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Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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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First astatine-labeled compound shipped in the U.S.
The Department of Energy’s National Isotope Development Center (NIDC) on March 31 announced the successful long-distance shipment in the United States of a biologically active compound labeled with the medical radioisotope astatine-211 (At-211). Because previous shipments have included only the “bare” isotope, the NIDC has described the development as “unleashing medical innovation.”
Nathan Siu, Ali Mosleh
Nuclear Technology | Volume 84 | Number 3 | March 1989 | Pages 265-281
Technical Paper | Probabilistic Safety Assessment and Risk Management / Nuclear Safety | doi.org/10.13182/NT89-A34210
Articles are hosted by Taylor and Francis Online.
Uncertainties in the estimation of parameters for common-cause failure models arise not only because of the small number of common-cause failure events but also because recorded events may not be relevant to the analysis of a particular plant. The data base for a plant-specific analysis may therefore be uncertain. A Bayesian methodology for treating data base uncertainties in the estimation of common-cause failure model parameters is developed and applied to a three-pump auxiliary feedwater system. Sensitivity analyses show that the results are not strongly sensitive to assumptions concerning prior distribution type and shape, but do depend somewhat on the degree of state-of-knowledge dependence between uncertain events. These analyses also show that ignoring the uncertainties in the data can lead to significant estimation errors. Finally, an approximate methodology for treating uncertain data is examined; this method provides reasonable estimates of the mean values of the common-cause failure model parameters, but underpredicts the uncertainty in these parameters.