The improved energy confinement mode (I-mode) is widely considered as an important operation regime for ITER. I-mode implementation depends on the specified basic plasma parameters and certain operation conditions, which are discovered by statistical plasma characteristics from a large number of I-mode discharges on a tokamak. The extraction process of I-mode plasma characteristics is complicated, time-consuming, and limited to the sampling rate of the measured signals. Experimental observation of the I-mode is accompanied by the appearance of a weakly coherent mode (WCM). However, it takes much time to accurately scan and quantify WCM characteristics when analyzing many I-mode discharges. Recently, a neural network identification method was developed as an I-mode detector to traverse a whole database as a replacement for manual identification. Two fully connected neural network models were trained with the spectrum of propagation velocity of density perturbation from Doppler backward scattering and the electron density measured by a polarimeter-interferometer system with the experimental advanced superconducting tokamak I-mode database. An accuracy of 98.30% in identifying WCMs in I-mode discharges is achieved with the WCM classification model. In addition, the regime classification model was also utilized to successfully distinguish between the low confinement mode (L-mode), I-mode, and high confinement mode (H-mode) with 96.03% accuracy. Finally, ablation experiments were performed on the regime classifiers, showing that there is potential for further performance improvement with future use of RNN model.
2013 Nuclear Fusion Prize Acceptance Speech