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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.
Bradley L. Wescott, Rizwan-uddin
Nuclear Science and Engineering | Volume 139 | Number 3 | November 2001 | Pages 293-305
Technical Paper | doi.org/10.13182/NSE01-A2239
Articles are hosted by Taylor and Francis Online.
An alternate formulation of the recently proposed modified nodal integral method (MNIM) has been developed to further reduce computation time when solving nonlinear partial differential equations with a nonlinear convection term such as Burgers' equation and the Navier-Stokes equation. In this formulation, by adding and subtracting a linearized convection term, in which the node-averaged velocity at the previous time step multiplies the spatial derivative, the node-interior approximate analytical solution is developed in terms of this previous time-step node-averaged velocity. This leads to a set of discrete equations with coefficients that need to be evaluated only once each time step for each node, resulting in a significant reduction in computing time when compared with the original MNIM formulation. A numerical scheme using the node-averaged velocities at the previous time step - to be referred to as M2NIM - for the two-dimensional, time-dependent Burgers' equation has been developed. The method is shown to be second order and to posses inherent upwinding. When compared with MNIM, numerical results show a significant reduction in the computation time without sacrificing accuracy.