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Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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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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Fusion Science and Technology
Latest News
ARPA-E announces $40 million to develop transmutation technologies for UNF
The Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) announced $40 million in funding to develop cutting-edge technologies to enable the transmutation of used nuclear fuel into less-radioactive substances. According to ARPA-E, the new initiative addresses one of the agency’s core goals as outlined by Congress: to provide transformative solutions to improve the management, cleanup, and disposal of radioactive waste and spent nuclear fuel.
Stefano Carli, Roberto Bonifetto, Tiago Pomella Lobo, Laura Savoldi, Roberto Zanino
Fusion Science and Technology | Volume 68 | Number 2 | September 2015 | Pages 336-340
Technical Paper | Proceedings of TOFE-2014 | doi.org/10.13182/FST14-986
Articles are hosted by Taylor and Francis Online.
In a tokamak with superconducting magnets, the operation of the cryoplant requires the knowledge of the heat load coming from the cryogenic loops that cool the different magnet systems.
Artificial Neural Networks (ANNs) are applied for the first time to the ITER Toroidal Field (TF) magnets. Two different models are developed: 1) a simpler one, aiming at checking the effects of the different operating scenarios on the cryoplant; 2) a more complex one, aiming at helping in the design of suitable control strategies for the magnet operation, to reduce the variation of the heat load to the cryoplant.
The developed ANNs are suitably trained based on results obtained with the state-of-the-art thermal-hydraulic code 4C, that simulates the TF magnet response when subject to a broad spectrum of heat load variations. The predictive capability of the resulting ANN models is tested in different operating scenarios.