The green energy revolution promised by nuclear fusion is now a step closer, thanks to the first successful use of a cutting-edge artificial intelligence system to shape the superheated hydrogen plasmas inside a fusion reactor.
The successful trial indicates that the use of AI could be a breakthrough in the long-running search for electricity generated from nuclear fusion — bringing its introduction to replace fossil fuels and nuclear fission on modern power grids tantalizingly closer.
“I think AI will play a very big role in the future control of tokamaks and in fusion science in general,” Federico Felici, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL) and one of the leaders on the project, told Live Science. “There’s a huge potential to unleash AI to get better control and to figure out how to operate such devices in a more effective way.”
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Felici is a lead author of a new study describing the project published in the journal Nature. He said future experiments at the Variable Configuration Tokamak (TCV) in Lausanne will look for further ways to integrate AI into the control of fusion reactors. “What we did was really a kind of proof of principle,” he said. “We are very happy with this first step.”
Felici and his colleagues at the EPFL’s Swiss Plasma Center (SPC) collaborated with scientists and engineers at the British company DeepMind — a subsidiary of Google owners Alphabet — to test the artificial intelligence system on the TCV.
The doughnut-shaped fusion reactor is the type that seems most promising for controlling nuclear fusion; a tokamak design is being used for the massive international ITER (“the way” in Latin) project being built in France, and some proponents think they’ll have a tokamak in commercial operation as soon as 2030.
The tokamak is principally controlled by 19 magnetic coils that can be used to shape and position the hydrogen plasma inside the fusion chamber, while directing an electric current through it, Felici explained.
The coils are usually governed by a set of independent computerized controllers — one for each aspect of the plasma that features in an experiment — that are programmed according to complex control engineering calculations, depending on the particular conditions being tested. But the new AI system was able to manipulate the plasma with a single controller, he said.
The AI – a “deep reinforcement learning” (RL) system developed by DeepMind – was first trained on simulations of the tokamak — a cheaper and much safer alternative to the real thing.
But the computer simulations are slow: It takes several hours to simulate just a few seconds of real-time tokamak operation. In addition, the experimental condition of the TCV can change from day to day, and so the AI developers needed to take those changes into account in the simulations.
When the simulated training process was complete, however, the AI was coupled to the actual tokamak.
The TCV can sustain a superheated hydrogen plasma, typically at more than 216 million degrees Fahrenheit (120 million degrees Celsius), for a maximum of 3 seconds. After that, it needs 15 minutes to cool down and reset, and between 30 and 35 such “shots” are usually done each day, Felici said.
A total of about 100 shots were done with the TCV under AI control over several days, he said: “We wanted some kind of variety in the different plasma shapes we could get, and to try it under various conditions.”
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Although the TCV wasn’t using plasmas of neutron-heavy hydrogen that would yield high levels of nuclear fusion, the AI experiments resulted in new ways of shaping plasmas inside the tokamak that could lead to much greater control of the entire fusion process, he said.
The AI proved adept at positioning and shaping the plasma inside the tokamak’s fusion chamber in the most common configurations, including the so-called snowflake shape thought to be the most efficient configuration for fusion, Felici said.
In addition, it was able to shape the plasma into “droplets” — separate upper and lower rings of plasma within the chamber — which had never been attempted before, although standard control engineering techniques could also have worked, he said.
Creating the droplet shape “was very easy to do with the machine learning,” Felici said. “We could just ask the controller to make the plasma like that, and the AI figured out how to do it.”
The researchers also saw that the AI was using the magnetic coils to control the plasmas inside the chamber in a different way than would have resulted from the standard control system, he said.
“We can now try to apply the same concepts to much more complicated problems,” he said. “Because we are getting much better models of how the tokamak behaves, we can apply these kinds of tools to more advanced problems.”
The plasma experiments at the TCV will support the ITER project, a massive tokamak that’s projected to achieve full-scale fusion in about 2035. Proponents hope ITER will pioneer new ways of using nuclear fusion to generate usable electricity without carbon emissions and with only low levels of radioactivity.
The TCV experiments will also inform designs for DEMO fusion reactors, which are seen as successors to ITER that will supply electricity to power grids – something that ITER is not designed to do. Several countries are working on designs for DEMO reactors; one of the most advanced, Europe’s EUROfusion reactor, is projected to begin operations in 2051.
Originally published on Live Science.