In a significant advancement for fusion energy research, a team led by Zongyu Yang from the Southwestern Institute of Physics has successfully implemented a deep learning-based disruption prediction algorithm on the newly constructed HL-3 tokamak. This innovative approach not only addresses the persistent challenge of limited training data but also tackles the complex issue of data distribution that continuously drifts as the plasma operates across a wide range of parameters.
The HL-3 tokamak, a cutting-edge facility in Chengdu, China, is at the forefront of fusion research, and the ability to predict disruptions—sudden instabilities that can damage the reactor—could prove transformative for the future of fusion energy. By achieving an impressive Area Under the Receiver-Operator Characteristic Curve (AUC) of 0.940 during offline tests, Yang and his team have demonstrated that their algorithm can effectively differentiate between disruptive and non-disruptive plasma shots, even with only the initial two campaigns’ data at their disposal.
“The challenge of insufficient training data is well-known, but what we encountered was a continuously drifting data distribution unique to the HL-3,” Yang explained. This drift complicates the predictive modeling process, as the causes of disruptions can vary significantly over time and operational conditions. In contrast to traditional tokamaks, where data distributions tend to stabilize, HL-3’s evolving environment requires a more dynamic approach.
To counter these challenges, the researchers developed several innovative modules, including a “predict-first” neural network, data augmentation techniques, and pseudo data placeholders. These enhancements not only improved accuracy by up to 20% but also provided robustness in handling missing input channels. Furthermore, the system’s interpretability allows researchers to pinpoint which plasma parameters are deviating from normal conditions, a critical factor for maintaining operational safety and efficiency.
The implications of this research extend beyond the laboratory. As the global energy sector increasingly seeks sustainable solutions, advancements in fusion technology could play a pivotal role in providing a clean and virtually limitless energy source. The ability to predict disruptions effectively could accelerate the development of future fusion reactors, such as ITER (International Thermonuclear Experimental Reactor), making them more reliable and commercially viable.
“The successful implementation of our deep learning algorithm on HL-3 is a promising step toward realizing the potential of fusion energy,” Yang stated. “By overcoming the limitations posed by data scarcity and drift, we are paving the way for safer and more efficient tokamak operations.”
The findings from this research have been published in ‘Nuclear Fusion,’ a journal that highlights significant progress in the field of fusion energy. As the energy sector continues to evolve, research like this could be the key to unlocking the full potential of fusion, transforming it from a theoretical concept into a practical energy source. For more information about Zongyu Yang’s work, visit Southwestern Institute of Physics.
