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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.
Constantine P. Tzanos
Nuclear Technology | Volume 41 | Number 2 | December 1978 | Pages 195-206
Technical Paper | Extraction of Energy From Nuclear Fuels Without Reprocessing to Separate Plutonium / Reactor | doi.org/10.13182/NT78-A32105
Articles are hosted by Taylor and Francis Online.
An efficient optimization method has been developed that determines simultaneously beginning-of-cycle (BOC) enrichment distribution and the control rod programming in reactors that lose reactivity during burnup such that (a) reactor criticality and a desired power distribution are satisfied throughout the cycle and (b) all the control rods are withdrawn at end-of-cycle (EOC). The method uses (a) an iterative scheme of uncontrolled burnup calculations [and linear programming (LP) for more than two enrichment zones] to determine a good approximation of the EOC reactor composition that satisfies reactor criticality and the desired power distribution and (b) LP in a reversed burnup process starting from the EOC conditions to determine the control rod programming and the BOC enrichment distribution.