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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.
Martin Lopez de Bertodano
Nuclear Science and Engineering | Volume 117 | Number 2 | June 1994 | Pages 126-133
Technical Paper | doi.org/10.13182/NSE94-A20079
Articles are hosted by Taylor and Francis Online.
The objective of this analysis is to obtain an algebraic correlation for flooding and unflooding in a pressurized water reactor (PWR) hot leg during reflux core cooling. This correlation may be used in loss-of-coolant accident analysis codes such as RELAP5. The one-dimensional two-fluid model equations are solved to obtain a void fraction profile along the pipe. A jump condition is included in the model to account for the possibility of a hydraulic jump. The flooding correlation by Mishima and Ishii is used to determine the flooding point. The model is validated against the scaled-down data of Krolewski and the full-scale data of Ohnuki, Adachi, and Murao. Reducing the coefficient of the flooding correlation to match the full-scale data is necessary to account for the effect of diameter size. Based on the validated model, a flooding correlation is obtained along the lines of the Wallis flooding criterion. It is further shown that under the conditions prevalent during PWR refluxing, the hysteresis between flooding and unflooding is not relevant, so the same correlation is valid for both.