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2026 ANS Annual Conference
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.
Constantine P. Tzanos, Elias P. Gyftopoulos, Michael J. Driscoll
Nuclear Science and Engineering | Volume 52 | Number 1 | September 1973 | Pages 84-94
Technical Paper | doi.org/10.13182/NSE73-3
Articles are hosted by Taylor and Francis Online.
An iterative optimization method based on linearization and linear programming is developed. The method can be used for the determination of the material distributions in a fast reactor which maximize or minimize integral reactor parameters that are linear functions of the neutron flux and the material volume fractions. The method has been applied to the problems of optimization of the fuel distribution in a reactor of fixed power output, constrained power density, and constrained material volume fractions so as to obtain (a) a maximum initial breeding gain, (b) a minimum critical mass, and (c) a minimum sodium void reactivity. Under this realistic set of constraints, numerical results show that the same fuel distribution yields maximum breeding gain, minimum critical mass, minimum sodium void reactivity, and uniform power density.