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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.
M. J. F. Notley
Nuclear Technology | Volume 44 | Number 3 | August 1979 | Pages 445-450
Technical Paper | Fuel | doi.org/10.13182/NT79-A32279
Articles are hosted by Taylor and Francis Online.
The ELESIM code models a single fuel element in a one-dimensional axisymmetric manner. The constituent subroutines are physically based (rather than empirical) models, and include such phenomena as fuel-to-sheath heat transfer; temperature and porosity dependence of fuel thermal conductivity; burnup-dependent neutron flux depression; burnup-and microstructure-dependent fission product gas release; fuel thermal expansion, swelling, and densification; and stress-, dose-, and temperature-dependent agreement with experimental data.