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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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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Latest News
Securing the advanced reactor fleet
Physical protection accounts for a significant portion of a nuclear power plant’s operational costs. As the U.S. moves toward smaller and safer advanced reactors, similar protection strategies could prove cost prohibitive. For tomorrow’s small modular reactors and microreactors, security costs must remain appropriate to the size of the reactor for economical operation.
Michael G. Lysenko, Hing-Ip Wong, G. Ivan Maldonado
Nuclear Science and Engineering | Volume 132 | Number 1 | May 1999 | Pages 78-89
Technical Paper | doi.org/10.13182/NSE99-A2050
Articles are hosted by Taylor and Francis Online.
Although artificial neural networks (ANNs) are powerful tools in terms of their high posttraining computational speed and their flexibility to construct complex nonlinear mappings from relatively few known data samples, a survey of past applications of ANNs to the area of core parameter prediction reveals drawbacks such as low prediction accuracy, lack of robust generalization, large network dimensionality, and typically high training requirements. This study provides a brief survey of past and recent applications of ANNs to direct core parameter predictions as well as an alternate hybrid approach that avoids the aforementioned shortcomings of ANNs by combining the mathematical rigor of generalized perturbation theory along with the strong qualities of ANNs in error prediction situations. The results presented focus exclusively on the neutron diffusion's fundamental mode eigenvalue (i.e., 1/keff) and demonstrate the viability of computationally inexpensive adaptive ANN error controllers for perturbation theory applications.