25 citations · 40 across the 7 of their papers we have counts for
7 papers
Effects of turbulence spreading and symmetry breaking on edge shear flow during sawtooth cycles in J-TEXT tokamak
Xiaoguan Ding, Kaijun Zhao, Yaoyu Xie +7
Sawtooth oscillations can trigger off heat and turbulence pulses that propagate into the edge plasma, and thus enhancing the edge shear flow and inducing a transition from low conf…
Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT
Chengshuo Shen, Jianchao Li, Yonghua Ding +10
Measurement of locked mode (LM) is important for the physical research of Magnetohydrodynamic (MHD) instabilities and plasma disruption. The n = 0 pick-up need to be extracted and…
A weighted matching scheme of magnetic coil design for FRC shaping control
Zitong Qu, Ping Zhu, Zhipeng Chen +2
The two-dimentional (2D) separatrix shaping plays a crucial role in the confinement of the Field Reversed Configuration (FRC), and the magnetic coils serve as an effective means fo…
Critical roles of edge turbulent transport in the formation of high-field-side high-density front and density limit disruption in J-TEXT tokamak
Peng Shi, Yuhan Wang, Li Gao +16
This article presents an in-depth study of the sequence of events leading to density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary turbulent transport and…
Validation of the plasma-wall self-organization model for density limit in ECRH-assisted start-up of Ohmic discharges on J-TEXT
Jiaxing Liu, Ping Zhu, Dominique Franck Escande +14
A recently developed plasma-wall self-organization (PWSO) model predicts a significantly enhanced density limit, which may be attainable in tokamaks with ECRH-assisted ohmic startu…
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
Xinkun Ai, Wei Zheng, Ming Zhang +12
The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-dr…
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Chengshuo Shen, Wei Zheng, Yonghua Ding +10
Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. Understanding why a predictor makes a certain prediction can be as…