{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:17:19Z","timestamp":1773692239185,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006133","name":"U.S. Department of Energy, Advanced Research Projects Agency\u2014Energy (ARPA-E)","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100006133","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor\u2019s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor\u2019s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.<\/jats:p>","DOI":"10.3390\/computation10070108","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T20:47:56Z","timestamp":1656535676000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0361-633X","authenticated-orcid":false,"given":"Stella","family":"Pantopoulou","sequence":"first","affiliation":[{"name":"Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA"},{"name":"School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA"}]},{"given":"Victoria","family":"Ankel","sequence":"additional","affiliation":[{"name":"Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA"},{"name":"Department of Physics, University of Chicago, Chicago, IL 60637, USA"}]},{"given":"Matthew T.","family":"Weathered","sequence":"additional","affiliation":[{"name":"Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8214-2114","authenticated-orcid":false,"given":"Darius D.","family":"Lisowski","sequence":"additional","affiliation":[{"name":"Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA"}]},{"given":"Anthonie","family":"Cilliers","sequence":"additional","affiliation":[{"name":"Kairos Power, Alameda, CA 94501, USA"}]},{"given":"Lefteri H.","family":"Tsoukalas","sequence":"additional","affiliation":[{"name":"School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8891-9323","authenticated-orcid":false,"given":"Alexander","family":"Heifetz","sequence":"additional","affiliation":[{"name":"Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.pnucene.2010.12.001","article-title":"Application of fault detection and diagnosis in nuclear power plants: A review","volume":"53","author":"Ma","year":"2011","journal-title":"Prog. 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