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Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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.
Edward W. Larsen, Jinan Yang
Nuclear Science and Engineering | Volume 159 | Number 2 | June 2008 | Pages 107-126
Technical Paper | doi.org/10.13182/NSE07-92
Articles are hosted by Taylor and Francis Online.
In Monte Carlo simulations of k-eigenvalue problems for optically thick fissile systems with a high dominance ratio, the eigenfunction is often poorly estimated because of the undersampling of the fission source. Although undersampling can be addressed by sufficiently increasing the number of particles per cycle, this can be impractical in difficult problems. Here, we present a new functional Monte Carlo (FMC) method that minimizes this difficulty for many problems and yields a more accurate estimate of the k-eigenvalue. In the FMC method, standard Monte Carlo techniques do not directly estimate the eigenfunction; instead, they directly estimate certain nonlinear functionals that depend only weakly on the eigenfunction. The functionals are then used to more accurately estimate the k-eigenfunction and the eigenvalue. Like standard Monte Carlo methods, the FMC method has only statistical errors that limit to zero as the number of particles per cycle and the number of cycles become large. We provide numerical results that illustrate the advantages and limitations of the new method.