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Division Spotlight
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.
Meeting Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Hinkley Point C gets over $6 billion in financing from Apollo
U.S.-based private capital group Apollo Global has committed £4.5 billion ($6.13 billion) in financing to EDF Energy, primarily to support the U.K.’s Hinkley Point C station. The move addresses funding needs left unmet since China General Nuclear Power Corporation—which originally planned to pay for one-third of the project—exited in 2023 amid U.K. government efforts to reduce Chinese involvement.
Chenhao Zeng, Wanchang Lai, Jinge Zhou, Hongjian Lin, Xiaojie Feng, Yongping Yu, Runqiu Gu, Shangqing Sun, Jinfei Wu
Nuclear Technology | Volume 209 | Number 4 | April 2023 | Pages 549-559
Technical Paper | doi.org/10.1080/00295450.2022.2133515
Articles are hosted by Taylor and Francis Online.
We address the performance of airborne gamma detection systems equipped with a NaI(Tl) detector to monitor radionuclides in specific areas. In particular, we analyze the use of the fast singular value decomposition (FSVD) algorithm to improve the nuclide recognition ability of the system and effectively trace radioactivity in a complex background environment. We first present a theoretical analysis of the FSVD algorithm and illustrate the nuclide recognition algorithm step by step. The core of the algorithm is singular value decomposition and parameter estimation based on a Gaussian Markov linear regression model. From the estimated values of the parameters, information about radionuclides can be effectively extracted. We assume the presence of a strong background due to a high concentration of 222Rn and its progeny, which is simulated using GEANT4. By adding trace elements of 131I and 137Cs and changing the relative emissivity, the ratio of the total energy peak count of 131I and 137Cs to the background environment interval count of the corresponding 222Rn and its progeny are controlled. Assuming a counting ratio equal to 0.005, the FSVD algorithm is still able to effectively discriminate the presence of a small number of nuclides, reflecting very excellent recognition ability. Finally, based on data from an airborne gamma detection system in a self-control radon chamber, the FSVD algorithm is employed to recognize the trace of 137Cs nuclides in a strong radon background. A DURRIDGE RAD7 radon measuring instrument is used to monitor the radon concentration in the radon chamber. The actual measurement results show that the FSVD algorithm can effectively detect 137Cs nuclides.
