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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.
R. G. Alsmiller, Jr., R. B. Perez, J. Barish
Nuclear Technology | Volume 36 | Number 1 | November 1977 | Pages 139-147
Technical Note | Material | doi.org/10.13182/NT77-A31967
Articles are hosted by Taylor and Francis Online.
A model based on phase-space considerations is developed to describe the fragmentation of UO2 by capacitor discharge, i.e., to predict such quantities as the amount of gas and liquid produced, the number of liquid fragments, the number distribution of the molecules in the liquid fragments, the kinetic-energy distribution of the gas and liquid fragments, etc. This model cannot give a unique numerical prediction of all of these quantities based only on the initial-state specification, but it does enable all of these quantities to be expressed in terms of the average internal energy of a gas molecule in the final state, the average binding energy of a UO2 molecule in a liquid fragment in the final state, and the average number of molecules in a liquid fragment in the final state.