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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.
M. L. Williams, R. T. Santoro, T. A. Gabriel
Nuclear Technology | Volume 29 | Number 3 | June 1976 | Pages 384-391
Technical Paper | Fusion Reactor Material / Material | doi.org/10.13182/NT76-A31603
Articles are hosted by Taylor and Francis Online.
The calculated nuclear performances of niobium, Type 304 stainless steel, and nimonic-105 as structural materials in a conceptual D-T fusion-reactor blanket model are compared. For each structural material, the tritium breeding ratio, the energy-deposition rate, the operating dose, the time dependence of the neutron-induced activity, the time dependence of the dose from the activation products, the time dependence of the nuclear afterheat, and the atomic displacement rate are calculated. Emphasis is placed on the nuclear response in the first structural wall to the selected structural material for an assumed neutron wall loading of 1 MW/m2. Taking into account all the nuclear responses, Type 304 stainless steel appears to be a reasonable choice as the structural material for fusion-reactor application.