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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.
S. L. Sharma, J. R. Buchanan, M. A. Lopez de Bertodano
Nuclear Science and Engineering | Volume 194 | Number 8 | August-September 2020 | Pages 665-675
Technical Paper | doi.org/10.1080/00295639.2020.1744406
Articles are hosted by Taylor and Francis Online.
Thermally induced density wave instability (DWI) (Type-II) is an important phenomenon for two-phase flow industrial systems. Developing numerical tools and methods for the prediction of the DWI boundary is of importance in the design and safety of nuclear reactors. With the advent of computational fluid dynamics (CFD) in nuclear safety analysis, it is important to first verify the CFD results against existing theory and validate them with experimental data. In this work, a CFD two-fluid model (TFM) for DWI was implemented and verified against the theory of Ishii (1971). Closure relations were selected to approach the homogeneous equilibrium flow model. A steady-state verification of the model was carried out first. Then, dynamic verification was performed. Predictions of the stability boundary and the frequency of oscillations are in a good agreement with the theory. This study further verifies the dynamic capability of TFM CFD.