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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.
A. L. Casadei, P. J. Turinsky
Nuclear Technology | Volume 44 | Number 2 | July 1979 | Pages 221-230
Technical Paper | Reactor | doi.org/10.13182/NT79-A32257
Articles are hosted by Taylor and Francis Online.
Accurate and economical methods have been developed to model water density feedback in diffusion calculations for application to pressurized water reactors. Spectral variations of the two-group macroscopic cross sections are accurately modeled by second-order polynomials in water density. Open channel and subcooled boiling effects on the core’s water density distribution are evaluated by coupling three-dimensional thermal-hydraulic and neutron diffusion computer codes. The costly requirement of numerical mesh compatibility between these codes is removed without loss of accuracy. The margin to fuel degradation limits during accident conditions is found to increase when the improved feedback model is employed, serving as a stimulus for further usage.