In some fusion systems, particles hotter than the core of the sun can become unstable within just a few thousandths of a second. That is far too fast for a human operator to respond. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University have developed a new software framework that uses artificial intelligence (AI) to make those rapid decisions while maintaining strict safety controls and leaving people responsible for setting the system's objectives.
The framework is called PACMAN (a novel abbreviation for Prediction And Control using MAchiNe learning). Researchers successfully tested it on a real fusion system in five separate experiments. Its design and initial results are described in a new paper published in the journal Nuclear Fusion.
AI Takes on Fusion's Millisecond Challenge Fusion has the potential to provide a virtually unlimited supply of electricity. Researchers are exploring several approaches to making fusion practical on Earth, including machines known as tokamaks. These devices rely on powerful magnetic fields to confine a plasma: an electrically charged gas often called the fourth state of matter.
For fusion to continue successfully, the plasma must remain hot, dense, and stable. That requires frequent adjustments to systems such as the tokamak's heating equipment, magnets and gas injectors. Even relatively small disturbances in the plasma, known as instabilities, can grow within milliseconds and disrupt the fusion reaction.
Predicting plasma behavior is another major challenge. Advanced computer simulations can take days or even months to complete. While those tools are valuable for planning future experiments, they are far too slow to guide an experiment in real time when the entire test may last only a few minutes.
"That's great for preparing for the next experiment in a year, but for control we need models that make a decision in the moment," said co-lead author Hiro Farre Kaga, a graduate student in the Princeton Program in Plasma Physics, which is a joint program of Princeton University and PPPL. "Machine learning models can describe the plasma behavior very well, and importantly, they are the only way we have to model the plasma in millisecond times. The speed of these models is what's key for control." Bringing Multiple AI Models Into One Fusion System Machine learning has already shown considerable potential for controlling fusion plasmas.
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