Fusion energy – the process that moves the sun – has long been considered the ultimate clean energy solution.
Unlike fossil fuels, no greenhouse gases are produced, and unlike nuclear fission, no long-lived radioactive waste is produced.
Theoretically, a fusion reactor can provide virtually infinite power by fusing very high temperature, lightweight nuclei, and releasing enormous amounts of energy in the process.
However, this challenge lies in controlling the plasma, which is the ultra-humidity, electrically charged gas, when fusion occurs.
Keeping it stable and safe inside a nuclear reactor is one of the biggest hurdles to making fusion a reliable power source.
Currently, a team of expert researchers in China is leveraging the possibilities of artificial intelligence (AI) to optimize the safety and performance of fusion.
Efforts to plasma instability through AI
The research team led by Professor Sun Youwen of Hefei Institutes of Physical Science, part of the Chinese Academy of Sciences, has made a breakthrough in this field by developing two AI systems to improve reactor performance and safety.
Their findings have recently been published in Nuclear Fusion and Plasma Physics and Controlled Fusion.
The first system serves as a destruction predictor focusing on plasma instability, known as “lock mode.”
These instabilities can lead to disruption of sudden damage within the fusion reactor. Using a decision tree model, AI doesn’t just flag potential problems. It also explains the reasons behind that prediction, providing greater transparency than a typical black box AI system.
The tests showed 94% accuracy, and early warnings arrived at 137 ms before the confusion, giving the operator important time to respond.
Smarter Plasma State Recognition
The second AI tool takes a different approach, monitoring the operating state of the plasma in real time.
Traditionally, different models were used to classify plasma conditions such as low configuration (L mode) and high facility (H mode) conditions to detect dangerous edge localization modes (ELM).
The Hefei team combined these tasks into a multitasking learning model to improve both accuracy and robustness. The results showed a success rate of 96.7% correctly identifying plasma conditions.
Fundamentals of next-generation fusion reactors
These innovations illustrate key steps towards the intelligent control systems needed for future fusion reactors.
By predicting destruction before it occurs and accurately identifying plasma conditions, AI systems not only protect reactor equipment, but also enhance scientists’ understanding of plasma behavior.
As nations compete to unlock the possibilities of fusion energy, such breakthroughs bring dreams of safe, sustainable, and almost infinite power that are close to reality.
With AI integration, fusion reactors could shift one day from experimental facilities to the backbone of global clean energy infrastructure.
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