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Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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Chicago, IL|Chicago Marriott Downtown
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Nominations open for CNTA awards
Citizens for Nuclear Technology Awareness is accepting nominations for its Fred C. Davison Distinguished Scientist Award and its Nuclear Service Award. Nominations for both awards must be submitted by August 1.
The awards will be presented this fall as part of the CNTA’s annual Edward Teller Lecture event.
Meng Yue, Lap-Yan Cheng, Robert A. Bari
Nuclear Technology | Volume 162 | Number 1 | April 2008 | Pages 26-44
Technical Paper | Reactor Safety | doi.org/10.13182/NT08-A3931
Articles are hosted by Taylor and Francis Online.
A Markov model approach is developed for the evaluation of proliferation resistance (PR) of nuclear energy systems. The focus of this study is to create a high-fidelity probabilistic assessment model that better represents nuclear energy systems. Both extrinsic and intrinsic barriers associated with the energy systems are considered. Modeling uncertainty and safeguards false alarms, composite safeguards approaches, concealment, and human performance are particularly discussed in detail and incorporated in the Markov model. These features are anticipated to have significant impacts on PR assessment. The Markov model approach is adapted to a hypothetical example sodium fast reactor (ESFR) system using physically meaningful parameters that can be obtained from physical processes. Development of metrics for six PR measures is discussed. Computation of the PR measures using the Markov model of the ESFR system is illustrated. The results obtained in this study demonstrate applicability and effectiveness of the Markov model approach in the PR assessment.