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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.
Takashi Kiguchi, Toshio Kawai
Nuclear Technology | Volume 27 | Number 3 | November 1975 | Pages 315-326
Technical Paper | Reactor | doi.org/10.13182/NT75-A24307
Articles are hosted by Taylor and Francis Online.
A method has been developed to optimally and automatically determine the adjustable parameters of the boiling water reactor three-dimensional core simulator FLARE. The steepest gradient method is adopted for the optimization. The parameters are adjusted to best fit the operating data on power distribution measured by the traversing in-core probes (TIP). The average error in the calculated TIP readings normalized by the core average is 0.053 at the rated power. The k∞ correction term has also been derived theoretically to reduce the relatively large error in the calculated TIP readings near the tips of control rods, which is induced by the coarseness of mesh points. By introducing this correction, the average error decreases to 0.047. The void-quality relation is recognized as a function of coolant flow rate. The relation is estimated to fit the measured distributions of TIP reading at the partial power states. The result shows that it is necessary to consider the flow rate dependence for simulating partial power cores.