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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.
Dean V. Power
Nuclear Technology | Volume 27 | Number 4 | December 1975 | Pages 680-691
Technical Paper | Nuclear Explosive | doi.org/10.13182/NT75-A24341
Articles are hosted by Taylor and Francis Online.
The coherency transfer function (CTF) is a method for summing seismograms from multiple nearly coherent sources by using a frequency domain transformation. Ground motion predictions for the nuclear explosive Rio Blanco experiment are calculated for peak vector amplitudes of acceleration, velocity, and displacement and are compared to the Rio Blanco data and the results of other prediction techniques. Predictions of amplitudes are higher than experimental results by a few percent for acceleration and displacement and by 20% for velocity. Data regression slopes are ∼12% greater than predicted values for acceleration but <5% greater for displacement and velocity. CTF predictions are found to agree with experimental results as good as or better than values predicted by other methods.