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Harnessing AI to Overcome Fusion Energy's Tearing Instability Challenge

Reinforcement Learning

In a groundbreaking study, researchers have developed an artificial intelligence (AI) system that significantly reduces the risk of tearing instability in fusion plasma, a common obstacle in the path to efficient fusion energy production. This advancement is a crucial step toward stable and sustainable fusion energy, promising a future of clean and abundant power.

The tearing instability occurs in the plasma of a tokamak reactor, a device designed to harness the power of fusion. This instability leads to disruptions that can damage the reactor and interrupt the fusion process. To mitigate this, scientists have turned to AI, specifically deep reinforcement learning (RL), to predict and prevent these instabilities by actively controlling the tokamak based on the observed state of the plasma.

The study utilized a dynamic model developed from extensive data gathered from diagnostics and actuators, which estimates the likelihood of future tearing instabilities. This model served as a training environment for an AI system, enabling it to learn how to adjust the reactor's controls to maintain high-pressure plasma without triggering instability. The successful application of this AI system was demonstrated at the DIII-D National Fusion Facility, the United States' largest magnetic fusion research facility, where it effectively kept the plasma stable under challenging conditions.

This AI-controlled approach allows for real-time adjustments to the reactor's operations, enabling it to navigate the narrow path between high plasma pressure and the onset of tearing instability. It represents a significant advancement over traditional preprogrammed control methods, which struggle to adapt to the dynamic conditions inside a tokamak reactor.

The significance of this research extends beyond the technical achievement of stabilizing plasma. It addresses one of the critical barriers to the development of fusion energy as a reliable and clean energy source. Fusion energy, powered by the same processes that fuel the sun, offers the promise of a virtually limitless supply of energy without the carbon emissions associated with current energy sources or the long-lived radioactive waste produced by traditional nuclear fission reactors.

Recent milestones in fusion research, such as the production of net energy gain demonstrated at the National Ignition Facility and the extended plasma sustainment achieved by various international tokamak projects, underscore the potential of fusion energy. However, challenges like plasma disruption and tearing instability have remained significant hurdles.

By integrating AI into the control systems of tokamak reactors, scientists can better predict and mitigate the conditions that lead to instability, making long-pulse, stable fusion operations more achievable. This research not only paves the way for the ITER project, an international endeavor to build the world's largest tokamak reactor, but also for the future of energy production. It highlights the role of advanced computing and AI in solving complex problems in energy science, potentially accelerating the arrival of fusion energy as a sustainable and clean power source for the world.

Read the paper titled ‘Avoiding fusion plasma tearing instability with deep reinforcement learning’.