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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.
Richard N. Oehlberg, Harold H. Scott
Nuclear Technology | Volume 40 | Number 3 | October 1978 | Pages 248-260
Technical Paper | Reactor | doi.org/10.13182/NT78-A26723
Articles are hosted by Taylor and Francis Online.
A partitioning of a hypothetical pressurized water reactor loss-of-coolant accident time history into pre-reflood and reflood periods allowed a study of reflood parameter effects without a well-defined knowledge of the complex blowdown history. Values for the initial reflood fuel rod temperature profiles, reflood cladding-to-coolant heat transfer coefficient (HTC), decay heat, and initial reflood hot node hoop strain were chosen to parametrically describe the initial reflood condition of the fuel rod. The sensitivity of the reflood peak cladding temperature to decay heat was constant; the sensitivity to the gap conductance was only a function of the initial reflood hoop strain. The sensitivity to decay heat and the reflood HTC ramp was greater for the lower initial reflood centerline temperature case.