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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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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.
Axel Hoefer, Oliver Buss, Michael Schmid
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1578-1587
Technical Paper | doi.org/10.1080/00295450.2018.1560784
Articles are hosted by Taylor and Francis Online.
A general Bayesian framework for best-estimate plus uncertainty predictions of multidimensional continuous observables is presented. Parameterizing uncertainties in terms of multivariate normal distribution models, this Multivariate normal Bayesian model (MNBM) framework allows one to include both measured data and linear constraints in a mathematically consistent way. The resulting updating formulas are generalizations of the updating formulas of the Generalized Linear Least Squares (GLLS) framework, which is widely used for the generation of adjusted nuclear data libraries. While the GLLS methodology is restricted to first-order perturbation theory, there is no such restriction for the considered MNBM framework. This makes it possible to use Monte Carlo uncertainty propagation and to apply the updating formulas directly to the observables of interest without having to first update the input parameter distributions. After a general presentation of the MNBM framework and a brief discussion of its possible applications, the generation of bounding burnup-dependent axial burnup profiles of light water reactor fuel assemblies for the purpose of criticality safety analysis is discussed as an example application.