Torrin Bechtel Amara ^1* Sterling Paul Smith ¹ Zichuan Anthony Xing ¹ Severin Sebastian Denk ¹ Akshay-Shrikant Deshpande ¹ Andrew Oak Nelson ² Christine Simpson ^3,4 Earl William DeShazer ¹ Tom F Neiser ¹ Oscar Antepara ⁵ C Mitchell Clark ¹ Jeff Lestz ¹ Juan Diego Colmenares ¹ Nicholas Tyler ^5,6 Pengfei Ding ^5,6 Mark Kostuk ¹ Eli D Dart ^5,7 Raffi Nazikian ¹ Thomas Osborne ¹ Samuel Williams ⁵ Thomas Uram ⁴ David Schissel ¹

• ¹ General Atomics (United States), San Diego, United States
• ² Columbia University, New York City, New York, United States
• ³ Argonne Leadership Computing Facility (DOE), Argonne, Illinois, United States
• ⁴ Argonne National Laboratory (DOE), Lemont, Illinois, United States
• ⁵ Berkeley Lab (DOE), Berkeley, California, United States
• ⁶ National Energy Research Scientific Computing Center (NERSC), Berkeley, California, United States
• ⁷ Energy Sciences Network (DOE), Berkeley, California, United States

DIII-D research is being accelerated by leveraging HPC and data resources available through the NERSC Superfacility initiative and was selected to be a pathfinder for IRI. As part of this initiative, a high-resolution, fully automated, whole discharge kinetic equilibrium reconstruction workflow was developed that runs at the NERSC for most DIII-D shots in under 20 minutes. This has eliminated a long-standing research barrier and opened the door to more sophisticated analyses, including plasma transport and stability. For transport, we are looking at producing flux matched profiles and also using particle tracing to predict fast ion heat deposition from neutral beam injection before a shot takes place. Our starting point for evaluating plasma stability focuses on the pedestal limits that must be navigated to achieve better confinement. This information is meant to help operators run more effective experiments, so it needs to be available rapidly inside the DIII-D control room. So far this has been achieved by ensuring the data is available with existing tools, but as more novel results are produced new visualization tools must be developed. In addition, all of the high-quality data we have generated has been collected into databases that can unlock even deeper insights. This has already been leveraged for model and code validation studies as well as for developing AI/ML surrogates. The workflows developed for this project are intended to serve as prototypes that can be replicated on other experiments and can be run to provide timely and essential information for ITER and future FPPs.