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 modeling, and uncertainty quantification. ML also showed promising potential to expand reactor T-H to a wider range of applications to better support advanced reactor deployment, such as digital twin. On the other hand, ML in T-H study has its unique challenges, from data availability and quality, model transparency and interpretability, to licensing readiness. 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.

Panelists

  • Luis Betancourt (NRC)
  • Scott Sidener (Westinghouse)
  • Antonio Cammi (Politecnico di Milano)
  • Yang Liu (TAMU)

Resources

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Presentation Slides (Visible to Attendees) — Introduction

Presentation Slides (Visible to Attendees) — Cammi

Presentation Slides (Visible to Attendees) — Betancourt

Presentation Slides (Visible to Attendees) — Sidener

Presentation Slides (Visible to Attendees)

Discussion

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