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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.
Pratibha Yadav, Reuven Rachamin, Jörg Konheiser, Silvio Baier
Nuclear Science and Engineering | Volume 198 | Number 2 | February 2024 | Pages 497-507
Research Article | doi.org/10.1080/00295639.2023.2211199
Articles are hosted by Taylor and Francis Online.
In nuclear engineering, Monte Carlo (MC) methods are commonly used for reactor analysis and radiation shielding problems. These methods are capable of dealing with both simple and complex system models with accuracy. The application of MC methods experiences challenges when dealing with the deep penetration problems that are typically encountered in radiation shielding cases. It is difficult to produce statistically reliable results due to poor particle sampling in the region of interest. Therefore, such calculations are performed by the Monte Carlo N-Particle Transport (MCNP) code in association with the weight window (WW) variance reduction technique, which increases the particle statistics in the desired tally region. However, for large problems, MCNP’s built-in weight window generator (WWG) produces zero WW parameters for tally regions located far from the source. To address this issue, the recursive Monte Carlo (RMC) method was proposed. This paper focuses on the RMC methodology and its implementation in the Helmholtz-Zentrum Dresden-Rossendorf’s (HZDR’s) in-house code TRAWEI, which is responsible for producing optimal zone weight parameters used for optimizing deep penetration MC calculations. In addition, this paper discusses the verification of the TRAWEI weight generator program to that of an existing MCNP WWG. The performance of TRAWEI-generated weight values is assessed using a handful of test cases involving two shield materials. Globally, the TRAWEI-generated weight values improved not only the statistical variance and computational efficiency of the MC run compared to the analog MCNP simulation but also those of the simulation with WW values generated by the standard MCNP WWG.