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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.
C.A. Beard, V. I. Belyakov-Bodin
Nuclear Science and Engineering | Volume 119 | Number 2 | February 1995 | Pages 87-96
Technical Paper | doi.org/10.13182/NSE95-A24073
Articles are hosted by Taylor and Francis Online.
A comparison was performed between the energy deposition predicted by the LAHET code system (LCS) and experimental values for 800-, 1000-, and 1200-MeV Protons on targets composed of beryllium, carbon, aluminum, iron, copper, lead, bismuth, and uranium. The lead, bismuth, and uranium targets showed agreement within ∼10% at locations throughout the targets, and the agreement of the total energy deposited over the axial length of the targets ranged from 1 to 18%. For the lighter materials, the agreement at locations throughout the target was within ∼25%. No definable trend could be determined for the lighter materials because some LCS predictions were greater and some were less than the experimental results, and some showed very good agreement. Also, the LCS underpredicted the proton ranges for 800-MeV protons on iron, 800- and 1000-MeV protons on copper, and 800- and 1000-MeV protons on uranium.