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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.
G. R. Odette, D. R. Doiron
Nuclear Technology | Volume 29 | Number 3 | June 1976 | Pages 346-368
Technical Paper | Fusion Reactor Material / Material | doi.org/10.13182/NT76-A31600
Articles are hosted by Taylor and Francis Online.
Neutron cross sections for displacements and post-short-term cascade annealing defects are derived from nuclear kinematics calculations of primary atomic recoil energy distributions and the number of secondary defects produced per primary as a function of recoil energy. For the first time, recoil kinematics of charged- and multiple-particle emission reactions are treated rigorously using a compound-nucleus evaporation spectrum nuclear model. Secondary-defect production functions, derived from computer simulation experiments, are taken from the literature. Spectral-averaged defect production cross sections for a fusion reactor first-wall-type environment are on the order of 1.5 to 2.5 times those for a fast fission reactor core-type spectrum. The indicated range of uncertainty is primarily due to secondary-defect production model sensitivity. Nuclear model and data errors are expected to become more significant at high neutron energies, greater than ∼20 MeV. Fusion reactor environments are found to produce some very energetic recoils and high-energy release events due to charged-particle reactions such as (n, α).