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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.
A. R. Buhl, J. C. Robinson, E. T. Tomlinson
Nuclear Technology | Volume 21 | Number 1 | January 1974 | Pages 67-74
Technical Paper | Technique | doi.org/10.13182/NT74-A31381
Articles are hosted by Taylor and Francis Online.
Numerical experiments were performed to evaluate the applicability of nonperturbing techniques for inferring the reactivity of fast reactors. Almost all such techniques contain the tacit assumption that the flux throughout the system is described by the fundamental mode and that higher modal contamination is negligible. Results were obtained for four reactive states of a typical 1000-liter-core Liquid Metal Fast Breeder Reactor (critical, -1$, -7$, and -30$) which illustrate the range of applicability of the point-model assumption with first-order corrections.