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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.
C. Y. Yang, R. W. Albrecht
Nuclear Technology | Volume 22 | Number 3 | June 1974 | Pages 323-330
Technical Paper | Reactor | doi.org/10.13182/NT74-A31417
Articles are hosted by Taylor and Francis Online.
A highly efficient and bias-free inverse kinetics technique has been developed to determine the subcriticality of a nuclear reactor. The new algorithm has been tested through simulation studies against two other algorithms currently being used. It was also extensively tested by analyzing actual rod-drop experimental data taken at the Argonne National Laboratory. Both simulation studies and the real data analyses showed that the new algorithm proposed here is a superior method in estimating the subcriticality of a system from the time-dependent neutron flux transient data.