Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak
Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak
复制标题
用于 J-TEXT 托卡马克密度极限破坏预测和避免的混合神经网络
DOI：
10.1088/1741-4326/aaad17
复制
发表时间：
2018
期刊：
影响因子：
3.3
通讯作者：
Pan Y
中科院分区：
文献类型：
--
作者：
Zheng W;Hu F R;Zhang M;Chen Z Y;Zhao X Q;Wang X L;Shi P;Zhang X L;Zhang X Q;Zhou Y N;Wei Y N;Pan Y
提高等离子体密度是实现有效聚变反应的关键方法之一。高密度运行是托卡马克等离子体研究的热点之一。密度极限中断仍然是安全运行的重要问题。有效的密度极限扰动预测与规避系统是长脉冲稳态运行避免密度极限扰动的关键。本文提出了一种用于预测J-TEXT托卡马克密度极限破裂的人工神经网络。神经网络由简单的多层结构改进为混合两级结构。第一阶段是一个自定义网络，它使用时间序列诊断作为输入来预测等离子体密度，第二阶段是一个三层前馈神经网络来预测密度极限中断的概率。结果表明，采用混合神经网络结构，结合辐射廓线信息作为输入，可以显著提高预报性能，尤其是平均预警时间（Twarn）。特别地，Twarn比先前工作中的Twarn好八倍（Wang等人2016 Plasma Phys. Control. Fusion 58 055014）（从5 ms到40 ms）。密度极限干扰炮的成功率在90%以上，其他炮的虚警率在10%以下。在J-TEXT托卡马克装置上，基于密度极限扰动预报系统和实时密度反馈控制系统，实现了在线密度极限扰动避免系统。
Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time (Twarn). In particular, the Twarn is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.