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Education, Training & Workforce Development
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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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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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.
Andres Gomez, Molly Ross, Hitesh Bindra
Nuclear Technology | Volume 210 | Number 12 | December 2024 | Pages 2346-2361
Research Article | doi.org/10.1080/00295450.2024.2361182
Articles are hosted by Taylor and Francis Online.
Accurate modeling of nuclear reactor transients is important for improving operation and maintenance planning strategies in advanced reactors. An adequate understanding of the connectivity between various components and sensor signals can provide the means to project plant information even where sensors are not operational. This work presents a method for projecting temperature signals using network graph Laplacian and support vector regression (SVR).
A system model of a scaled liquid-metal-cooled reactor experiment facility is used to generate thermal-hydraulic data simulating cold-shock transients into the reactor plenum. Two different flow rates are investigated, 0.03 kg/s and 0.06 kg/s, describing the different degrees of nonlinear disturbances in two cases. The temperatures at 72 locations within the system are used to test the temperature prediction model. The inter-connectivity of these points is described using the network graph Laplacian, which describes a location in the loop as a node and then comprises a weighted matrix of connections between each of these nodes. Additional nodes, called ghost nodes, are used to model the heat transfer of the loop with the surroundings. The graph Laplacian and corresponding nodal sensing data are then used to train a kernel, which can inform the impact of various phenomena at one location in the plant on phenomena at other locations in the plant.
The sensing data and graph network of these nodes are used to construct a surrogate model of the liquid-metal loop. This model is then used to predict the behavior at certain nodes where the training data are not provided to the model. These model predictions are compared against the test data for the two different inlet flow rates. When optimized, the average error of the simulated temperature data remained below 5% when 3 of the 72 nodes are predicted using the graph-based SVR model. As the number of unknown sensors is increased, the root-mean-square error increases slightly but still remains below 2% when 24 of the 72 nodes are unknown or tested. The test or unknown sensor location plays a larger role than the number of unknowns, with sensor locations near ghost nodes and near the outlet pipes in the plenum having the largest error, with a maximum recorded error of 7%.
The objective of the graph-based SVR model is to not only capture the temperature or field variable accurately but also to capture the relative connectivity between the simulated sensors. The correlation coefficient matrix provides a scaled reference for the correlation between temperatures at two different node locations. The calculated correlation coefficient for the simulated temperature data and actual temperature data is within 5% for most of the system, with a maximum relative error of 15%.