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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.
C. L. Schuske, D. Dickinson, S. J. Altschuler
Nuclear Technology | Volume 23 | Number 2 | August 1974 | Pages 157-176
Chemical Processing | Nuclear Safeguards (Presented at November 1973 Meeting) | doi.org/10.13182/NT74-A31449
Articles are hosted by Taylor and Francis Online.
Models for the safe and economical storage of large quantities of plutonium or highly enriched uranium have been developed. The models cover a wide range of compositions from aqueous solutions, wet and dry oxides, to full density metals. The models all use the parameters of unit surface-to-volume ratio and array surface density to define the safe storage arrays. The models take account of unit shape, size, and composition, as well as vault size. Arrays containing a mixture of different units are easy to calculate by the model. Important accident situations, such as double batching and fissile solution spills, are considered.