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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
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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NRC begins special inspection at Hope Creek
The Nuclear Regulatory Commission is conducting a special inspection at Hope Creek nuclear plant in New Jersey to investigate the cause of repeated inoperability of one of the plant’s emergency diesel generators, the agency announced in a February 25 news release.
K. Forsberg, Ning He, A. R. Massih
Nuclear Science and Engineering | Volume 122 | Number 1 | January 1996 | Pages 142-150
Technical Note | doi.org/10.13182/NSE96-A28555
Articles are hosted by Taylor and Francis Online.
Distribution of some important fuel rod performance parameters, internal rod pressure, and fission gas release in a boiling water reactor are studied using the quasi-Monte Carlo (QMC) probabilistic method. Rod power histories and important fabrication parameters are considered. The deterministic fuel performance code STAV6 together with a QMC pre- and postprocessor are used in the analysis. The convergence rate of the QMC method is considerably higher than the standard Monte Carlo method, which saves a substantial amount of computer time. Asymptotically, the error for QMC is proportional to 1/N, and for Monte Carlo, it is essentially proportional to 1/ where N is the number of calculations (computer runs). Principles of the QMC method are discussed, and an algorithm to generate such data is outlined.