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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Heinz E. Haefner
Nuclear Technology | Volume 34 | Number 1 | June 1977 | Pages 69-74
Technical Paper | Fuel | doi.org/10.13182/NT77-A31830
Articles are hosted by Taylor and Francis Online.
Designing advanced fuel element concepts for fast breeder reactors and assessing by models the fuel rod behavior over the intended in-pile time require information about creeping and swelling in the nuclear fuels under in-pile conditions. For this reason, a number of in-pile facilities that allow such material data to be determined have been used at the Karlsruhe Nuclear Research Center. These data depend not only on the properties of the fuel, above all the type of fuel and its density, but also, and in a very decisive way, on such parameters as specimen temperature, specimen loading, and burnup. A new series of experiments serves the concrete purpose of a quantitative assessment of the dependence on these parameters of carbides as potential fast breeder fuels, carbides being more susceptible to swelling than oxides. The loading of the specimen is given by the cladding restraint with the swelling fuel, and does not cause any undue expansion of the cladding of realistic fuel rods under operating conditions. This permissible