In the blink of an eye, the unruly, overheated plasma that drives nuclear fusion can escape its magnetic confinement in the donut-shaped device, or tokamak, designed to contain it. These leaks often mark the end of the reaction, posing a major challenge to the development of fusion as a non-polluting and virtually unlimited energy source.
But a team led by Egemen Kolemen *08, associate professor of mechanical and aerospace engineering and the Andlinger Center for Energy and Environmenttrained an AI controller to predict and then avoid a type of plasma instability in real time.
The team first trained the controller on data from past experiments at National DIII-D Fusion Facility in San Diego. They then demonstrated that the controller could learn from past experiments to predict the likelihood of instability in new fusion experiments and adjust specific reactor parameters in milliseconds to prevent instability from ever forming.
The researchers showed that their model could predict potential plasma instabilities, known as tear mode instabilities, up to 300 milliseconds in advance. Although this does not leave more time than is necessary for a slow blink in humans, the AI controller had plenty of time to change some operating parameters to avoid what would have turned into a tear in the plasma’s magnetic field lines, disrupting its balance and openness. the door for an escape ending the reaction.
“By learning from past experiences, rather than incorporating information from physics-based models, AI could develop a final control policy that would support a stable, high-power plasma regime in real time, in a real reactor,” said Kolemen, who is also a research physicist at the Princeton Plasma Physics Laboratory (PPPL).
The research opens the door to more dynamic control of a fusion reaction than current approaches and provides a basis for using artificial intelligence to address a wide range of plasma instabilities, which have long been obstacles to achieve a sustained fusion reaction.
“Previous studies have generally focused on suppressing or attenuating the effects of these tear instabilities after they appear in the plasma,” said the first author. Jaemin Seoassistant professor of physics at Chung-Ang University in South Korea, who did much of the work while he was a postdoctoral researcher in Kolemen’s group. “But our approach allows us to predict and avoid these instabilities before they appear.”
Although the researchers say this work represents a promising proof of concept demonstrating how artificial intelligence can effectively control fusion reactions, it is just one of many next steps already underway in Kolemen’s group to advance the field of fusion research.
The first step is to obtain more evidence that the AI controller works on the DIII-D tokamak, and then expand the controller to work on other tokamaks.
A second line of research involves extending the algorithm to handle many different control problems simultaneously. Although the current model uses a limited number of diagnostics to avoid a specific type of instability, researchers could provide data on other types of instabilities and provide access to more buttons for the AI controller to adjust, allowing it potentially controlling several types of instabilities. instabilities simultaneously.
And by developing better AI controllers for fusion reactions, researchers could also better understand the underlying physics. By studying the decisions of the AI controller as it attempts to contain the plasma, which may be radically different from what traditional approaches might prescribe, artificial intelligence can be not only a tool for controlling fusion reactions , but also an educational resource.
“Eventually, this could be more than just a one-way interaction between scientists developing and deploying these AI models,” Kolemen said. “By studying them in more detail, they might have some things they could teach us as well.” »
This article was adapted from a previously published article.