Achieving a sustained fusion reaction is a delicate balancing act, requiring 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.
With their approach, which optimizes the system's suppression response in real-time, the research team demonstrated the highest fusion performance without the presence of edge bursts at two different fusion facilities -- each with its own set of operating parameters. The researchers reported their findings on May 11 in Nature Communications, underscoring the vast potential of machine learning and other artificial intelligence systems to quickly quash plasma instabilities.
One fix involves using the magnetic coils that surround a fusion reactor to apply magnetic fields to the edge of the plasma, breaking up the structures that might otherwise develop into a full-fledged edge instability. Yet this solution is imperfect: while successful at stabilizing the plasma, applying these magnetic perturbations typically leads to lower overall performance.
"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."
Kolemen said the current work is yet another example of the potential for AI to overcome longstanding bottlenecks in developing fusion power as a clean energy resource. Previously, researchers led by Kolemen successfully deployed a separate AI controller to predict and avoid another type of plasma instability in real time at the DIII-D tokamak.