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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.
Chris Keckler, Massimiliano Fratoni, Ehud Greenspan
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 1079-1088
Technical Paper | doi.org/10.1080/00295639.2020.1715688
Articles are hosted by Taylor and Francis Online.
This study quantifies the uncertainty in the calculated displacements per atom (DPA) value due to uncertainties in the neutron spectrum resulting from cross-section data uncertainty. Using generalized perturbation theory, covariance matrices, and fine-group DPA cross sections, a method for propagating nuclear data uncertainties through to the calculation of DPA is outlined. This method is then implemented for the case of a typical sodium-cooled breed-and-burn core. The majority of uncertainties in accumulated DPA were found to come from the inelastic scattering cross section for 238U. Overall the uncertainty in accumulated DPA is found to be roughly 2% of the total value.