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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.
Peter D. Esser, Robert J. Witt
Nuclear Science and Engineering | Volume 114 | Number 1 | May 1993 | Pages 20-35
Technical Paper | doi.org/10.13182/NSE93-A24011
Articles are hosted by Taylor and Francis Online.
An upwind nodal solution method is developed for the steady, two-dimensional flow of an incompressible fluid. The formulation is based on the nodal integral method, which uses transverse integrations, analytical solutions of the one-dimensional averaged equations, and node-averaged uniqueness constraints to derive the discretized nodal equations. The derivation introduces an exponential upwind bias by retaining the streamwise convection term in the homogeneous part of the transverse-integrated convection-diffusion equation. The method is adapted to the stream function-vorticity form of the Navier-Stokes equations, which are solved over a nonstaggered nodal mesh. A special nodal scheme is used for the Poisson stream function equation to properly account for the exponentially varying vorticity source. Rigorous expressions for the velocity components and the no-slip vorticity boundary condition are derived from the stream function formulation.The method is validated with several benchmark problems. An idealized purely convective flow of a scalar step function indicates that the nodal approximation errors are primarily dispersive, not dissipative, in nature. Results for idealized and actual recirculating driven-cavity flows reveal a significant reduction in false diffusion compared with conventional finite difference techniques.