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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Empowering the next generation: ANS’s newest book focuses on careers in nuclear energy
A new career guide for the nuclear energy industry is now available: The Nuclear Empowered Workforce by Earnestine Johnson. Drawing on more than 30 years of experience across 16 nuclear facilities, Johnson offers a practical, insightful look into some of the many career paths available in commercial nuclear power. To mark the release, Johnson sat down with Nuclear News for a wide-ranging conversation about her career, her motivation for writing the book, and her advice for the next generation of nuclear professionals.
When Johnson began her career at engineering services company Stone & Webster, she entered a field still reeling from the effects of the Three Mile Island incident in 1979, nearly 15 years earlier. Her hiring cohort was the first group of new engineering graduates the company had brought on since TMI, a reflection of the industry-wide pause in nuclear construction. Her first long-term assignment—at the Millstone site in Waterford, Conn., helping resolve design issues stemming from TMI—marked the beginning of a long and varied career that spanned positions across the country.
Young Ho Chae, Poong Hyun Seong (KAIST), Jung Taek Kim (KAERI)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 957-966
The operating condition of secondary loop of nuclear power plant has the characteristics that are vulnerable to flow accelerated corrosion phenomena. Because of the flow accelerated corrosion, from 1970 to 2012, in the world 1987 number of events were occurred. [1] Nuclear power plant utilities try to estimate the flow accelerated corrosion induced wall thinning by using CHECWORKS code. CHECWORKS code is based on empirical test results of the pipes. Therefore, CHECWORKS code can only estimate the pipe, which has empirical test result. However, in reality, extract the whole test result from the secondary system is almost impossible. Therefore, for the pipes which are not listed on the CHECWORKS code, ultrasonic measurements were conducted during the maintenance period. For the ultrasonic measure, the insulators in the secondary system should be removed therefore, the measure entails huge works. To overcome this issue, Jung Taek Kim et al. [2] focused on the change of pipes' vibration characteristic due to wall thinning effect. By using vibration signal, pipes thinning condition can be diagnosed in online. Jung Taek Kim used Fourier Transform to analyze vibration characteristics. However, pipes' vibration change was too tiny to classify the differences. By using pre-trained wall thinning classifier, we tried to find possible vibration characteristic. To generate vibration mode, generative adversarial network model is used. After the several training sequences, the generator which is the part of the generative adversarial network imitate vibration data. By combining pre-trained diagnosis network and generator, unknown vibration characteristics may be found. In this study, to estimate pipes' thinning condition several machine learning algorithms (Support vector machine, Convolutional neural network, and Long-short term memory network) were reviewed and applied. Each algorithms were trained by using pipes' vibration signal. As a results, LSTM network shows best classification performance. And also, several vibration modes were imitated by using generative adversarial network.