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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.
J. Michael Doster, Jeremy M. Kauffman
Nuclear Science and Engineering | Volume 132 | Number 1 | May 1999 | Pages 90-104
Technical Paper | doi.org/10.13182/NSE99-A2051
Articles are hosted by Taylor and Francis Online.
Drift-flux models can be used to describe two-phase-flow systems when explicit representation of the relative phase motion is not required. In these models, relative phase velocity is described by flow-regime-dependent, semiempirical models. Numerical stability of the mixture drift-flux equations is examined for different semi-implicit time discretization schemes. Representative flow-regime-dependent drift-flux correlations are considered, and analytic stability limits are derived based on these correlations. The analytic stability limits are verified by numerical experiments run in the vicinity of the predicted stable boundaries. It is shown that the stability limits are strong functions of the time-level specification and functional form chosen for the relative phase velocity. It is also shown that the mixture Courant limit normally associated with these methods is insufficient for ensuring a stable numerical scheme.