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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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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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A webinar, and a new opportunity to take ANS’s CNP Exam
Applications are now open for the fall 2025 testing period for the American Nuclear Society’s Certified Nuclear Professional (CNP) exam. Applications are being accepted through October 14, and only three testing sessions are offered per year, so it is important to apply soon. The test will be administered from November 12 through December 16. To check eligibility and schedule your exam, click here.
In addition, taking place tomorrow (September 19) from 12:00 noon to 1:00 p.m. (CDT), ANS will host a new webinar, “How to Become a Certified Nuclear Professional.” More information is available below in this article.
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.