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November 30–December 3, 2021
Washington, DC|Washington Hilton
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Nuclear Science and Engineering
Fusion Science and Technology
Neutron noise monitoring during plant operation expedites flexure replacement at Salem-1
The nuclear industry has historically relied on intermittent ultrasonic test and visual inspections of pressurized water reactor components to identify and manage degradation. While this reactive approach has proven to be effective, imagine a scenario in which the degradation could propagate throughout the reactor internals, making a more proactive measure necessary to avoid a major enterprise risk to the plant. Could a utility identify the onset of degradation within the reactor internals during plant operation? If so, could a repair be developed prior to the next refueling outage to prevent additional, cascading degradation? That is exactly the situation that Public Service Enterprise Group (PSEG) and Westinghouse engineers were able to navigate over the course of the 2019–2020 operating cycle at Salem Unit 1, resulting in a tremendous success for the plant and a historic landmark in the nuclear industry, while earning the team a 2021 Nuclear Energy Institute Top Innovative Practice (TIP) award.
Keisuke Fujii, Ichihiro Yamada, Masahiro Hasuo
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 57-64
Technical Paper | dx.doi.org/10.1080/15361055.2017.1396179
Articles are hosted by Taylor and Francis Online.
Manual uncertainty propagation from possible noise sources has often been adopted for data analysis in many fields of science, including the analysis of Thomson scattering measurement data in fusion plasma science. However, it is not possible to perfectly model all the noise sources and their distributions. In this work, we propose a more data-driven approach for the noise modeling of multichannel measurement systems. We directly modeled the noise distribution by tractable density distributions parameterized with neural networks and trained their weights from a vast amount of measurement data. We demonstrated an application of this method in Thomson scattering measurement data for the Large Helical Device project. This method enabled us to make a realistic inference even without sufficient prior knowledge about the noise.