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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.
Douglas E. Peplow
Nuclear Technology | Volume 174 | Number 2 | May 2011 | Pages 289-313
Technical Paper | Special Issue on the SCALE Nuclear Analysis Code System / Radiation Protection | doi.org/10.13182/NT174-289
Articles are hosted by Taylor and Francis Online.
Monte Carlo shielding analysis capabilities in SCALE 6 are centered on the Consistent Adjoint Driven Importance Sampling (CADIS) methodology. CADIS is used to create an importance map for space/energy weight windows as well as a biased source distribution. New to SCALE 6 are the Monaco functional module, a multigroup fixed-source Monte Carlo transport code, and the Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) sequence. MAVRIC uses the Denovo code (also new to SCALE 6) to compute coarse-mesh discrete ordinates solutions that are used by CADIS to form an importance map and biased source distribution for the Monaco Monte Carlo code. MAVRIC allows the user to optimize the Monaco calculation for a specific tally using the CADIS method with little extra input compared with a standard Monte Carlo calculation. When computing several tallies at once or a mesh tally over a large volume of space, an extension of the CADIS method called FW-CADIS can be used to help the Monte Carlo simulation spread particles over phase space to obtain more uniform relative uncertainties.