Feb 16, 2022 - Science

This AI can control the fuel for fusion energy

Plasma inside the TCV tokamak. Regions of different colors correspond to different temperatures of the hot plasma.

Plasma inside the TCV tokamak. Credit: Curdin Wüthrich/SPC/EPFL

Researchers have developed an algorithm to control the hot, chaotic plasma that fuels fusion energy reactors.

Why it matters: The plasma in fusion reactors needs to be controlled in order to extract energy from the reactions. Fusion is a potential source of clean fuel that has long been promised — but not yet delivered.

Background: The goal of nuclear fusion is to harness the energy released when two atomic nuclei of hydrogen are pushed together.

  • Experimental reactors are notching advances in creating energy but to become useful as a commercial fuel source, reactors will have to generate more energy than is required to produce the reaction in the first place.
  • One approach for creating and controlling plasma is to use powerful magnets in a donut-shaped reactor called a tokamak.

How it works: The tokamak at the Swiss Plasma Center at the EPFL is used to study the optimal conditions for confining constantly-changing plasma.

  • The plasma's shape and distribution in the tokamak can be adjusted by changing the voltage in the 19 magnets that hold it in place.
  • But testing new plasma configurations by changing the interconnected conditions in the tokamak requires significant engineering and design effort.

What they did: DeepMind researchers developed a deep reinforcement learning algorithm that learned to control the magnetic coils to yield — and maintain — different plasma configurations.

  • The algorithm takes in 90 observations about the plasma and adjusts the coils to produce different plasma shapes.
  • They first trained the algorithm in a simulator then tested it in the tokamak at the Swiss Plasma Center.
  • It was able to produce a range of shapes — conventional, elongated plasmas and advanced ones, including a snowflake configuration and an arrangement of two separate plasmas at the same time, the researchers reported in the journal Nature.

The intrigue: The algorithm was able to handle a major challenge presented by the plasma — its state changes constantly. Unlike a game of Go, which a DeepMind algorithm has mastered, there aren't discrete choices to be made, Jonas Buchli of DeepMind said in a press briefing. There are "an infinite amount of possibilities."

  • "AI, and specifically reinforcement learning, is particularly well-suited to the complex problems presented by controlling plasma," Martin Reidmiller of DeepMind said in the briefing.

What's next: The results suggest there may be new ways to control the plasma, Anne White, a professor of nuclear science and engineering at MIT who was not involved in the research told Axios in an email.

  • Those might include "how to optimize control of the plasma shape and heat load at the divertor (the main heat “exhaust pipe”) dynamically during a tokamak pulse."

Keep in mind: The tokamak in the study is designed for research purposes. But the researchers say the machine learning approach could be used to test control of future reactors.

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