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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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
DOE issues new NEPA rule and procedures—and accelerates DOME reactor testing
Meeting a deadline set in President Trump’s May 23 executive order “Reforming Nuclear Reactor Testing at the Department of Energy,” the DOE on June 30 updated information on its National Environmental Policy Act (NEPA) rulemaking and implementation procedures and published on its website an interim final rule that rescinds existing regulations alongside new implementing procedures.
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.