MHD precursor to disruption in Iran tokamak 1

Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor

Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption discharges at high performance can hardly compose an abundant training set, 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 disruption prediction model containing a feature extractor that is able to extract common disrupti…

托卡马克锁定模破裂模型

A Model of Tokamak Locked Mode Disruptions

本文提出一个托卡马克锁定模破裂模型，通过分析电流收缩平衡的MHD稳定性，揭示了q=2面靠近壁时电阻壁撕裂模失稳的阈值条件，并与热猝灭实验观测一致。

Nonlinear Evolution of q=1 Triple Tearing Modes in a Tokamak Plasma

In magnetic configurations with two or three q=1 (with q being the safety factor) resonant surfaces in a tokamak plasma, resistive magnetohydrodynamic modes with poloidal mode numbers m much larger than 1 are found to be linearly unstable. It is found that these high-m double or triple tearing modes significantly enhance through nonlinear interactions, the growth of the m=1 mode. This may account for the sudden onset of the internal resistive kink, i.e., the fast sawtooth trigger. Based on the subsequent reconnection dynamics that can proceed without formation of the m=1 islands, it is propose…

Role of explosive instabilities in high-$β$ disruptions in tokamaks

Intrinsically explosive growth of a ballooning finger is demonstrated in nonlinear magnetohydrodynamic calculations of high-$β$ disruptions in tokamaks. The explosive finger is formed by an ideally unstable n=1 mode, dominated by an m/n=2/1 component. The quadrupole geometry of the 2/1 perturbed pressure field provides a generic mechanism for the formation of the initial ballooning finger and its subsequent transition from exponential to explosive growth, without relying on secondary processes. The explosive ejection of the hot plasma from the core and stochastization of the magnetic field occ…

基于卷积自编码器的等离子体破裂预测器跨托卡马克部署研究

Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder

本文针对未来托卡马克数据稀缺下的跨装置破裂预测部署问题，提出增强型卷积自编码器异常检测（E-CAAD）预测器，并验证利用现有装置数据微调可最快完成新装置部署。

基于Transformer数据驱动模型的ADITYA托卡马克电流猝灭事件早期预测

Early Prediction of Current Quench Events in the ADITYA Tokamak using Transformer based Data Driven Models

本文首次将Transformer深度学习模型应用于ADITYA托卡马克数据，实现电流猝灭的早期预测，在8-10 ms预测阈值下召回率保持0.9以上，显著优于LSTM，为短脉冲托卡马克实时破裂规避奠定基础。