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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.
M. Nowak, D. Mancusi, D. Sciannandrone, E. Masiello, H. Louvin, E. Dumonteil
Nuclear Science and Engineering | Volume 193 | Number 9 | September 2019 | Pages 966-981
Technical Paper | doi.org/10.1080/00295639.2019.1578568
Articles are hosted by Taylor and Francis Online.
In radiation protection studies, the goal is to estimate the response of a detector exposed to a strongly attenuated radiation field. Monte Carlo (MC) particle transport codes give the possibility to efficiently solve for such responses using several variance-reduction (VR) methods that help allocating more CPU time to the simulation of highly contributing histories. The TRIPOLI-4® MC particle transport code offers two main methods, the exponential transform and adaptive multilevel splitting (AMS), which rely on the definition of a suitable importance map. In this paper, we present an implementation of a generalized Consistent Adjoint Driven Importance Sampling (CADIS) methodology for TRIPOLI-4. The implementation relies on coupling with the IDT code, a deterministic solver for the Boltzmann adjoint transport equation, for the generation of importance maps. We study the performance of both VR methods present in TRIPOLI-4 in this setting. In particular, to our knowledge, this is the first time that a CADIS-like methodology has been applied to AMS.