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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.
Zbigniew Weiss
Nuclear Science and Engineering | Volume 63 | Number 4 | August 1977 | Pages 457-492
Technical Paper | doi.org/10.13182/NSE77-A27062
Articles are hosted by Taylor and Francis Online.
The response matrix equations (RME) are analyzed from two points of view: (a) their computational feasibility, and (b) their consistency with other methods used in reactor analysis. It is shown that RME can be derived directly from the weak form of the diffusion equation without the concept of partial currents, and hence, are also applicable to the description of phenomena, where partial currents have no physical meaning (for example, the conduction of heat). By splitting the high-order RME into a coupled system of single-order equations, the analysis of the convergence properties of the iterative solutions to RME could be greatly simplified. The derived explicit expressions for the convergence ratio were verified by numerical experimentation. As an illustration, the well-known International Atomic Energy Agency benchmark problem has been calculated by two two-dimensional response matrix programs at ASEA-ATOM, CIKADA, and LABAN. In the second part of the paper, the relation of RME to finite difference (FD) equations has been investigated. It was shown that for small mesh sizes, RME are computationally not feasible. For rectangular nodes, an algorithm called the “vectorial model” (VM) was developed, which reduces the amount of unknowns in RME by a factor of 2. This is a generalization to two- and three-dimensional nodes of the author's earlier results. An approximate reduction of VM to scalar equations (one unknown per node) has been discussed, and its relation to recent developments in nodal methods has been emphasized. Several ideas in this paper, such as the improved FD scheme, are far from being completed and therefore should be challenging for further investigation.