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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.
N. V. Kornilov, S. M. Grimes, A. Voinov
Nuclear Science and Engineering | Volume 172 | Number 3 | November 2012 | Pages 278-286
Technical Paper | doi.org/10.13182/NSE11-61
Articles are hosted by Taylor and Francis Online.
The variations of ˜14-MeV (n, p), (n, ), and (n, 2n) reaction cross sections with A and Z have been analyzed. We tried to answer a rather interesting question: Can a simple parameterization be useful in comparing with nuclear reaction model calculations? In addition, we checked several approaches for parameterization. Simple systematics gave a better prediction than model calculation for the (n, 2n) reaction at A > 120. At a low mass number, the difference between experimental data and calculated or fitted results may be connected with the structure of levels for residual nuclei. We saw better agreement between experimental and fitted data in comparison with results of model calculation in particular for the (n, ) reaction for A < 110. Both approaches failed to predict (n, p) cross sections inside experimental uncertainties for A < 110 and the (n, ) cross section for A > 110. This failure may be connected with low accuracy of experimental data or with some unknown physical effect that provides an additional splitting of experimental data.