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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.
K. Lassmann
Nuclear Technology | Volume 40 | Number 3 | October 1978 | Pages 321-328
Technical Paper | Material | doi.org/10.13182/NT78-A26730
Articles are hosted by Taylor and Francis Online.
The state-of-the-art in fuel rod structural analysis is discussed, and possible future developments in this field are outlined. The conclusion is drawn that the most important goal for future research is a deeper understanding of material behavior. It is suggested that a strategy of successive use of diverse models appropriate to the varying degrees of theoretical sophistication be followed in fuel rod structural analysis: Preliminary work should be an analysis of the integral fuel rod with one-dimensional models, followed by local two-dimensional analyses. Finally, the deterministic analyses should be augmented by probabilistic work. All these modeling approaches are inevitably complementary in exhaustive fuel rod analysis, but they are, despite the tremendous theoretical efforts, no substitute for fuel rod performance tests. Nevertheless, analytical modeling will remain an indispensable tool for a long time to come, since with this theoretical background, the interpretation of experimental results is facilitated, and a better insight into fuel rod behavior is provided.