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.

Speakers

Luis Betancourt

NRC

Scott Sidener

Westinghouse

Antonio Cammi

Politecnico di Milano

Yang Liu

TAMU

Resources

To access session resources, you must be logged in and registered for the meeting.

Register NowLog In

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

To join the conversation, you must be logged in and registered for the meeting.

Register NowLog In