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Division Spotlight
Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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!
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Latest News
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
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.
