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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.
José M. Aragonés, José M. Martínezval, María R. Corella
Nuclear Technology | Volume 34 | Number 3 | August 1977 | Pages 398-411
Technical Paper | Fuel Cycle | doi.org/10.13182/NT77-A31804
Articles are hosted by Taylor and Francis Online.
Fuel management requires that mass, energy, and reactivity balance be satisfied in each reload cycle. Procedures for selection of alternatives, core-state models, and fuel cost calculations have been developed for both equilibrium and transition cycles. Effective cycle lengths and fuel cycle variables—namely, reload batch size, schedule of in-core residence for the fuel, feed enrichments, energy sharing cycle by cycle, and discharge burnup and isotopics—are the variables being considered for fuel management planning with a given energy generation plan, fuel design, recycling strategy, and financial assumptions.