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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.
Balabhadra Misra, Victor A. Maroni
Nuclear Technology | Volume 35 | Number 1 | August 1977 | Pages 40-50
Technical Paper | Reactor | doi.org/10.13182/NT77-A31849
Articles are hosted by Taylor and Francis Online.
Isotopic enrichment of the spent fuels from deuterium-tritium (D-T)-burning tokamak-type power reactors is an essential processing step in the reactor fuel cycle. Analysis of cryogenic distillation as a method for accomplishing this enrichment was carried out using computer methods to simulate the required multicomponent separation of the six isotopomeric forms of molecular hydrogen. The application of matrix inversion techniques (as opposed to iterative methods) resulted in rapid convergence even for simultaneous analyses of multicolumn configurations having a wide range of input and output conditions. Two distinctly different fuel cycle scenarios were studied: