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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.
K. H. Sun, R. B. Duffey
Nuclear Technology | Volume 43 | Number 1 | April 1979 | Pages 21-27
Technical Paper | Reactor | doi.org/10.13182/NT79-A16171
Articles are hosted by Taylor and Francis Online.
A simple generalized model has been developed to predict the rate of mass effluence during bottom reflooding, which is an emergency core cooling mechanism for the light water reactors. The effluence of mass during reflooding is important not only for determining the core heat transfer rate, but also the overall system pressure drop characteristics. The model accounts for the propagation of the quench front and vaporization below the quench front. It treats the quench front explicitly as a reference for mass and energy balance considerations. Comparisons were made between the model predictions and full-length bundle data with two power profiles and basic single-tube data. The results demonstrate that the rate of mass effluence is strongly coupled to the rate of quench front propagation in the core.