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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.
J. W. Allen, J. C. Robinson, N. J. Ackermann, Jr.
Nuclear Technology | Volume 22 | Number 3 | June 1974 | Pages 315-322
Technical Paper | Reactor | doi.org/10.13182/NT74-A31416
Articles are hosted by Taylor and Francis Online.
A study was made to determine the uncertainty in subcritical reactivity as inferred from inverse kinetics rod drop experiments (using the three-point method) due to the statistical uncertainty inherent in the observed count rate of the neutron sensor. The two methods employed were a classical propagation of error analysis, and an analysis of simulated repeated rod drops, with an assumption that the uncertainty in reactivity was due to the detection process itself for both techniques, To test the analysis methods, the reactivity uncertainties for various experimental rod drop data sets were computed by both methods. There was excellent agreement of the results. The propagation of error analysis may be used on three-point subcriticality measurements to provide an experimenter with an index to the statistical reliability of the inferred reactivity estimate.