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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Juan Jose Ortiz, Ignacio Requena
Nuclear Science and Engineering | Volume 143 | Number 3 | March 2003 | Pages 254-267
Technical Paper | doi.org/10.13182/NSE03-A2334
Articles are hosted by Taylor and Francis Online.
The problem of optimizing refueling in a nuclear boiling water reactor is difficult since it concerns combinatorial optimization and it is NP-Complete. In order to solve this problem, many techniques have been applied, ranging from expert systems to genetic algorithms. In most of these procedures, nuclear reactor simulators are used, which require a longer computation time, to evaluate the goodness of the proposed solutions. As the processes are iterative, many evaluations with the simulator are necessary, and this makes the process extremely slow. In this paper, the use of trained neural networks (NNs) is proposed as an alternative to the simulator, and the results of the NN training are shown in order to predict some variables of interest in the optimization, such as the effective multiplication factor and some thermal limits, related to safety aspects. Finally, a study about the effect of modifying several NN parameters is shown.