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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.
Pew Basu, R. Sarangapani, D. Datta, B. Venkatraman
Nuclear Technology | Volume 208 | Number 7 | July 2022 | Pages 1192-1204
Technical Paper | doi.org/10.1080/00295450.2021.2018275
Articles are hosted by Taylor and Francis Online.
The present study is envisaged with an aim to highlight a novel approach of applying the statistical factorial design analysis (FDA) technique in radiation shielding design. In FDA, the estimated total dose rate (TDR) and concentration of elements in shielding material are termed as “Response” and “Factors.” The impact on the response due to the change in the level of factors is defined as “Effects.” Monte Carlo simulation (MCS) is performed using the MCNP4A code to compute the surface TDRs due to the coupled neutron-gamma field arising from the 740-GBq 241Am-Be source housed inside a shielding container made of composite polymer (CP). The composition of CP is hydrogen, carbon, and oxygen with lead and natural boron as fillers. In the present work, the weight percent of hydrogen, carbon, and lead is optimized in the CP by minimizing the surface TDR at the exit of the shield, and a proposed CP (PCP) is obtained. For the first time, a detailed regression analysis is performed to develop a model linking TDR and the three factors, namely, hydrogen weight percent, carbon weight percent, and lead weight percent. Three levels of each factor are considered, and the impact due to the linear, quadratic, and interaction effects of the factors that influences the TDR is investigated using Student’s t-test analysis. The results from the statistical analysis indicate that the weight percent of hydrogen and lead have a greater influence on TDR. The interaction effects arising out of the combination of hydrogen, carbon, and lead are observed to be negligible. Hence, the regression model is modified by dropping the statistically insignificant terms from the equation, and the new model has shown excellent correlation within ±1% of the estimated TDRs using MCS. The R2 and R2Adj values are found to be 0.99970 and 0.99966, which explains the computation power of the model. The model can be applied to compute the TDRs for any combinations of factors within the range of variability as studied in the present work. The shielding container made of PCP obtained from the present study provides a minimum of 20% reduction in volume and mass compared to the conventional high-density polyethylene and other polymer-based materials.
