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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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.
Ross Hays, Paul Turinsky
Nuclear Technology | Volume 186 | Number 1 | April 2014 | Pages 76-89
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT13-68
Articles are hosted by Taylor and Francis Online.
The process of transitioning from the current once-through nuclear fuel cycle to a hypothetical closed fuel cycle necessarily introduces a much greater degree of supply feedback and complexity. When considering such advanced technologies, it is necessary to consider when and how fuel cycle facilities can be deployed in order to avoid resource conflicts while maximizing certain stakeholder values. A multiobjective optimization capability was developed around the VISION nuclear fuel cycle simulation code to allow for the automated determination of optimum deployment scenarios and objective trade-off surfaces for dynamic fuel cycle transition scenarios. A parallel simulated annealing optimization framework with modular objective function definitions is utilized to maximize computational power and flexibility. Three sample objective functions representing a range of economic and sustainability goals are presented, as well as representative optimization results demonstrating both robust convergence toward a set of optimum deployment configurations and a consistent set of trade-off surfaces.