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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.
Katarzyna Borowiec, Tomasz Kozlowski, Caleb S. Brooks
Nuclear Science and Engineering | Volume 194 | Number 8 | August-September 2020 | Pages 737-747
Technical Paper | doi.org/10.1080/00295639.2020.1713671
Articles are hosted by Taylor and Francis Online.
The work presents validation of the TRAC/RELAP Advanced Computational Engine (TRACE) code for natural circulation two-phase flow in a vertical annulus. Natural circulation experiments were recently conducted for a vertical internally heated annulus at the Multiphase Thermo-Fluid Dynamics Laboratory at the University of Illinois. The experimental matrix consists of 107 experiments with system pressure in the range of 145 to 950 kPa and heat flux up to 275 kW/m2. Void fraction, gas velocity, and interfacial area concentration were measured in five axial locations along the test section with six measurements of bulk liquid temperature and pressure. To validate the capability of the TRACE code under natural circulation flow conditions, a complete model of the experimental facility was created and validated using forced convection and single-phase natural circulation data.
Sensitivity and uncertainty quantification were performed. The sensitivity to important simulation parameters was studied using Sobol’s variance decomposition and the Morris screening method. The sensitivity of boundary conditions on void fraction measurement was investigated. The sensitivity study has shown significant differences in model sensitivity between different experimental conditions. With heat flux being the most influential parameter for high-pressure cases without flashing and pressure, temperature and heat flux have a combined strong effect in the case of low-pressure experiments when flashing occurs. Additionally, higher uncertainty in void fraction prediction was observed for experimental conditions at low pressure with flashing.