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Conference Spotlight
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.
Ian Wall and Henri Fenech
Nuclear Science and Engineering | Volume 22 | Number 3 | July 1965 | Pages 285-297
Technical Paper | doi.org/10.13182/NSE65-A20933
Articles are hosted by Taylor and Francis Online.
The fuel management optimization of a nuclear power plant is separable from the over-all optimum design. It has weak interactions with the core design and poison management which may be expressed by constraints upon the maximum permissible fuel burnup and ratio of peak-to-average power density (power peaking). Each time the reactor becomes subcritical, a decision must be made as to which fuel should be discharged and replaced and to what degree rearrangement is advantageous. This is a multistage decision process whose objective is the minimum power cost over the plant life. A dynamic programing algorithm and a computer program have been developed to optimize the refueling policies of a single-enrichment, three-zone, 1000-MWe PWR core for a minimum unit power cost. The major assumptions necessary for this method are the representation of the fuel composition by the sole parameter, burnup, and the prediction of the system behavior by least-squares polynomial curves fitted to prior calculations. These approximations have been verified and their accuracy is about 3%. Many problems are displayed to demonstrate the application of the method. The cost figures given in the numerical examples are for illustration purposes only and may not reflect current manufacturers' and utilities' policies.