Princeton Team Harnesses Machine Learning to Boost Fusion Reactor Performance

A team of fusion researchers from Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have used machine learning to control harmful energy bursts in plasma, a key challenge in achieving sustained fusion reactions. The team, led by Egemen Kolemen, demonstrated the highest fusion performance without these energy bursts at two different facilities. The machine learning approach significantly reduces computation time, allowing for real-time adjustments to the plasma’s conditions. The team’s findings, published in Nature Communications, highlight the potential of artificial intelligence in overcoming challenges in developing fusion power as a clean energy resource.

Machine Learning Enhances Fusion Reactor Performance

Fusion reactors, the potential future of clean energy, operate under a delicate balance of conditions. A high-performing plasma must be dense, hot, and confined long enough for fusion to occur. However, as researchers push the boundaries of plasma performance, they encounter challenges in controlling plasma, including energy bursts from the plasma’s edge. These bursts can negatively impact performance and damage reactor components over time.

A team of fusion researchers led by Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully used machine learning to suppress these harmful edge instabilities without sacrificing plasma performance. Their findings, published in Nature Communications, highlight the potential of machine learning and artificial intelligence in managing plasma instabilities.

The Challenge of High-Confinement Mode

One promising approach to achieving the necessary conditions for fusion involves operating a reactor in high-confinement mode. This mode is characterized by a steep pressure gradient at the plasma’s edge, which enhances plasma confinement. However, this mode often leads to instabilities at the plasma’s edge, requiring creative solutions from fusion researchers.

One such solution involves using magnetic coils surrounding a fusion reactor to apply magnetic fields to the plasma’s edge, disrupting structures that could develop into a full-fledged edge instability. However, this solution is not perfect: while it stabilizes the plasma, it typically leads to lower overall performance.

Machine Learning for Real-Time Optimization

The Princeton-led team’s machine learning approach significantly reduces computation time, allowing for real-time optimization. The machine learning model can monitor the plasma’s status from one millisecond to the next and alter the amplitude and shape of the magnetic perturbations as needed. This allows the controller to balance edge burst suppression and high fusion performance without sacrificing one for the other.

The researchers demonstrated the success of their approach at both the KSTAR tokamak in South Korea and the DIII-D tokamak in San Diego. At both facilities, the method achieved strong confinement and high fusion performance without harmful plasma edge bursts.

Future Applications and Improvements

The team is already working to refine their model to be compatible with other fusion devices, including planned future reactors such as ITER, which is currently under construction. One active area of work involves enhancing the model’s predictive capabilities. For instance, the current model still relies on encountering several edge bursts over the course of the optimization process before working effectively, posing unwanted risks to future reactors.

The researchers aim to improve the model’s ability to recognize the precursors to these harmful instabilities, potentially optimizing the system without encountering a single edge burst. This work is another example of the potential for AI to overcome longstanding bottlenecks in developing fusion power as a clean energy resource.

The Role of AI in Fusion Power Development

The use of AI in fusion power development is not new. Previously, researchers led by Kolemen successfully deployed a separate AI controller to predict and avoid another type of plasma instability in real time at the DIII-D tokamak.

The machine learning approaches have unlocked new ways of approaching these well-known fusion challenges. The computational complexity of traditional tools has often limited the implementation of solutions. However, machine learning and AI offer a way to overcome these limitations, opening up new possibilities for the development of fusion power as a clean energy resource.