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Avoiding fusion plasma tearing instability with deep reinforcement learning.
- Source :
-
Nature [Nature] 2024 Feb; Vol. 626 (8000), pp. 746-751. Date of Electronic Publication: 2024 Feb 21. - Publication Year :
- 2024
-
Abstract
- For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance <superscript>1-4</superscript> . However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators <superscript>5</superscript> . Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D <superscript>6</superscript> , the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1476-4687
- Volume :
- 626
- Issue :
- 8000
- Database :
- MEDLINE
- Journal :
- Nature
- Publication Type :
- Academic Journal
- Accession number :
- 38383624
- Full Text :
- https://doi.org/10.1038/s41586-024-07024-9