论文标题
机器学习启用KSTAR中的ELM细丝动力学分析
Machine-Learning enabled analysis of ELM filament dynamics in KSTAR
论文作者
论文摘要
在高额定模式期间，在Tokamak等离子体内定期使用电子回旋型发射成像（ECEI）诊断系统定期研究丝状结构的出现和动力学。 ECEI允许推断电子温度变化，通常是在多型横截面上。以前，已经手动对丝状动力学的详细分析和对榆树崩溃的前体进行了分类。我们提出了一个基于机器学习的模型，该模型能够自动识别ELM细丝的位置，空间范围和振幅。该模型是一个深度卷积神经网络，已在KSTAR Tokamak的一组手动标记的ECEI数据上进行了训练和优化。一旦受过训练，该模型就可以达到93.7％的精度，并允许在看不见的ECEI数据中稳健地识别血浆丝。训练有素的模型用于表征单个H模式等离子体拍摄中的ELM细丝动力学。我们确定了细丝大小，总热量和径向速度的准周期性振荡。这些数量的详细动力学似乎相互关联，并且在爆炸和榆树崩溃阶段的质量不同。
The emergence and dynamics of filamentary structures associated with edge-localized modes (ELMs) inside tokamak plasmas during high-confinement mode is regularly studied using Electron Cyclotron Emission Imaging (ECEI) diagnostic systems. ECEI allows inference of electron temperature variations, often across a poloidal cross-section. Previously, detailed analyses of filamentary dynamics and classification of the precursors to ELM crashes have been done manually. We present a machine-learning-based model, capable of automatically identifying the position, spatial extent, and amplitude of ELM filaments. The model is a deep convolutional neural network that has been trained and optimized on an extensive set of manually labeled ECEI data from the KSTAR tokamak. Once trained, the model achieves a 93.7% precision and allows to robustly identify plasma filaments in unseen ECEI data. The trained model is used to characterize ELM filament dynamics in a single H-mode plasma shot. We identify quasi-periodic oscillations of the filaments' size, total heat content, and radial velocity. The detailed dynamics of these quantities appear strongly correlated with each other and appear qualitatively different during the pre-crash and ELM crash phases.
