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Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Nuclear Science and Engineering
Fusion Science and Technology
Finland in Front: The World’s Likely First Spent Fuel Repository Moves Toward Licensing
The year 2024 is shaping up to be a historic one for Posiva, the waste management organization owned by Finland’s two nuclear power plant utilities, Fortum and Teollisuuden Voima. The company is looking to receive regulatory approval of its operating license for the Onkalo deep geological repository for high-level radioactive waste by the end of the year.
Arvind Sundaram, Yeni Li, Hany Abdel-Khalik
Nuclear Technology | Volume 208 | Number 9 | September 2022 | Pages 1365-1381
Technical Paper | doi.org/10.1080/00295450.2022.2027147
Articles are hosted by Taylor and Francis Online.
The widespread digitization of critical industrial systems such as nuclear reactors has led to the development of digital twins and/or the adoption of artificial intelligence techniques for simulating baseline behavior and performing predictive maintenance. Such analytical tools, referred to as anomaly detection techniques, rely on features extracted from data that describe the underlying physical process. While these anomaly detection systems may work well with simulated data, their real-world applications are often hindered by the presence of noise. In some cases, noise may obscure subtle anomalies that may carry information about incipient stages of system faults. These subtle variations may also be the result of malicious intrusion such as so-called false data injection attack, equipment degradation causing sensor drift, or other natural disturbances in the process or the sensors. Consequently, there is a need to extract features that are robust to noise and also denoise data in a manner that aids machine-learning (ML) tools in diagnostics. In this regard, this paper presents a singular value decomposition–based statistical data–driven approach for feature extraction, denoted by randomized window decomposition, to capture the underlying physics of the system. Additionally, the features are used to denoise data to reveal subtle anomalies while also preserving relevant information for ML algorithms. The denoising algorithm is demonstrated using a RELAP5 simulation of a representative nuclear reactor with virtual noise.