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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. Bess, Nozomu Fujimoto
Nuclear Science and Engineering | Volume 178 | Number 3 | November 2014 | Pages 414-427
Technical Paper | doi.org/10.13182/NSE14-14
Articles are hosted by Taylor and Francis Online.
Benchmark models were developed to evaluate six cold-critical and two warm-critical, zero-power measurements of the high-temperature engineering test reactor (HTTR). Additional measurements of the subcritical configuration of the fully loaded core, core excess reactivity, shutdown margins, six isothermal temperature coefficients, and axial reaction-rate distributions were also evaluated as acceptable benchmark experiments. Insufficient information is publicly available to develop finely detailed models of the HTTR as much of the design information is still proprietary. The uncertainties in the benchmark models are judged to be of sufficient magnitude to encompass any biases and bias uncertainties incurred through the simplification process used to develop the benchmark models. However, use of the benchmark critical configurations of the HTTR for nuclear data adjustment is not recommended as the impact of these biases has not been addressed with rigorous detail. The impact of any simplification biases, if any, is not expected to significantly impact evaluation of the other reactor physics measurement calculations. Dominant uncertainties in the experimental keff for all core configurations come from uncertainties in the impurity content of the various graphite blocks that compose the HTTR. Monte Carlo calculations of keff are between ∼0.9% and ∼2.7% greater than the benchmark values. Reevaluation of the HTTR models as additional information becomes available could improve the quality of this benchmark and possibly reduce the computational biases. High-quality characterization of graphite impurities would significantly improve the quality of the HTTR benchmark assessment. Simulations of the other reactor physics measurements are in good agreement with the benchmark experiment values. The complete benchmark evaluation details are available in the 2014 edition of the International Handbook of Evaluated Reactor Physics Benchmark Experiments.