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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.
John O. Mingle
Nuclear Technology | Volume 27 | Number 2 | October 1975 | Pages 248-257
Technical Paper | Fuel Cycle | doi.org/10.13182/NT75-A24292
Articles are hosted by Taylor and Francis Online.
A two-step procedure is developed for the optimization of in-core nuclear fuel management using perturbation theory to predict the effects of various core configurations. The first procedure is a cycle cost minimization using linear programming with a zoned core and discrete burnup groups. The second program utilizes an individual fuel assembly shuffling sequence to minimize the maldistribution of power generation. This latter quantity is represented by a figure of merit or by an assembly power peaking factor. A pressurized water reactor example calculation is utilized.