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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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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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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
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.
