The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, GMA, Flow Walk, and Mask R-CNN) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak `a Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. High entry barriers traditionally limit tokamak plasma research to a small community of researchers in the field. By making a dataset and benchmark publicly available, we hope to open the field to a broad community in science and engineering.
翻译:尽管过去15年来在理论建模方面取得了广泛进展,但我们仍缺乏对托卡马克等磁性封闭装置的动荡的完整和一致的理解。实验性研究具有挑战性,因为驱动动荡现象高速动态的各种过程。这项工作展示了一种新型的动态跟踪应用,以识别和跟踪聚变等离子体中的动荡丝,即所谓的浮质。我们从气压成像诊断器中获取的高频视频中获取了这种分析。我们比较了四种基准方法(RAFT、GMA、流动漫步和面具R-CNN),这些方法受过合成数据培训,然后测试了托卡马克配置变量(TCV)等离子体获得的合成和真实世界数据。从对布洛布轨轨分析中确定出的浮质制度与每种基准方法的最先进的有条件平均方法一致,对这些技术的准确性充满信心。我们卡马克等离子研究在传统上对实地的小型研究人员构成了限制。通过公开设定数据基准和科学界的希望。