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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.
Jeremy Lloyd Conlin, Stephen J. Tobin, Adrienne M. LaFleur, Jianwei Hu, TaeHoon Lee, Nathan P. Sandoval, Melissa A. Schear
Nuclear Science and Engineering | Volume 169 | Number 3 | November 2011 | Pages 314-328
Technical Paper | doi.org/10.13182/NSE10-88
Articles are hosted by Taylor and Francis Online.
The quantification of the plutonium mass in spent nuclear fuel assemblies is an important measurement for nuclear safeguards practitioners. A program is well underway to develop nondestructive assay instruments that, when combined, will be able to quantify the plutonium content of a spent nuclear fuel assembly. Each instrument will quantify a specific attribute of the spent fuel assembly, e.g., the fissile content. In this paper, we present a Monte Carlo-based method of estimating the mean and distribution of some assembly attributes. An MCNPX model of each instrument has been created, and the response of the instrument was simulated for a range of spent fuel assemblies with discrete parameters (e.g., burnup, initial enrichment, and cooling time). The Monte Carlo-based method interpolates between the modeled results for an instrument to emulate a response for parameters not explicitly modeled. We demonstrate the usefulness of this technique in applying the technique to six different instruments under investigation. The results show that this Monte Carlo-based method can be used to estimate the assembly attributes of a spent fuel assembly based upon the measured response from the instrument.