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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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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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Latest News
X-energy receives federal tax credit for TRISO fuel facility
Advanced reactor company X-energy has been awarded $148.5 million in tax credits under the Inflation Reduction Act for construction of its TRISO-X fuel fabrication facility in Oak Ridge, Tenn.
T. Craciunescu, A. Murari, I. Tiseanu, J. Vega, JET-EFDA Contributors
Fusion Science and Technology | Volume 62 | Number 2 | October 2012 | Pages 339-346
Selected Paper from the Seventh Fusion Data Validation Workshop 2012 (Part 1) | doi.org/10.13182/FST12-A14625
Articles are hosted by Taylor and Francis Online.
Multifaceted asymmetric radiation from the edge (MARFE) instabilities may reduce confinement leading to harmful disruptions. They cause a significant increase in impurity radiation, and therefore, they leave a clear signature in the video data. This information can be exploited for automatic identification and tracking. A MARFE classifier, based on the phase congruency theory, has been developed and adjusted to extract the structural information in the images of Joint European Torus (JET) cameras. This approach has the advantage of using a dimensionless quantity and providing information that is invariant to image illumination, contrast, and magnification. The method was tested on JET experimental data and has proved to provide a good prediction rate.