In the quest for sustainable and clean energy, scientists are continually pushing the boundaries of what’s possible with nuclear fusion. A recent breakthrough from Chinese researchers offers a glimpse into the future of plasma confinement, a critical aspect of making fusion power a viable energy source. The study, published in the journal Nuclear Fusion, details a novel approach using multi-task learning neural networks to identify and control plasma states in tokamaks, the doughnut-shaped devices designed to harness fusion energy.
At the heart of this research is Guo-Hong Deng, a scientist from the Hefei Institutes of Physical Science at the Chinese Academy of Sciences and the University of Science and Technology of China. Deng and his team have developed a multi-task learning neural network (MTL-NN) that can automatically identify different plasma confinement states in the Experimental Advanced Superconducting Tokamak (EAST). This capability is crucial for maintaining stable and efficient fusion reactions, which are essential for commercial fusion power.
The MTL-NN tackles two primary tasks: identifying operational modes and detecting edge localized modes (ELMs). ELMs are sudden releases of energy and particles from the edge of the plasma, which can damage the tokamak’s walls. By accurately detecting and controlling these events, scientists can enhance the longevity and efficiency of fusion reactors.
“The multi-task learning framework allows for mutual error correction between tasks, resulting in higher accuracy and robustness compared to single-task models,” Deng explained. This means the MTL-NN can learn from and correct errors in both tasks simultaneously, leading to more reliable plasma state identification.
The researchers used data from ELM control experiments in EAST to train their model. They selected specific features for each task—Dα and Mirnov coil measurements for ELM detection, and parameters from scaling laws for identifying operational modes. The results were impressive: the MTL-NN achieved an accuracy of 96.7% on the test set, a significant improvement over single-task models.
So, what does this mean for the future of fusion energy? The ability to automatically and accurately identify plasma states can lead to more stable and efficient fusion reactions, bringing us one step closer to commercial fusion power. This research could pave the way for more advanced control systems in future tokamaks, making fusion energy a more viable option for the energy sector.
As Deng puts it, “This work represents a significant step forward in the development of intelligent control systems for fusion reactors.” With continued research and development, these intelligent systems could revolutionize the way we harness fusion energy, making it a sustainable and clean source of power for generations to come.
The study, published in the journal Nuclear Fusion, highlights the potential of machine learning in advancing fusion energy research. As the world seeks new energy solutions, breakthroughs like this offer hope for a future powered by clean, sustainable fusion energy.