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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.
I. Dilber and E. E. Lewis
Nuclear Science and Engineering | Volume 91 | Number 2 | October 1985 | Pages 132-142
Technical Paper | doi.org/10.13182/NSE85-A27436
Articles are hosted by Taylor and Francis Online.
Nodal diffusion and transport methods are formulated variationally in terms of the even-parity form of the neutron transport equation and applied to problems in X-Y geometry. The resulting functional guarantees the satisfaction of nodal balance, regardless of the form of the space-angle trial function within the node or on its boundaries. Deletion of X-Y cross terms from the within-node flux approximations yields equations that are strikingly similar to conventional diffusion nodal methods; inclusion of the terms obviates ad hoc approximations to the transverse leakage. Transport and diffusion nodal methods differ only in the angular basis functions. In both cases the equations are first solved for partial current moments along nodal interfaces. Subsequently, the detailed flux distribution and the node-averaged scalar flux values are obtained from the spatial trial functions. Results are given for fixed-source two-dimensional problems in the P1 and P3 approximations. Code vectorization and generalization to three dimensions are discussed.