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A closer look at the initial NLIC selections—Part 2
In January, the Department of Energy announced its new Nuclear Lifecycle Innovation Campus (NLIC) program, inviting states via a request for information to express their interest in hosting a facility supporting work from the front to the back end of the nuclear fuel cycle.
By April, 26 states had expressed interest in hosting such a facility. At the end of July, the DOE signed memorandums of understanding with five states—Idaho, Louisiana, Oklahoma, Tennessee, and Utah—to more closely explore the possibilities of state-federal partnerships. These MOUs are not firm commitments from either the federal or state governments. Time will tell which—if any—of the five states develop projects through the program. In the meantime, today, we are taking a close look at what Utah, Idaho, Tennessee can offer in terms of a preexisting nuclear sector that could support new fuel cycle developments.
Chen Fu, Peng Xu, Yonggang Huo, Sufen Li, Xingfu Cai
Nuclear Science and Engineering | Volume 196 | Number 9 | September 2022 | Pages 1114-1124
Technical Paper | doi.org/10.1080/00295639.2022.2052551
Articles are hosted by Taylor and Francis Online.
For the problem of searching radioactive sources in a certain area, a search method combining Tsallis divergence strategy with a particle filtering algorithm is proposed. The method this paper proposes for searching for radioactive sources is carried out by using a mobile platform equipped with a NaI(Tl) scintillator detector. The estimation model of the parameters of the radioactive source is constructed and is based on the inverse square law and the fact that the count values of the NaI(Tl) detector in nuclear decay processes obeys the Poisson distribution. The Tsallis divergence strategy is used as a reward function to control the movement of the platform. The posterior distribution of the parameters of the radioactive source is continuously and iteratively updated by using the particle filtering algorithm. The results of Monte Carlo simulations and practical experiments verify the effectiveness of the algorithm.