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Conference Spotlight
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.
Adam Davis, Donald J. Dudziak, Man-Sung Yim, David McNelis, H. Omar Wooten
Nuclear Technology | Volume 173 | Number 3 | March 2011 | Pages 270-288
Technical Paper | Radiation Protection | doi.org/10.13182/NT11-110
Articles are hosted by Taylor and Francis Online.
In radiation protection, photon buildup factors provide a convenient method for calculating dose and exposure response after various shielding configurations, as well as information about the behavior of radiation in these configurations. Though many situations call for multilayer shields, few databases and derived analytical formulas for photon buildup in multilayer shields exist. This research develops buildup factors and analytical fits to these data for slab-geometric, dual-layer shields composed of various materials. The photon buildup factors were calculated for monoenergetic photon sources incident on two-layer shields of various combinations of lead, polyethylene, aluminum, and stainless steel for thicknesses varying between 2 and 20 mean free paths using the Parallel Time Independent Sn (PARTISN) discrete ordinates code. The Gauss-Lobatto S100 quadrature was used with a 244-energy-group structure and coupled photon and electron cross sections. Data from PARTISN calculations were then benchmarked for representative cases using MCNP5, and fits to a new analytical formula were developed using Mathematica 6.0.