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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.
Todd J. Urbatsch, R. Arthur Forster, Richard E. Prael, Richard J. Beckman
Nuclear Technology | Volume 111 | Number 2 | August 1995 | Pages 169-182
Technical Paper | Nuclear Criticality Safety Special / Fission Reactor | doi.org/10.13182/NT95-A35128
Articles are hosted by Taylor and Francis Online.
The Monte Carlo code MCNP has three different, but correlated, estimators for calculating keff in nuclear criticality calculations: collision, absorption, and track length estimators. The combination of these three estimators, the three-combined keff estimator, is shown to be the best keff estimator available in MCNP for estimating keff confidence intervals. Theoretically, the Gauss-Markov theorem provides a solid foundation for MCNP’s three-combined estimator. Analytically, a statistical study, where the estimates are drawn using a known covariance matrix, shows that the three-combined estimator is superior to the estimator with the smallest variance. Empirically, MCNP examples for several physical systems demonstrate the three-combined estimator’s superiority over each of the three individual estimators and its correct coverage rates. Additionally, the importance of MCNP’s statistical checks is demonstrated.