In the past few years, reactor thermal-hydraulic (T-H) study has advanced with the support of machine learning (ML) in many aspects, including automated experimental data analysis, data-driven prediction for important reactor thermal-fluid phenomena, and surrogate modeling and uncertainty quantification for reactor system codes. ML also showed promising potential to expand reactor T-H to a wider range of applications to better support advanced reactor deployment, such as integrated multi-physics modeling and digital twin. On the other hand, ML in T-H study has its unique challenges, from data availability and quality to model transparency and interpretability. In this panel session, experts from different institutes with a diverse background will share their experience and perspectives on ML for T-H study, including recent progresses, existing challenges and potential solutions, and future opportunities.

Speakers

Prashant Jain

ORNL

Xu Wu

NCSU

Juliana Duarte

Virginia Tech

Yang Liu

TAMU

Pat Everett

Oklo Inc

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