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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.
Yunhuang Zhang, Jean C. Ragusa, Jim E. Morel
Nuclear Science and Engineering | Volume 194 | Number 10 | October 2020 | Pages 903-926
Technical Paper | doi.org/10.1080/00295639.2020.1771141
Articles are hosted by Taylor and Francis Online.
The Simplified () approximation is often used to model radiation transport phenomena, but it converges to the true solution of the transport equation only in one-dimensional slab geometry. In all other geometries, it incurs a model error that needs to be quantified. In this paper, we estimate the radiation transport model error due to the approximation and employ transport solutions (with high order) as reference transport solutions. Because the solution does not contain the full angular information of the transport solution, an angular intensity must be reconstructed from the solution in order to compute the model error. We propose two such reconstruction schemes. Model error estimates are given for various quantities of interests, i.e., scalar radiation intensity, radiation flux, and boundary leakage. An adjoint-based approach is proposed to evaluate the model error and is compared against forward and residual techniques. Two-dimensional numerical experiments are presented.