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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.
Keith Woodard
Nuclear Technology | Volume 25 | Number 4 | April 1975 | Pages 635-639
Technical Paper | Reactor Siting | doi.org/10.13182/NT75-A16120
Articles are hosted by Taylor and Francis Online.
Parametric studies using 26 site-years of meteorological data from 10 different nuclear reactor sites were made to determine the sensitivity of diffusion estimates to selected aspects of meteorological measurement programs. These sensitivities were determined by comparing diffusion estimates obtained using the actual data records with estimates obtained by altering the same record to simulate incomplete data recovery and instrument error. Variations from one year to the next were also explored, as was the effect of varying assumed wind speeds for calm hours. Results indicate that sufficiently accurate estimates, well within the accuracy and conservatism of the diffusion models, can be made with a smaller data base and with less measurement accuracy than required by U.S. Atomic Energy Commission guidelines.