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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.
Hashem Akbari, Lawrence M. Grossman
Nuclear Technology | Volume 49 | Number 3 | August 1980 | Pages 400-409
Technical Paper | Reactor Siting | doi.org/10.13182/NT80-A17688
Articles are hosted by Taylor and Francis Online.
A methodology is developed to optimize the size and the location of power plants supplying given demand centers by minimizing the cost of transmission lines and plant capital costs subject to the physical constraint that the power plants must be located within a predetermined feasible geographical region. The optimization problem falls within a class of mixed integer nonlinear constrained programming for which no general method of solution exists. Optimization is carried out in two steps to separate considerations of integer and continuous variables. A complete set of possible configuration alternatives in terms of the number of power plants is first generated by examining the comers of a polyhedron set defined by the upper and lower bounds on the number of power plants at each location, with the demand satisfied through a predefined directed transmission network. Then, through a constrained nonlinear programming technique, the optimum location for each promising, feasible alternative is calculated. The best alternative, i.e., the one having the minimum total cost, is selected as the optimum solution.