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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.
Jaakko Leppänen
Nuclear Technology | Volume 205 | Number 11 | November 2019 | Pages 1416-1432
Technical Paper | doi.org/10.1080/00295450.2019.1603710
Articles are hosted by Taylor and Francis Online.
A deterministic importance solver has been implemented as an internal subroutine in the Serpent 2 Monte Carlo code for the purpose of producing weight-window meshes for variance reduction. The routine solves the adjoint transport problem using the response matrix method with coupling coefficients obtained from a conventional forward Monte Carlo simulation. The methodology can be applied to photon and neutron external source problems, and the solver supports multiple energy groups and several mesh types. Importances can be generated with respect to multiple responses, and an iterative global variance reduction sequence enables distributing the transported particle population evenly throughout the geometry. This paper describes the methodology applied in the response matrix solver and presents a verification for the generated importance functions through simple demonstrations. A practical example involving a photon shielding problem is included for performance evaluation.