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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.
Zhiee Jhia Ooi, Vineet Kumar, Caleb S. Brooks
Nuclear Science and Engineering | Volume 194 | Number 8 | August-September 2020 | Pages 598-619
Technical Paper | doi.org/10.1080/00295639.2020.1732123
Articles are hosted by Taylor and Francis Online.
The static correlations from RELAP5 and TRACE as well as the interfacial area transport equation (IATE) are benchmarked for flashing flow with selected cases from a recently published experimental data set. The RELAP5 correlation is able to predict the interfacial area concentration more accurately than the TRACE correlation. The one-group decoupled IATE, supplied with experimental void fraction, shows overprediction of interfacial area concentration, especially at low-pressure conditions. Additionally, the one-group IATE is solved simultaneously with the void transport equation where at low pressures, the accuracy of the predicted interfacial area concentration improves even with the void fraction being underpredicted. However, as the pressure increases, the improving accuracy of the predicted void fraction leads to an overprediction of the interfacial area concentration. The two-group IATE is also benchmarked, first using the interfacial mass generation model from RELAP5 and TRACE and then with a model derived through a mass-energy balance approach. The accuracy of the two-group IATE is observed to be sensitive to the choice of the heat transfer length scale and Nusselt number correlations.