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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., J. Barish, C. R. Weisbin
Nuclear Technology | Volume 34 | Number 3 | August 1977 | Pages 376-386
Technical Paper | Reactor | doi.org/10.13182/NT77-A31802
Articles are hosted by Taylor and Francis Online.
Calculated results have been obtained of the uncertainties in the neutron scalar flux, the energy deposition per unit volume, and the displacements per atom in the toroidal field coil of a tokamak experimental power reactor due to neutron cross-section errors in iron and carbon, which are major constituents of the blanket-shield-coil configuration considered. The calculations were carried out using perturbation theory to obtain sensitivity profiles for the various cross sections of interest, and these profiles were then combined with cross-section error estimates, including correlations, to obtain the uncertainties. Each of the three responses—the neutron scalar flux, the energy deposition per unit volume, and the displacements per atom—is found to be very sensitive to the cross sections in the energy group that contains the source (∼2.2 pJ) since a deuterium-tritium source is assumed, and each of the responses is found to have a relative standard deviation of ∼100% due to neutron cross-section errors in iron.