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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.
Yang Liu, Nam Dinh
Nuclear Science and Engineering | Volume 193 | Number 1 | January-February 2019 | Pages 81-99
Technical Paper – Selected papers from NURETH 2017 | doi.org/10.1080/00295639.2018.1512790
Articles are hosted by Taylor and Francis Online.
Two-fluid model-based multiphase computational fluid dynamics (MCFD) has been considered one of the most promising tools to investigate a two-phase flow and boiling system for engineering purposes. The MCFD solver requires closure relations to make the conservation equations solvable. The wall boiling closure relations, for example, provide predictions on wall superheat and heat partitioning. The accuracy of these closure relations significantly influences the predictive capability of the solver. In this paper, a study of validation and uncertainty quantification (VUQ) for the wall boiling closure relations in the MCFD solver is performed. The work has three purposes: (1) to identify influential parameters to the quantities of interest (QoIs) of the boiling system through sensitivity analysis (SA), (2) to evaluate the parameter uncertainty through Bayesian inference with the support of multiple data sets, and (3) to quantitatively measure the agreement between solver predictions and data sets. The widely used Kurul-Podowski wall boiling closure relation is studied in this paper. Several statistical methods are used, including the Morris Screening method for global SA, Markov Chain Monte Carlo for inverse Bayesian inference, and confidence interval as the validation metric. The VUQ results indicate that the current empirical correlations-based wall boiling closure relations achieved satisfactory agreement on wall superheat predictions. However, the closure relations also demonstrate intrinsic inconsistency and fail to give consistently accurate predictions for all QoIs over the well-developed nucleate boiling regime.