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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.
E. E. Gruber
Nuclear Technology | Volume 35 | Number 3 | October 1977 | Pages 617-634
Technical Paper | Fuel | doi.org/10.13182/NT77-A31871
Articles are hosted by Taylor and Francis Online.
The need for a capability to predict transient fission gas behavior arises because of the complexity of fission gas response to transient conditions, the importance of fission gas to fuel mechanical response, and the prevalent limitations on experimental information relevant to the problem. A detailed mechanistic analysis of intragranular swelling and release of gas from grains to grain boundaries, both as they result from transient heating, has been developed and incorporated in a fission gas release and swelling code (FRAS). A generalized parametric model to approximate the results that would be obtained from the more detailed calculations has also been developed. The need for this model arises from the necessity to consider the fission gas effects in more general multinode accident-analysis and pin-mechanics codes. For such calculations, the FRAS code is prohibitive in its demands on computer storage and execution time, while the parametric FRAS (PFRAS) code reduces these demands by an order of magnitude. Transient calculations have been carried out with both codes, both to illustrate the sensitivity of the results to the parameters and to indicate the level of confidence that can reasonably be ascribed to PFRAS results. The parameters considered include initial gas concentration, grain size, heating rate, thermal gradient, and pressure. The PFRAS model gives a satisfactory approximation to FRAS results for the broad range of parameters surveyed.