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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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Powering the future: How the DOE is fueling nuclear fuel cycle research and development
As global interest in nuclear energy surges, the United States must remain at the forefront of research and development to ensure national energy security, advance nuclear technologies, and promote international cooperation on safety and nonproliferation. A crucial step in achieving this is analyzing how funding and resources are allocated to better understand how to direct future research and development. The Department of Energy has spearheaded this effort by funding hundreds of research projects across the country through the Nuclear Energy University Program (NEUP). This initiative has empowered dozens of universities to collaborate toward a nuclear-friendly future.
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.
