ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
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!
Latest Magazine Issues
Jun 2025
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Latest Journal Issues
Nuclear Science and Engineering
August 2025
Nuclear Technology
July 2025
Fusion Science and Technology
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.
Jonghwan Kim, Byunyoung Jung, Junhong Park, Youngchul Choi
Nuclear Technology | Volume 208 | Number 7 | July 2022 | Pages 1184-1191
Technical Paper | doi.org/10.1080/00295450.2021.2018271
Articles are hosted by Taylor and Francis Online.
A pipe wall thinning diagnosis method based on vibration characteristics is proposed. Elbow specimens with artificial pipe wall thinning were fabricated and combined in a loop. By running a pump in the loop, vibration was induced by flow, and the vibrational signals were measured with accelerometers. The effect of pipe wall thinning on the vibrational signals was investigated by analyzing the spectral data of the acceleration signals. The analyzed vibration characteristics were difficult to observe because the change in characteristics was small. A convolutional neural network (CNN) specialized for data recognition was applied to recognize the small change in vibrational signal resulting from the pipe wall thinning. A regression model based on CNN was chosen to learn the tendency of change in the vibrational signals with varying thinning. The data types advantageous for training the regression model were identified. An early stopping technique using the validation data set was adopted to regularize the regression model. The trained regression model was able to predict pipe thinning.