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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.
Surendra Mishra , R. S. Modak, S. Ganesan
Nuclear Science and Engineering | Volume 170 | Number 3 | March 2012 | Pages 280-289
Technical Paper | doi.org/10.13182/NSE10-84
Articles are hosted by Taylor and Francis Online.
Large-sized pressurized heavy water reactors (PHWRs) are neutronically loosely coupled and hence are prone to significant changes in flux shape during operation. As a result, they need a sophisticated regulation procedure based on an online flux mapping system (OFMS). During the reactor operation, neutron flux is continuously measured at certain predetermined in-core locations. The purpose of OFMS is to compute a detailed flux map at all points in the reactor, after every 2 min, by making use of the measured fluxes. The knowledge of detailed flux distribution is then used for an appropriate regulating action. The choice of computational method used by OFMS is of crucial importance because the method is expected to be both efficient and accurate and should work for a range of reactor configurations occurring during the operation. In this paper, three different methods, namely, flux synthesis, internal boundary condition, and combined least squares (CLSQ), are analyzed for their prospective use in the forthcoming 700-MW(electric) Indian PHWR. The CLSQ method is found to be most accurate, although it needs significant computation. A hybrid method that combines certain features of other methods is also studied and seems to give good accuracy with moderate computational effort.