A team from Princeton has developed a machine learning method to control plasma edge bursts in fusion reactors, achieving high performance without instabilities and reducing computation times dramatically for real-time system adjustments.
Achieving a sustained fusion reaction is a complex, yet delicate balancing act. It requires a sea of moving parts to come together to maintain a high-performing plasma: one that is dense enough, hot enough, and confined for long enough for fusion to take place.
Yet as researchers push the limits of plasma performance, they have encountered new challenges for keeping plasmas under control, including one that involves bursts of energy escaping from the edge of a super-hot plasma. These edge bursts negatively impact overall performance and even damage the plasma-facing components of a reactor over time.
Now, a team of fusion researchers led by engineers at Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully deployed machine learning methods to suppress these harmful edge instabilities — without sacrificing plasma performance.
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