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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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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Nuclear Science and Engineering
Fusion Science and Technology
University of Florida-led consortium to research nuclear forensics
A 16-university team of 31 scientists and engineers, under the title Consortium for Nuclear Forensics and led by the University of Florida, has been selected by the Department of Energy’s National Nuclear Security Administration (NNSA) to develop the next generation of new technologies and insights in nuclear forensics.
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.