According to both U.S. NRC and DOE, the nuclear industry has yet to fully leverage the recent advances in machine learning/artificial intelligence (ML/AI) techniques, and Digital Twin (DT) will play a significant role in risk-informed decision-making in this regard. For example, "NRC FY2021-23 Planned Research Activities" and "NRC Future Focused Research" state that "Methodology and Evaluation Tools for Digital Twin Applications" is one of the top priority strategic areas. However, the major challenges related to DT include but are not limited to (a) Incorporating trustworthy data analytics algorithm, (b) Treatment of noisy or erroneous data and data unavailability, (c) Uncertainty quantification, (d) Robust optimization, and (e) Update module in DT by solving the "On-the-fly Inverse Problem." This session will discuss the DT-enabling technologies and components for nuclear systems.

Panelists

  • Syed Bahauddin Alam (University of Illinois Urbana-Champaign)
  • Prashant Jain (ORNL)
  • Zahra Mohaghegh (Univ. Illinois, Urbana-Champaign)
  • Tanwi Mallick (ANL)

Discussion

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