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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.
N. J. McCormick, R. E. Schenter, R. P. Omberg
Nuclear Technology | Volume 29 | Number 2 | May 1976 | Pages 200-208
Fuel | doi.org/10.13182/NT76-A31579
Articles are hosted by Taylor and Francis Online.
Gas tagging consists of adding small amounts of gas with a unique isotopic composition for each assembly to nuclear reactor fuel and control assemblies. During subsequent irradiation, when any pin of an assembly fails, the tag gas released along with other gas from the pin plenum enables location of the defective assembly by a mass spectrometric analysis of a sample of the reactor cover gas. The general procedure presented for the design of a gas tag system has been used to produce three designs for the gas ratios for Cores I through IV of the Fast Flux Test Facility. The designs are compared with and without “age tagging,” the use of information from tag gas burnup to help discriminate between failures of different assemblies. A few comments included on the operation of a gas tag system help ensure that the system will operate within the assumptions made in the design of the gas tag ratios.