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Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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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.
John D. Metzger, Mohamed S. El-Genk,Alexander G. Parlos
Nuclear Science and Engineering | Volume 109 | Number 2 | October 1991 | Pages 171-187
Technical Paper | doi.org/10.13182/NSE91-A28516
Articles are hosted by Taylor and Francis Online.
To ensure that a space nuclear power system will operate safely and respond in a predictable and desired manner, the system’s controller design must account for changes in the system parameters over its lifetime. A model reference adaptive controller is applied to enable the actual space nuclear power system to follow a predictable and desired response of a reference model system, despite changes in the actual system’s operating parameters. Model reference adaptive control is well developed for linear systems and has been applied to simple, single-input, single-output (and the output’s derivative) systems. Model reference adaptive control is applied to a single-input, multiple-output nonlinear system but also shows the development for a multiple-input, multiple-output linear system. An algorithm is developed for linear systems to determine the constant gains in the model reference adaptive control algorithm and a method is developed that allows selective weighting of a desired state variable. Examples are presented to show that a model reference adaptive controller can ensure the load-following response of a nonlinear space nuclear power system and that the reference model can be complex enough to embody the physics of the plant. The results of the example cases show that a model reference adaptive controller can cause a selected nonlinear plant state variable to track the transient trajectory of the corresponding state variable of the reference model with local stability.