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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.
Justin M. Pounders, Farzad Rahnema
Nuclear Science and Engineering | Volume 176 | Number 3 | March 2014 | Pages 273-291
Technical Paper | doi.org/10.13182/NSE12-108
Articles are hosted by Taylor and Francis Online.
A new solution technique is derived for the time-dependent transport equation. This approach extends the steady-state coarse-mesh transport method that is based on global-local decompositions of large (i.e., full-core) neutron transport problems. The new method is based on polynomial expansions of the space, angle, and time variables in a response-based formulation of the transport equation. The local problem (coarse-mesh) solutions, which are entirely decoupled from each other, are characterized by space-, angle-, and time-dependent response functions. These response functions are, in turn, used to couple an arbitrary sequence of local problems to form the solution of a much larger global problem. In the current work, the local problem (response function) computations are performed using the Monte Carlo method, while the global (coupling) problem is solved deterministically. The spatial coupling is performed by orthogonal polynomial expansions of the partial currents on the local problem surfaces, and similarly, the time-dependent response of the system (i.e., the time-varying flux) is computed by convolving the time-dependent surface partial currents and time-dependent volumetric sources against precomputed time-dependent response kernels.