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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.
J. L. Straalsund, R. L. Fish, G. D. Johnson
Nuclear Technology | Volume 25 | Number 3 | March 1975 | Pages 531-540
Technical Paper | Material | doi.org/10.13182/NT75-A24390
Articles are hosted by Taylor and Francis Online.
Thermal transient-test failure data for proto-typic fast test reactor fuel cladding were correlated with failure data from conventional mechanical properties tests. The correlation was developed by the application of a life fraction rule to stress rupture, tensile and tube burst, as well as transient test data and by constructing a Larson-Miller plot which encompasses the stress and temperature range for all four types of tests. The high degree of correlation between the different types of test data indicates that this approach will be useful in the prediction of failures under hypothetical transient conditions. It is shown that the Dorn parameter can be used instead of the Larson-Miller parameter with equal success for the various types of mechanical properties tests.