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Division Spotlight
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.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Yong Xu, Yunze Cai, Lin Song
Nuclear Technology | Volume 209 | Number 7 | July 2023 | Pages 929-962
Critical Review | doi.org/10.1080/00295450.2023.2169042
Articles are hosted by Taylor and Francis Online.
The condition assessment of equipment in nuclear power plants (NPPs) could provide essential information for operation and maintenance decisions, which would have a significant impact on improving the safety and economy of NPPs. To date, substantial work has been conducted on the condition assessment based on machine learning for NPP equipment. To provide a comprehensive overview for researchers interested in developing machine learning methods for NPP equipment condition assessment, this critical review presents a detailed literature survey on state-of-the-art research and identifies challenges for future study. Valuable information is presented, including major failure modes, data sources, maintenance strategies, and the relationship between equipment lifetime, assessment technology, and maintenance strategy. Following the typical lifetime of NPP equipment for condition assessment, current works in this domain are categorized into anomaly detection, remaining useful life prediction, and fault detection and diagnosis. The techniques and methodologies adopted in the literature are summarized from each aspect. In particular, the in-depth NPP equipment condition assessment survey based on deep learning methods is presented. In addition, we elaborate on current issues, challenges, and future research directions for the condition assessment of equipment in NPPs. These directions we believe will pave the way for equipment condition assessment.