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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Constantine P. Tzanos
Nuclear Technology | Volume 183 | Number 1 | July 2013 | Pages 88-100
Technical Paper | Thermal Hydraulics | doi.org/10.13182/NT13-A16994
Articles are hosted by Taylor and Francis Online.
Heat transfer coefficients have been computed for flow in a pipe and flow between two plates with correlations and turbulence models based on Reynolds Averaging of the Navier-Stokes (RANS) equations. Predictions of the correlations and those of RANS turbulence models have been compared with experimental data of flow in a pipe. The correlations considered are those of Dittus-Boelter, Seider-Tate, Petukhov, and Sleicher-Rouse, while the turbulence models include the standard high Reynolds number, the Reynolds stress model, the low Reynolds number, and the v2f model. There are significant differences in the predictions of the correlations as well as in those of the turbulence models. Although computational fluid dynamics simulations have wider applicability and provide more information than simulations using correlations, the heat transfer coefficient predicted by the turbulence models is not always more accurate than that predicted by correlations. The discrepancy in the heat transfer coefficient predicted by the turbulence models is due mainly to discrepancies in the prediction of turbulence near the wall and to the uncertainty in the value of the turbulent Prandtl number.