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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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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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PR: American Nuclear Society welcomes Senate confirmation of Ted Garrish as the DOE’s nuclear energy secretary
Washington, D.C. — The American Nuclear Society (ANS) applauds the U.S. Senate's confirmation of Theodore “Ted” Garrish as Assistant Secretary for Nuclear Energy at the U.S. Department of Energy (DOE).
“On behalf of over 11,000 professionals in the fields of nuclear science and technology, the American Nuclear Society congratulates Mr. Garrish on being confirmed by the Senate to once again lead the DOE Office of Nuclear Energy,” said ANS President H.M. "Hash" Hashemian.
Ezgi Gursel, Bhavya Reddy, Katy Daniels, Jamie Baalis Coble, Mahboubeh Madadi, Vivek Agarwal, Ronald Boring, Vaibhav Yadav, Anahita Khojandi
Nuclear Technology | Volume 210 | Number 12 | December 2024 | Pages 2299-2311
Research Article | doi.org/10.1080/00295450.2024.2338507
Articles are hosted by Taylor and Francis Online.
In nuclear power plants (NPPs), anomalies arising from sensors or human errors (HEs) can undermine the performance and reliability of plant operations. Anomaly detection models can be employed to detect sensor errors and HEs. Additionally, physics-informed machine learning models can utilize the known physics of the system, as described by mathematical equations, to ensure that sensor values are consistent with physical laws. Hence, we propose SPIDARman: System-level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors, a holistic physics-informed anomaly detection approach based on generative adversarial networks (GANs) to detect anomalies in both automatically collected sensor data and manually collected surveillance data. We test our approach on data collected from a flow loop testbed, showcasing its potential to detect anomalies. Results demonstrate that the proposed model performs better than the baseline GAN-based models in detecting sensor and surveillance anomalies, suggesting the potential of physics-informed anomaly detection GAN models in NPPs.