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Nuclear Criticality Safety
NCSD provides communication among nuclear criticality safety professionals through the development of standards, the evolution of training methods and materials, the presentation of technical data and procedures, and the creation of specialty publications. In these ways, the division furthers the exchange of technical information on nuclear criticality safety with the ultimate goal of promoting the safe handling of fissionable materials outside reactors.
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Baranwal reviews virtual STEM lessons for U.S. tribal communities
In a blog post to the Department of Energy’s website on November 23, Rita Baranwal, assistant secretary for the Office of Nuclear Energy, commended recent virtual lesson projects from the Office of Nuclear Energy and the Nuclear Energy Tribal Working Group to increase STEM opportunities for Native American tribes.
The spotlighted lesson discussed in the article focused on a 3D-printed clip that turns a smartphone or tablet into a microscope with the ability to magnify items by 100 times. The Office of Nuclear Energy shipped nearly 1,000 of these microscope clips to students across the country, many of them going to U.S. tribal communities.
Christopher M. Perfetti, Bradley T. Rearden
Nuclear Science and Engineering | Volume 193 | Number 10 | October 2019 | Pages 1090-1128
Technical Paper | dx.doi.org/10.1080/00295639.2019.1604048
Articles are hosted by Taylor and Francis Online.
Criticality safety analyses rely on the availability of relevant benchmark experiments to determine justifiable margins of subcriticality. When a target application lacks neutronically similar benchmark experiments, validation studies must provide justification to the regulator that the impact of modeling and simulation limitations is well understood for the application and often must provide additional subcritical margin to ensure safe operating conditions. This study estimated the computational bias in the critical eigenvalue for several criticality safety applications supported by only a few relevant benchmark experiments. The accuracy of the following three methods for predicting computational biases was evaluated: the Upper Subcritical Limit STATisticS (USLSTATS) trending analysis method; the Whisper nonparametric method; and TSURFER, which is based on the generalized linear least-squares technique. These methods were also applied to estimate computational biases and recommended upper subcriticality limits for several critical experiments with known biases and for several cases from a blind benchmark study. The methods are evaluated based on both the accuracy of their predicted computation bias and upper subcriticality limit estimates, as well as on the consistency of the methods’ estimates, as the model parameters, covariance data libraries, and set of available benchmark data were varied. Data assimilation methods typically have not been used for criticality safety licensing activities, and this study explores a methodology to address concerns regarding the reliability of such methods in criticality safety bias prediction applications.