This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Inside some fusion energy systems, particles hotter than the core of the sun can become unruly in a few thousandths of a second, far faster than any human operator can react. A new software framework developed by researchers at the U.S.
Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University hands those split-second decisions to artificial intelligence (AI), while keeping the machine safe and humans firmly in charge of the goals. Known as PACMAN (a novel abbreviation for Prediction And Control using MAchiNe learning), the AI framework was successfully tested on a real fusion system in five experiments. The framework's design and first results are detailed in a new paper in the journal Nuclear Fusion.
Fusion could one day serve as a virtually unlimited source of electricity. Scientists are working on several ways to perfect the process here on Earth, including devices called tokamaks, which use powerful magnetic fields to hold a plasma: an electrically charged gas often called the fourth state of matter. Keeping the plasma hot, dense and stable requires constant adjustments to the tokamak, including its heating systems, magnets and gas injectors.
The fusion reaction can be thwarted by small disturbances in the plasma, known as instabilities, that grow in milliseconds. Plasma is also hard to predict. The sophisticated computer programs used to simulate the behavior of a plasma can take days or months to run.
That approach is far too slow to work in real time during an experiment that might only last 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 plasma behavior very well, and importantly, they are the only way we have to model plasma on millisecond time scales.
The speed of these models is what's key for control." Machine learning has already shown great promise for taming fusion plasmas. But most attempts to use it to control a fusion plasma were built from scratch, without a set of overarching design principles to ensure the models could be easily combined. Multiple models are needed to monitor and control different aspects of the fusion system.
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