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The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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Strontium: Supply-and-demand success for the DOE’s Isotope Program
The Department of Energy’s Isotope Program (DOE IP) announced last week that it would end its “active standby” capability for strontium-82 production about two decades after beginning production of the isotope for cardiac diagnostic imaging. The DOE IP is celebrating commercialization of the Sr-82 supply chain as “a success story for both industry and the DOE IP.” Now that the Sr-82 market is commercially viable, the DOE IP and its National Isotope Development Center can “reassign those dedicated radioisotope production capacities to other mission needs”—including Sr-89.
Steven E. Aumeier, Bulent Alpay, John C. Lee, A. Ziya Akcasu
Nuclear Science and Engineering | Volume 153 | Number 2 | June 2006 | Pages 101-123
Technical Paper | doi.org/10.13182/NSE06-A2599
Articles are hosted by Taylor and Francis Online.
We present probabilistic techniques that make synergistic use of available process information for diagnosis and detection of component fault manifestation in a multicomponent system. We begin by describing the motivation for using probabilistic techniques for systems diagnostics and then define probabilistic expressions that embody the diagnostics knowledge of interest. We show that a combination of a Bayesian expression with the solution to the Chapman-Kolmogoroff equation contains the diagnostic information of interest while explicitly making use of available process information including plant data or measurements, mathematical system models, and individual component reliability data. Given these probabilistic expressions, we introduce a practical means of obtaining the necessary constituent probability density functions corresponding to feasible component transitions via an adaptive Kalman filtering formulation. To demonstrate the consolidated probabilistic technique, we consider a low-order model of a balance of plant of a boiling water reactor, represented by 11 system variables, 9 component characteristics, and 5 observations. We simulate 5 to 10% degradations in two components subject to 1% signal noise in two different transient events. Our test calculations indicate that the proposed algorithm is able to provide correct fault detection and diagnosis of the faulted components and fault magnitudes, together with a rank-ordered likelihood of the binary faults.