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Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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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General Kenneth Nichols and the Manhattan Project
Nichols
The Oak Ridger has published the latest in a series of articles about General Kenneth D. Nichols, the Manhattan Project, and the 1954 Atomic Energy Act. The series has been produced by Nichols’ grandniece Barbara Rogers Scollin and Oak Ridge (Tenn.) city historian David Ray Smith. Gen. Nichols (1907–2000) was the district engineer for the Manhattan Engineer District during the Manhattan Project.
As Smith and Scollin explain, Nichols “had supervision of the research and development connected with, and the design, construction, and operation of, all plants required to produce plutonium-239 and uranium-235, including the construction of the towns of Oak Ridge, Tennessee, and Richland, Washington. The responsibility of his position was massive as he oversaw a workforce of both military and civilian personnel of approximately 125,000; his Oak Ridge office became the center of the wartime atomic energy’s activities.”
Mattia Zanotelli, J. Wesley Hines, Jamie B. Coble
Nuclear Science and Engineering | Volume 199 | Number 1 | January 2025 | Pages 100-114
Research Article | doi.org/10.1080/00295639.2024.2303165
Articles are hosted by Taylor and Francis Online.
In the nuclear industry, high system reliability requirements are essential since in-service failure can result in undesirable consequences in terms of costs or safety. However, the current approach to maintaining systems and components is costly and known to involve overly conservative periodic maintenance activities. It is, therefore, appropriate to develop monitoring, detection, and predictive tools to enable operators to create optimal maintenance strategies. These strategies can vary from the substitution of an item to its repair, intending to avoid unexpected consequences. The repair can restore the item’s functionality to an as-good-as-new condition (perfect repair) or sometimes can keep some accumulated degradation and change the item’s degradation rate (imperfect or partial repair). Current techniques and models that can perform prognostics with extraordinary accuracy are often designed on the assumption that following maintenance, the item is restored to an as-good-as-new condition. When these models are used to predict items that follow imperfect repairs, the predictions are likely to be inaccurate. Therefore, the present work focuses on the condition-based prognostics of items, considering and handling the criticalities that arise after the items undergo different kinds of repairs. The proposed solution involves a data-driven framework that employs Left-Right Gaussian Hidden Markov Models (LR-GHMMs). These models can intrinsically manage accumulated degradation. The idea is to train different LR-GHMMs, each specific to a degradation path, and then combine them to cover possible intermediate paths. The effectiveness of the approach is tested in two case studies. In the first one, we consider simple artificial sequences that are useful to explain the method’s capabilities. In the second case study, we consider semi-simulated nuclear data describing the degradation transients of a condenser that undergoes fouling. The framework is trained with data collected from items that start without accumulated degradation. The test data represent either new items or items that undergo imperfect repairs. The results demonstrate an attractive elasticity of the framework in adapting to nonstandard degradation behaviors. In addition, the applications provide interpretable and highly accurate outputs.