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2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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Ho Nieh, TVA board members, and nuclear fuel recycling bill head to Senate floor
Nieh
Ho Nieh, the Trump administration’s nominee to be a member of the Nuclear Regulatory Commission, and four new board members of the Tennessee Valley Authority were approved in a vote today by the Senate Environment and Public Works Committee and head to the Senate floor for a final vote.
The committee also voted to advance to the Senate floor the Nuclear REFUEL Act of 2025 (S. 2082), which would smooth the regulatory pathway for recycling used nuclear fuel.
President Donald nominated Nieh on July 30 to serve as NRC commissioner for the remainder of a term set to expire June 30, 2029, which was held by former NRC commissioner Chris Hanson, who Trump fired in June.
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.