基于神经网络的EAST密度极限破裂预测

为了对全超导托卡马克核聚变实验装置(EAST)密度极限破裂进行预测, 根据密度极限破裂的基本特征从2014到2019年放电数据中筛选出972炮密度极限破裂炮, 选取了13种诊断信号为特征作为输入, 分别由多层感知机(MLP)和长短时记忆网络(LSTM)为模型、以破裂概率为模型输出建立破裂预测器对密度极限破裂进行预测实验. 结果表明: 对密度极限破裂炮, 在不同的预警时间下, LSTM的成功预测率(95%)均高于MLP的成功预测率(85%); 而对于非破裂炮, LSTM和MLP的错误预测率相近(8%). LSTM对密度极限破裂的预测性能较MLP有较大的提高. 说明利用神经网络进行EAST密度极限破裂预测以及提高破裂避免和缓解系统响应性能的可行性.     

Development of Dα band symmetrical visible optical diagnostic for boundary reconstruction on EAST tokamak

To investigate the potential of utilizing visible spectral imaging for controlling the plasma boundary shape during stable operation of plasma in future tokamak, a D_α band symmetric visible light diagnostic system was designed and implemented on the Experimental Advanced Superconducting Tokamak(EAST). This system leverages two symmetric optics for joint plasma imaging. The optical system exhibits a spatial resolution less than 2 mm at the poloidal cross-section, distortion within the ...     

Density limit disruption prediction using a long short-term memory network on EAST

Disruption prediction using a long short-term memory (LSTM) algorithm has been developed on EAST, due to its inherent advantages in time series data processing. In the present work, LSTM is used as the model and the AUC (area under receiver operation characteristic curve) is used as the evaluation index. When the model is trained on data from the plasma current flattop phase and tested on data from the same period multiple times, the highest AUC is 0.8646 and the training time is about 6900 s per epoch. For comparison, the last 1000 ms of the flattop phases are intercepted as short time sequences. When the model is trained on data from short time sequences and tested on data from the same period, the highest AUC is increased to 0.9379 and the training time is restricted to 36 s per epoch. When the best model trained on the short time sequences is applied to the flattop phase for testing, the AUC is up to 0.9189. The experiment results show that it is possible for LSTM to train the model on data from short time sequences and migrate the model to the entire flattop phase, with a shorter training time and higher AUC value.