For the first time, artificial intelligence has been used to control the super-hot plasma inside a fusion reactor, offering a new way to increase stability and efficiency
Fusion reactors promise cheap, abundant and relatively clean energy – if we can get them to work. Now, thanks to artificial intelligence firm DeepMind, fusion researchers are one step closer to extracting power from plasma hotter than the surface of the sun.
DeepMind worked with scientists at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, to create a neural network capable of controlling the magnetic fields within EPFL’s Variable Configuration Tokamak (TCV) fusion reactor.
These magnetic fields are essential for keeping the plasma generated by the reactor safely contained. If the plasma touches the walls of the reactor it rapidly cools, stifling the reaction and potentially causing significant damage.
Researchers at the TCV previously used 19 magnetic coils, each controlled by a separate algorithm that monitored the interior of the reactor thousands of times a second with a host of sensors. DeepMind instead created a single neural network to control all the coils at once, automatically learning which voltages needed to be supplied to them to best contain the plasma.
The team trained the AI on a precise digital simulation of the reactor before conducting experiments on the real machine. Ultimately, it was able to successfully contain the plasma for around 2 seconds, which is approaching the limits of the reactor – TCV can only sustain the plasma in a single experiment for up to 3 seconds, after which it needs 15 minutes to cool down. The record for fusion reactors is only 5 seconds, set recently by the Joint European Torus in the UK.
As well as controlling the plasma, the AI was able to shape it and move it around within the reactor. New plasma shapes may bring efficiency or stability improvements in new fusion reactors such as ITER, which is currently being built in France and will be the world’s largest tokamak when complete in 2025. The AI even demonstrated the ability to control two separate beams of plasma at once.
Federico Felici at EPFL says that although there are many theoretical approaches that could be used to contain the plasma with a magnetic coil, scientists have tried-and-tested strategies. But the AI surprised the team with its novel approach to forming those same plasma shapes with the coils.
“This AI algorithm, the reinforcement learning, chose to use the TCV coils in a completely different way, which still more or less generates the same magnetic field,” says Felici. “So it was still creating the same plasma as we had expected, but it just used the magnetic cores in a completely different way because it had complete freedom to explore the whole operational space. So people were looking at these experimental results about how the coil currents evolve and they were pretty surprised.”
Gianluca Sarri at Queen’s University Belfast, UK, says that AI is key to the future of control systems for fusion reactors, which have yet to sustain a reaction that produces more power than is consumed.
“Once this is done, this is not the end of the story. Then you have to make it a power plant,” he says. “And this AI is, in my opinion, the only way forward. There are so many variables, and a small change in one of them can cause a big change in the final output. If you try to do it manually, it’s a very lengthy process.”
To make fusion reactors efficient, practical power sources, physicists need to increase the ratio between the pressure of the plasma and the power of the magnetic fields containing it, a value called beta, says Howard Wilson at the University of York, UK.
“The plasma writhes and wiggles and tries to escape the clutches of the magnetic fields, and as one is pushing up that beta parameter, one is having to work harder and harder to get the control that one needs to just hold the plasma there,” he says. “The further you push the plasma, the more chance you just suddenly lose it.”
Wilson believes these AI experiments show promise for containing plasma in “extreme geometries”, which paves the way for experiments with different plasma shapes that might yield improvements in stability or efficiency. “It makes the risky parameter space less risky to operate in, but also opens up new parameter space that we can go into and explore,” he says.
Journal reference: Nature, DOI: 10.1038/s41586-021-04301-9
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