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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.
B. R. Upadhyaya, M. Kitamura
Nuclear Science and Engineering | Volume 77 | Number 4 | April 1981 | Pages 480-492
Technical Paper | doi.org/10.13182/NSE81-A18961
Articles are hosted by Taylor and Francis Online.
A method of monitoring stability of boiling water reactors (BWRs) has been developed. The stability parameters were derived from empirical discrete-time modeling of process noise signals and neutron noise signals. Data were taken from an operating BWR-4, and used to perform univariate analysis of average power range monitor (APRM), and local power range monitor signals, and multivariate analysis of APRM and the process signals, reactor pressure, and core flow rate. The parameters such as decay ratio, damping ratio, and characteristic frequency of oscillation, which represent the system stability, were estimated from the impulse response of the system. The impulse response was determined by using the time series models and contains information about the closed loop dynamics of a BWR. The results indicate the feasibility of using APRM noise analysis for monitoring overall core stability and temporal variations in the stability margin of the reactor. Any significant variation in the stability parameters can be studied using multivariate noise signal algorithms, and cause and effect relationships can be obtained. Because the derived parameters depend on the random noise properties of the signals, this nonperturbing method is most useful for monitoring changes in stability. If an absolute measurement is necessary, a perturbation test must be performed.