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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.
R. A. Lillie, R. G. Alsmiller, Jr., J. T. Mihalczo
Nuclear Technology | Volume 43 | Number 3 | May 1979 | Pages 373-381
Technical Paper | Accelerator | doi.org/10.13182/NT79-A19225
Articles are hosted by Taylor and Francis Online.
A number of Type 316 stainless-steel right circular cylindrical shells of varying lengths have been analyzed using two-dimensional discrete-ordinates transport methods together with first- and last-flight particle estimators to aid in the design of neutron collimators for the Tokamak Fusion Test Reactor (TFTR). In the TFTR, the 14-MeV neutron source has a very large spatial extent, and the collimators must be designed to allow spectral measurements that refer to only a small spatial region of this extended source. The analysis identifies the 14-MeV neutrons from scattering in the Type 316 stainless steel immediately adjacent to the collimator opening as the dominant contributor to detector background. Collimator lengths >0.60 m were found sufficient to attenuate uncollided background neutrons for reasonable source-detector distances. The lower energy (<13.8 MeV) neutron background and gamma background were not found to be significant.