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Division Spotlight
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.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Dragonfly, a Pu-fueled drone heading to Titan, gets key NASA approval
Curiosity landed on Mars sporting a radioisotope thermoelectric generator (RTG) in 2012, and a second NASA rover, Perseverance, landed in 2021. Both are still rolling across the red planet in the name of science. Another exploratory craft with a similar plutonium-238–fueled RTG but a very different mission—to fly between multiple test sites on Titan, Saturn’s largest moon—recently got one step closer to deployment.
On April 25, NASA and the Johns Hopkins University Applied Physics Laboratory (APL) announced that the Dragonfly mission to Saturn’s icy moon passed its critical design review. “Passing this mission milestone means that Dragonfly’s mission design, fabrication, integration, and test plans are all approved, and the mission can now turn its attention to the construction of the spacecraft itself,” according to NASA.
Keehoon Kim, Eric B. Bartlett
Nuclear Technology | Volume 108 | Number 2 | November 1994 | Pages 283-297
Technical Paper | Reactor Control | doi.org/10.13182/NT94-A35035
Articles are hosted by Taylor and Francis Online.
The objective of this research is to develop a fault-diagnostic advisor for nuclear power plant transients that is based on artificial neural networks. A method is described that provides an error bound and therefore a figure of merit for the diagnosis provided by this advisor. The data used in the development of the advisor contain ten simulated anomalies for the San Onofre Nuclear Power Generating Station. The stacked generalization approach is used with two different partitioning schemes. The results of these partitioning schemes are compared. It is shown that the advisor is capable of recognizing all ten anomalies while providing estimated error bounds on each of its diagnoses.
