Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning

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Abstract

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. The results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

Original languageEnglish
Pages (from-to)1660-1672
Number of pages13
JournalSeismological Research Letters
Volume93
Issue number3
DOIs
StatePublished - May 2022

Funding

The work described in this article was funded by the U.S. National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development, Office of Proliferation Detection. This article has been authored in part by UT-Battelle, LLC, under Contract Number DE-AC05-00OR22725 with the US Department of Energy (DOE). This research used resources at the High Flux Isotope Reactor (HFIR), a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory (ORNL). This research used resources of the Compute and Data Environment for Science (CADES) at the ORNL, which is supported by the Office of Science of the U.S. Department of Energy under Contract Number DE-AC05-00OR22725. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this article, or allow others to do so, for U.S. Government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan, last accessed January 2022). The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government. The authors acknowledge that there are no conflicts of interest recorded. The authors thank Jason Hite for helpful suggestions. The authors thank the Editor Allison Bent, and two anonymous reviewers for their constructive comments.

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