For decades, scientists have been trying to develop clean, limitless energy by re-creating the conditions at the center of the sun here on Earth. But if nuclear fusion is to be practical for electricity production, that immense, raging power must be controlled.
“When the plasma in a fusion experiment becomes unstable, it can escape confinement and touch the wall of the machine, causing severe damage and sometimes even melting or vaporizing components,” said Julian Kates-Harbeck, a physics Ph.D. student and a Department of Energy (DOE) Computational Science Graduate Fellow. “If you could predict those escapes, or ‘disruptions,’ you could mitigate their effects and build in safety protocols that would cool the plasma down gently and keep it from damaging the machine.”
In a new study published in Nature and led by the U.S. DOE’s Princeton Plasma Physics Laboratory (PPPL), Kates-Harbeck and his colleagues created a “deep learning” artificial intelligence (AI) code to successfully forecast disruptions in fusion reactors. The new method’s predictions can be applied across machines, making it a major step forward for international fusion energy initiatives.
The harder they come, the harder they fall
In the sun, lighter elements are fused into heavier ones in the form of intensely hot plasma, which generates energy. To re-create fusion, scientists use a tokamak: a building-sized, donut-shaped machine that contains hot plasma using a powerful magnetic field.
“Some of the biggest nuclear fusion machines in the field — hundreds of tons of solid steel — can jump a centimeter up in the air when a disruption happens,” said Kates-Harbeck. “That gives you an idea how much power is released. You really don’t want this to happen.”
Disruption prediction is critical, because the bigger the machine, the bigger the disruption. In the $25 billion Iter tokamak currently under construction in France, disruptions are expected to be severe: The machine has more than eight times the volume and energy of the Joint European Torus (JET), the next-largest magnetically confined plasma physics experiment in operation, and less surface area to capture it.
“We don’t have good strategies for completely avoiding these disruption events yet,” said Kates-Harbeck. “The best thing we can do is to predict when they are going to happen so we can avoid most of their adverse effects. That might be, for example, by injecting neutral gas that cools the plasma before it smashes into the wall. But you can’t mitigate anything if you don’t know it’s coming.”
Kates-Harbeck worked at Princeton for a summer through his DOE fellowship. There, he teamed up with Bill Tang, principal research physicist at PPPL, professor in the Department of Astrophysical Sciences at Princeton, and the senior author on the study. Tang was just starting a new project using machine learning to tackle disruption prediction. For Kates-Harbeck, it was the perfect combination.
“My background is in physics and AI, so being able to combine both while working on a problem as meaningful as fusion energy — a topic I had always wanted to work on — was like hitting the jackpot,” he said. “People have been using classical machine learning on disruption prediction for years, but I’d just come out of my [computer science] master’s program and got really excited about applying deep learning to the problem. Bill was very open and supportive of the idea, and that’s how we got started.”
Tang called artificial intelligence “the most intriguing area of scientific growth right now,” and said, “to marry it to fusion science is very exciting. We’ve accelerated the ability to predict with high accuracy the most dangerous challenge to clean fusion energy.”