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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.
D. Guarino, V. Marinelli, L. Pastori
Nuclear Technology | Volume 23 | Number 1 | July 1974 | Pages 38-52
Technical Paper | Fuel | doi.org/10.13182/NT74-A31432
Articles are hosted by Taylor and Francis Online.
Most published steady-state burnout experimental data on BWR square geometry rod bundles at 70 kg/cm2 were analyzed and compared with the main calculation methods, in order to examine the state-of-the-art in burnout power predictions. The calculations were performed using two system parameter correlations—Barnett and Macbeth, a local condition correlation—Becker, and two hydrodynamic condition correlations—CISE-III and ACHAB. Furthermore, a selected number of representative cases were calculated by means of LEUCIPPO and COBRA-II subchannel codes, in which the Becker correlations for annuli and round tubes were applied to the peripheral and central subchannels, respectively. The comparisons showed that Becker and ACHAB methods predict the burnout powers with rms errors lower than 10%, while the subchannel analysis (applied neglecting the void drift) yields errors of 20 to 25%.