In the relentless pursuit of harnessing fusion energy, scientists are continually pushing the boundaries of what’s possible. A recent breakthrough from the University of Science and Technology of China in Hefei, led by Feifei Long, promises to revolutionize the way we understand and control one of the most challenging aspects of tokamak operation: the tearing mode (TM). This large-scale magnetohydrodynamic instability can wreak havoc on the delicate balance of magnetic fields within a tokamak, leading to significant energy losses and even discharge termination. For the International Thermonuclear Experimental Reactor (ITER), which aims to demonstrate the feasibility of fusion power, accurate identification and control of TMs are crucial.
Long and her team have developed an innovative approach using physics-guided attention-aware convolutional neural networks (AA-CNNs) to identify magnetic islands formed during TMs. These islands disrupt the equilibrium magnetic surfaces, reducing core electron temperature and density, and ultimately causing energy losses. “The accurate identification of a magnetic island in real time is crucial for the effective control of the TM in ITER,” Long emphasizes.
The team utilized data from the Electron Cyclotron Emission (ECE) diagnostics in the Experimental Advanced Superconducting Tokamak (EAST) in Hefei, China. The dataset, spanning from 2016 to 2021, includes over 2.5 billion data points from 68 shots. By splitting the data into training, validation, and test sets, the researchers trained their AA-CNN model to achieve an impressive classification accuracy of 91.96% in TM identification. This is a significant improvement over conventional convolutional neural networks (CNNs) without the attention-aware mechanism.
The attention mechanism in the AA-CNN enhances the model’s capability to extract relevant features from the signals, making it more effective in identifying TMs. “By leveraging the deep learning model, which incorporates a physical understanding of the tearing process to identify TM behaviors, we emphasize the combination of physical mechanisms and deep learning,” Long explains. This fusion of physics and machine learning lays a solid foundation for future intelligent control of TM dynamics.
The implications of this research are far-reaching for the energy sector. As fusion energy moves closer to commercial viability, the ability to accurately identify and control TMs will be essential for maintaining the stability and efficiency of tokamak operations. This breakthrough could pave the way for more reliable and cost-effective fusion power, bringing us one step closer to a sustainable energy future.
The study, published in the journal Nuclear Fusion, which translates to Nuclear Fusion in English, highlights the potential of integrating deep learning with physical understanding to tackle complex challenges in fusion energy. As we continue to explore the frontiers of fusion power, innovations like the AA-CNN will be instrumental in overcoming the hurdles that stand between us and a clean, abundant energy source. The fusion of physics and artificial intelligence is not just a technological advancement; it’s a beacon of hope for a future powered by the same energy that fuels the stars.
