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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
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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
NRC engineers share their expertise at the University of Puerto Rico
Robert Roche-Rivera and Marcos Rolón-Acevedo are licensed professional engineers who work at the U.S. Nuclear Regulatory Commission. They are also alumni of the University of Puerto Rico–Mayagüez (UPRM) and have been sharing their knowledge and experience with students at their alma mater since last year, serving as adjunct professors in the university’s Department of Mechanical Engineering. During the 2023–2024 school year, they each taught two courses: Fundamentals of Nuclear Science and Engineering, and Nuclear Power Plant Engineering.
Mihály Makai, Zoltán Szatmáry
Nuclear Science and Engineering | Volume 177 | Number 1 | May 2014 | Pages 52-67
Technical Paper | doi.org/10.13182/NSE12-97
Articles are hosted by Taylor and Francis Online.
In the Monte Carlo (MC) method, statistical noise is usually present, and it may become dominant in the calculation of a distribution, usually by iteration, but it is less important in calculating integrals. The subject of the present work is the role of statistical noise in iterations involving stochastic simulation (the MC method). Convergence is checked by comparing two consecutive solutions in the iteration. The statistical noise may randomize or pervert the convergence. We study the probability of convergence and the correct estimation of the variance in a simplified model problem. We also study the statistical properties of the solution to a deterministic problem with a stochastic source obtained from a stochastic calculation. There are iteration strategies resulting in nonconvergence or a randomly stopped iteration.