Abstract
The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.
| Original language | English |
|---|---|
| Article number | 3990 |
| Journal | Nature Communications |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2024 |
Funding
The authors would like to express their deepest gratitude to KSTAR and the DIII-D team. This material was supported by the U.S. Department of Energy, under awards DE-SC0020372, DE-SC0024527, DE-AC52-07NA27344, DE-AC05-00OR22725, DE-FG02-99ER54531, DE-SC0022270, DE-SC0022272, and DE-SC0019352. The U.S. Department of Energy also supported this work under contract number DEAC02-09CH11466 (Princeton Plasma Physics Laboratory). The United States Government retains a non-exclusive, paidup, irrevocable, and worldwide license to publish or reproduce the published form of this manuscript or allow others to do so for United States Government purposes. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under award(s) DE-FC02-04ER54698. This research was also supported by the R&D Program of \u201DKSTAR experimental collaboration and fusion plasma research (EN2401-15)\u201D through the KFE, funded by government funds, and the Technology development projects for Leading Nuclear Fusion through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. RS-2024-00281276). This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.