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Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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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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Strong performances across the board
Craig Piercycpiercy@ans.org
Another year, another stellar performance by America’s nuclear plants. We’ve come to expect high capacity factors, and it’s a credit to the men and women of the profession. They’ve made routine something that was unimaginable not so long ago.
The decadal challenge for the nuclear enterprise now is to maintain this high level of operational excellence for the current fleet, while at the same time ushering in a new generation of technologies at scale. It will be a big job—but one that seems more and more likely with each passing day.
Andrew T. Till, Marvin L. Adams, Jim E. Morel
Nuclear Science and Engineering | Volume 196 | Number 1 | January 2022 | Pages 53-74
Technical Paper | doi.org/10.1080/00295639.2021.1932224
Articles are hosted by Taylor and Francis Online.
Energy discretization of the transport equation is difficult due to numerous strong, narrow cross-section (XS) resonances. The standard traditional multigroup (MG) method can be sensitive to approximations in the weighting spectrum chosen for XS averaging, which can lead to inaccurate treatment of important phenomena such as self-shielding. We generalize the concept of a group to a discontiguous range of energies to create the Finite-Element with Discontiguous-Support (FEDS) method. FEDS uses clustering algorithms from machine learning to determine optimal definitions of discontiguous groups. By combining parts of multiple resonances into the same group, FEDS can accurately treat resonance behavior even when the number of groups is orders of magnitude smaller than the number of resonances. In this paper, we introduce the theory of the FEDS method and describe the workflow needed to use FEDS, noting that ordinary MG codes can use FEDS XSs without modification, provided these codes can handle upscattering. This allows existing MG codes to produce FEDS solutions. In the context of light water reactors, we investigate properties of FEDS XSs compared to MG XSs and compare -eigenvalue and reaction rate quantities of interest to continuous-energy Monte Carlo, showing that FEDS provides higher accuracy and less cancellation of error than MG with expert-chosen group structures.