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UIUC submits MMR construction permit application
The University of Illinois–Urbana-Champaign, in partnership with Nano Nuclear Energy, has submitted a construction permit application to the Nuclear Regulatory Commission for construction of a Kronos micro modular reactor (MMR). This is the first major step in the two-part 10 CFR Part 50 licensing process for the research and test reactor and is the culmination of years of technical refinement and regulatory alignment.
The team chose to engage with the NRC in a preapplication readiness assessment, providing the agency with draft versions of the majority of the CPA’s technical content for feedback, which is expected to ensure a high-quality application.
Jian-Yu Zhu, Hao-Wei Dai, Wen-Xiong Xie
Nuclear Technology | Volume 192 | Number 2 | November 2015 | Pages 172-180
Technical Paper | Radiation Measurements and General Instrumentation | doi.org/10.13182/NT14-115
Articles are hosted by Taylor and Francis Online.
Algorithms for locating the neutron source by neutron time-of-flight (TOF) measurement are established and discussed for monoenergetic and multienergetic neutrons in this paper. For the monoenergetic neutron source, the location of the source could be estimated by locating the position where the variance between the actual TOF and the calculation gains its minimum. For multienergetic neutrons the maximum likelihood (ML) method has been applied to process the time-correlation measurement. The efficiencies of location estimations are studies with simulations. In the simulations, the TOFs are acquired by time-correlation measurement of three neutron detectors surrounding the suspected area of the neutron source. The results indicate that the location of monoenergetic neutron sources could be estimated by the neutron TOF acquired by three detectors, while for multienergetic neutron sources, the likelihood method could be used to locate the most probable location, as well as its possible distribution of location. As the result of large-scale simulation and comparison, the ML estimation method is more effective than traditional methods, especially in conditions of low count rates or low resolution.