Science, Space and Technology News 2024
Researchers at Princeton and the Princeton Plasma Physics Laboratory have successfully used machine learning to suppress energy bursts at the edges of plasma in fusion reactors, enhancing performance without damage. Credit: SciTechDaily.com
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.Depiction of a tokamak.
However, the high-confinement mode has historically come hand-in-hand with instabilities at the plasma’s edge, a challenge that has required fusion researchers to find creative workarounds. “In the past, everything has had to be pre-programmed,” said co-first author SangKyeun Kim, a staff research scientist at PPPL and former postdoctoral researcher in Kolemen’s group. “That limitation has made it difficult to truly optimize the system, because it means that the parameters can’t be changed in real time depending on how the conditions of the plasma unfold.
“Some machine learning approaches have been critiqued for being solely data-driven, meaning that they’re only as good as the amount of quality data they’re trained on,” Shousha said. “But since our model is a surrogate of a physics code, and the principles of physics apply equally everywhere, it’s easier to extrapolate our work to other contexts.
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