Machine Learning for Gas Void Fraction Prediction in Two-Phase Flow

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations
Original languageEnglish
Pages (from-to)1288-1291
Number of pages4
JournalTransactions of the American Nuclear Society
Volume130
Issue number1
DOIs
StatePublished - 2024
Event2024 Annual Conference on Transactions of the American Nuclear Society, ANS 2024 - Las Vegas, United States
Duration: Jun 16 2024Jun 19 2024

Funding

This research was supported in part by the US Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists under the Science Undergraduate Laboratory Internships program. This research was also supported by the Nuclear Energy Advanced Modeling and Simulation program for Modeling and Simulation of Nuclear Reactors under US Department of Energy contract no. DE-AC05-00OR22725.

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