ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Nuclear Criticality Safety
NCSD provides communication among nuclear criticality safety professionals through the development of standards, the evolution of training methods and materials, the presentation of technical data and procedures, and the creation of specialty publications. In these ways, the division furthers the exchange of technical information on nuclear criticality safety with the ultimate goal of promoting the safe handling of fissionable materials outside reactors.
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!
Latest Magazine Issues
May 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
June 2024
Nuclear Technology
Fusion Science and Technology
Latest News
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.
Jung Hwan Kim, Chul Min Kim, Yong Hee Lee, Man-Sung Yim
Nuclear Technology | Volume 207 | Number 11 | November 2021 | Pages 1753-1767
Regular Technical Paper | doi.org/10.1080/00295450.2020.1837583
Articles are hosted by Taylor and Francis Online.
The safe operation of a nuclear power plant (NPP) can be guaranteed through the team effort of operators in the main control room (MCR). Among the various features, peer checks, concurrent verification, independent verification, and communication reconfirmation are major contributors to effective operations in the MCR. In the digital MCR environment of advanced NPPs, there are potential emerging issues of concern related to these contributors resulting from the use of PC-soft controls for reactor operations. The objective of this study is to investigate the development of quantitative indicators for estimating the implicit intentions of reactor operators as a way to mitigate such concerns. The proposed quantitative indicators support peer checks and concurrent/independent verifications for diagnosing and preventing human errors through communication enhancement in a digital technology-based MCR. A machine learning–based algorithm was used to classify two implicit intentions of agreement and disagreement. The classification was based on electroencephalography data measured from human subjects while they performed mock operational tasks using soft controls. The mock operational tasks were based on using a Windows-based nuclear plant performance analyzer (Win-NPA). Statistical analysis was performed on the measured data to identify significant differences between the agreement and disagreement judgments by the operators. An average classification accuracy of 72% was achieved by using a support vector machine classifier for the Win-NPA task with a low number of features across the various Brodmann areas. The methodology proposed in this study may also serve to enhance communications in conventional MCRs for human error minimization.