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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Fusion Science and Technology
Latest News
The RAIN scale: A good intention that falls short
Radiation protection specialists agree that clear communication of radiation risks remains a vexing challenge that cannot be solved solely by finding new ways to convey technical information.
Earlier this year, an article in Nuclear News described a new radiation risk communication tool, known as the Radiation Index, or, RAIN (“Let it RAIN: A new approach to radiation communication,” NN, Jan. 2025, p. 36). The authors of the article created the RAIN scale to improve radiation risk communication to the general public who are not well-versed in important aspects of radiation exposures, including radiation dose quantities, units, and values; associated health consequences; and the benefits derived from radiation exposures.
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.