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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
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.
Christopher Wallace, Curtis McEwan, Graeme West, William Aylward, Stephen McArthur
Nuclear Technology | Volume 206 | Number 5 | May 2020 | Pages 697-705
Technical Paper | doi.org/10.1080/00295450.2019.1697174
Articles are hosted by Taylor and Francis Online.
This paper summarizes a novel approach to improved localization of fuel defects by fusing existing data sources and methods within a neural network model to make accurate and quantifiable identification earlier than existing processes. The approach is demonstrated through application to a CANDU reactor and utilizes a small, manually labeled set of delayed neutron data augmented with neutronic power data to train a neural network to estimate the probability of a fuel channel containing a defect. Results demonstrate that the model is often capable of identifying likely defects earlier than existing methods and could support earlier decision making to enable a reduction in cost and time required to localize defects. The approach described has broader application to other reactor types given the general difficulty of detecting fuel defects via fission product measurement and the large quantities of ancillary parameters normally already recorded that can be leveraged using machine learning techniques.