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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.
Priscila Palma Sanchez, Adimir dos Santos
Nuclear Science and Engineering | Volume 195 | Number 5 | May 2021 | Pages 555-562
Technical Note | doi.org/10.1080/00295639.2020.1854541
Articles are hosted by Taylor and Francis Online.
In order to ensure safety in a nuclear power plant, operation and protection systems must take into account safety parameters, whether to guide operators or to trip the reactor in emergency cases. Especially in a boron-free small modular reactor (SMR) where reactivity and power are controlled exclusively by rod banks, the power distribution is mostly influenced by its movements affecting the power peaking factor (PPF), which is an important parameter to be considered. The PPF relates the maximum local linear power density to the average power density in a fuel rod indicating a high neutron flux that can cause fuel rod damage. In this technical note, 2117 samples from simulations of an idealized boron-free SMR controlled exclusively by rod banks were used to generate a Support Vector Machine (SVM) model capable of estimating the PPF as a function of control rod bank positions. Such model could be used to predict the maximum PPF in the reactor core by carrying out simple calculation. Residing in a SVM parameter grid search and a 10-cross-validation process in the training set to reach an optimized and robust model, the results have shown a root-mean-squared error of about 0.1% consistent for both training and testing sets.