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Division Spotlight
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
WIPP improves utility shaft safety, begins infrastructure project
Harrison Western Shaft Sinkers (HWSS), the company drilling a new utility shaft at the Department of Energy’s Waste Isolation Pilot Plant in New Mexico, has retained a safety culture expert following a near-miss accident in the shaft late last year. The safety expert will conduct monthly facilitated discussions with crews working on the shaft to reinforce expectations for identifying concerns regarding unsafe circumstances, according to a recent report by the Defense Nuclear Facilities Safety Board (DNFSB).
Juan José Ortiz, Ignacio Requena
Nuclear Science and Engineering | Volume 146 | Number 1 | January 2004 | Pages 88-98
Technical Paper | doi.org/10.13182/NSE04-A2395
Articles are hosted by Taylor and Francis Online.
A genetic algorithm is used to optimize the nuclear fuel reload for a boiling water reactor, and an order coding is proposed for the chromosomes and appropriate crossover and mutation operators. The fitness function was designed so that the genetic algorithm creates fuel reloads that, on one hand, satisfy the constrictions for the radial power peaking factor, the minimum critical power ratio, and the maximum linear heat generation rate while optimizing the effective multiplication factor at the beginning and end of the cycle. To find the values of these variables, a neural network trained with the behavior of a reactor simulator was used to predict them. The computation time is therefore greatly decreased in the search process. We validated this method with data from five cycles of the Laguna Verde Nuclear Power Plant in Mexico.