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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.
Krishan Chitkara, Joel Weisman
Nuclear Technology | Volume 24 | Number 1 | October 1974 | Pages 33-49
Technical Paper | Fuel Cycle | doi.org/10.13182/NT74-A31459
Articles are hosted by Taylor and Francis Online.
As an equilibrium core configuration is reached after only a few refueling cycles, equilibrium core energy costs dominate plant economics. Optimization of these costs can therefore lead to nearly optimum energy costs over plant life. A simple reactor state model has been developed that can predict equilibrium core conditions without reference to previous cycles. By coupling this model to an assignment and direct search algorithm, the refueling procedure and core power at equilibrium are determined so that energy costs are minimized. The equilibrium core parameters are then used to establish pre-equilibrium core conditions.