transport modeling of AI scenarios by generating families of response functions which depict neutron, gamma, or electron radiation exiting various regions within the problem, per unit source of radiation entering the region. The solution for a given scenario, typically the detector count rate, is computed by injecting a source term into the first region and applying the appropriate response functions, in sequence, for each subsequent region. For the AI systems modeled in this work, the source is an electron beam in a linear accelerator. Subsequent response functions create and transport bremsstrahlung photons into the SNM, and transport neutrons born in the problem to a detector. The computed solution is comparable to that of a full Monte Carlo simulation, but is assembled in orders of magnitude less time from pre-computed response function libraries. The ability to rapidly compute detector spectra for complicated AI scenarios opens up research and analysis possibilities not previously possible, including conducting parametric studies of scenarios spanning a large portion of the threat space and generating detector spectra used for conditioning and testing of alarm algorithms. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/21158 |
| Date | 11 September 2013 |
| Creators | Armstrong, Hirotatsu |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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