Predicting disruptions across different tokamaks is necessary for next generation device. Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge, which makes it difficult for current data-driven methods to obtain an acceptable result. A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem. The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data, and can be easily transferred to other tokamaks. Based on the concerns above, this paper presents a deep feature extractor, namely the Fusion Feature Extractor (FFE), which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks. Furthermore, an FFE-based disruption predictor on J-TEXT is demonstrated. The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics. Strong inductive bias on tokamak diagnostics data is introduced. The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks, as well as a physics-based feature extraction with a traditional machine learning method. Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction, and reach a better result compared with other deep learning methods.

The toroidal component of the velocity for geodesic acoustic modes (GAMs) is first demonstrated. Multiple Langmuir probe arrays set up near the top tokamak of the J-TEXT were utilized for this study. A significant peak at the GAM frequency is observed in Mach number fluctuations. The toroidal velocity for the GAMs is estimated as∼10-100 m s −1 and increases with the poloidal velocity. The ratio of toroidal component to the poloidal one of the velocity is mainly located in the interval between 0.3 and 1.0. With higher safety factors q, the ratio almost does not change with decreasing the safety factor, whereas it goes up sharply at low q. The coherencies between poloidal electric fields and Mach number fluctuations in turbulence frequency bands are also evaluated, and are higher than those between radial electric fields and Mach number fluctuations.