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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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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.
Kuan-Chywan Tu, Chien-Hsiung Lee, Shih-Jen Wang, Bau-Shei Pei
Nuclear Technology | Volume 124 | Number 3 | December 1998 | Pages 243-254
Technical Paper | Thermal Hydraulics | doi.org/10.13182/NT98-A2923
Articles are hosted by Taylor and Francis Online.
A new mechanistic critical heat flux (CHF) model has been developed for flow boiling CHF data of low-pressure (i.e., 0.2 to 4.0 MPa), low-mass-flux (i.e., 189 to 789 kg/m2s), and high-quality conditions. In general, CHF at these conditions associates with the flow regime of annular flow. This model assumes that the Helmholtz instability at the liquid-vapor interface of annular flow triggers the onset of CHF. CHF is the energy required to dryout the liquid film isolated by flow instability. With five empirical constants to properly correlate the liquid-vapor configurations of annular flow in the steam-water systems, the model successfully achieves a mean deviation error of 10.2% over a CHF data set consisting of 733 CHF data. The prediction of this model is more accurate than those of Biasi and Bowring correlations at the aforementioned low-pressure and low-mass-flux conditions.