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Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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The busyness of the nuclear fuel supply chain
Ken Petersenpresident@ans.org
With all that is happening in the industry these days, the nuclear fuel supply chain is still a hot topic. The Russian assault in Ukraine continues to upend the “where” and “how” of attaining nuclear fuel—and it has also motivated U.S. legislators to act.
Two years into the Russian war with Ukraine, things are different. The Inflation Reduction Act was passed in 2022, authorizing $700 million in funding to support production of high-assay low-enriched uranium in the United States. Meanwhile, the Department of Energy this January issued a $500 million request for proposals to stimulate new HALEU production. The Emergency National Security Supplemental Appropriations Act of 2024 includes $2.7 billion in funding for new uranium enrichment production. This funding was diverted from the Civil Nuclear Credits program and will only be released if there is a ban on importing Russian uranium into the United States—which could happen by the time this column is published, as legislation that bans Russian uranium has passed the House as of this writing and is headed for the Senate. Also being considered is legislation that would sanction Russian uranium. Alternatively, the Biden-Harris administration may choose to ban Russian uranium without legislation in order to obtain access to the $2.7 billion in funding.
Michael G. Devereux, Paul Murray, Graeme West
Nuclear Technology | Volume 208 | Number 1 | January 2022 | Pages 115-128
Technical Paper | doi.org/10.1080/00295450.2020.1863067
Articles are hosted by Taylor and Francis Online.
Remote visual inspection is a common approach to understanding the health of key components and substructures within nuclear power plants, particularly in difficult to access and high dosage areas. Interpretation of inspection footage is a manually intensive procedure and challenges arise in localizing and dimensioning defects directly from a video feed, which may be subject to uncertainty from a range of sources such as lens distortion, nonuniform lighting, and lack of depth from a monocular camera system. A common approach to addressing these issues is to develop a scaling factor based on identifying a reference object of known dimensions in the image and using this to size regions of interest. Manual, accurate identification of these reference objects is onerous, time consuming, and prone to variation across different human experts, therefore, robust identification of suitable reference objects in an automated, reliable, and repeatable manner is of significant value. In this paper we evaluate two approaches for the automated detection of reference objects in the inspection of graphite cores in the United Kingdom’s fleet of advanced gas-cooled reactors (AGRs). The first method is a multistep approach using tools from mathematical morphology. The approach uses a genetic algorithm to “grow” suitable structuring elements, refine the order of operations, and remove operations proposed by the human designer that have a negative impact on performance. The second approach uses semantic segmentation, a technique which is normally applied to scene labeling in computer vision applications, applied to produce a binary mask, separating the reference object from the background. We show that this second method performs significantly better than the mathematical morphology approach when applied to the identification of brick interface keyways in AGR inspection images. Though improved in terms of accuracy, it is recognized that a greater initial effort is required to train the approach, and as it utilizes black-box neural network approaches, the greater transparency offered by the mathematical morphology approach is lost. While explicability of techniques is often a highly desirable characteristic of automated analysis techniques applied to health assessment within nuclear power plants, the results of the reference object detection can be made explicit to the end user, ensuring that the human analyst is retained within the decision-making process thus mitigating the need for transparency.