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

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第17918号 / エネ博第290号 / 新制||エネ||60(附属図書館) / 30738 / 京都大学大学院エネルギー科学研究科エネルギー応用科学専攻 / (主査)教授 大垣 英明, 教授 白井 康之, 教授 松田 一成 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DGAM

This thesis covers the individual work of Michael Chin as part of the sponsored research project funded by the U.S. State Department in support of a computational design of a "Mobile Pit Verification System" (MPVS), a mobile “drive by” passive radiation detection system to be applied in special nuclear materials (SNM) storage facilities for validation and compliance purposes. The MPVS system is intended to enable a comprehensive, rapid verification and validation of stored nuclear weapon core physics packages containing SNM, or so-called “weapon pits,” in weapon materials and stockpile storage facilities. The MPVS platform is designed to move at a constant speed and accumulate a signal for each stored weapon pit container. The gamma detector was selected to be a 4 × 4 × 8 cubic inch CsI detector while the neutron detector array designed for the “Transport Simulation and Validation of a Synthetic Aperture SNM Detection System (“T-SADS”) project was used in conjunction with this work; T-SADS was a 3 year project funded by DOE-NNSA which was completed on May 2013. The computational design effort for this project was completed in April 2013, and leveraged novel computational radiation transport methods, algorithms, and SNM identification methods, including a synthetic aperture collection approach, and a new gamma ratio methodology for distinguishing between naturally occurring radiation materials and weapon class SNM materials. Both forward and adjoint transport methods were utilized to characterize the adjoint reaction rate as a function of inter-source spacing, collimation thickness, linear and angular field of view, source age, source type, source geometry, and mobile platform speed. The integrated count was then compared with background radiation and the associated probabilities of detection and false alarm were then computed. Publications resulting from this research were published in PHYSOR 2012, presented at the 53rd annual Proceedings of the INMM, and at the Mathematics & Computation 2013 Conference.