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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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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
Can hydrogen be the transportation fuel in an otherwise nuclear economy?
Let’s face it: The global economy should be powered primarily by nuclear power. And it probably will by the end of this century, with a still-significant assist from renewables and hydro. Once nuclear systems are dominant, the costs come down to where gas is now; and when carbon emissions are reduced to a small portion of their present state, it will become obvious that most other sources are only good in niche settings. I mean, why use small modular reactors to load-follow when they can just produce that power instead of buffering it?
David J. Kropaczek, Ryan Walden
Nuclear Science and Engineering | Volume 193 | Number 5 | May 2019 | Pages 506-522
Technical Paper | doi.org/10.1080/00295639.2018.1554173
Articles are hosted by Taylor and Francis Online.
A method is developed, assessed, and demonstrated for addressing objective functions and constraints within the context of combinatorial optimization problems. The penalty-free method developed, referred to as constraint annealing, eliminates the use of traditional constraint penalty factors by treating the objective functions and constraints as separate and concurrently solved minimization problems within a global optimization search framework. The basis of the constraint annealing algorithm is a highly scalable method based on the method of parallel simulated annealing with mixing of states. Unique to constraint annealing is a novel approach that employs both global solution acceptance and local objective function and constraint statistics in the calculation of adaptive cooling temperatures that are specific to each objective function and constraint. The constraint annealing method is assessed against a traditional penalty-factor approach for a realistic core loading pattern design problem and shown to be robust with respect to elimination of arbitrary weighting factors on constraint values. In addition, the constraint annealing method is demonstrated to be robust with respect to parallel scaling as well as improved optimization performance on high-performance-computing systems.