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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
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
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
Thomas E. Booth, Shane P. Pederson
Nuclear Science and Engineering | Volume 110 | Number 3 | March 1992 | Pages 254-261
Technical Paper | doi.org/10.13182/NSE92-A23897
Articles are hosted by Taylor and Francis Online.
Historically, Monte Carlo variance reduction techniques have been developed one at a time in response to calculational needs. The theoretical basis is provided for obtaining unbiased Monte Carlo estimates from all possible combinations of variance reduction techniques. Hitherto, the techniques have not been proven to be unbiased in arbitrary combinations. The authors are unaware of any Monte Carlo techniques (in any linear process) that are not treated by the theorem herein.