Evidence of a nonlinear transition from mitigation to suppression of the edge localized mode (ELM) by using resonant magnetic perturbations (RMPs) in the EAST tokamak is presented. This is the first demonstration of ELM suppression with RMPs in slowly rotating plasmas with dominant radio-frequency wave heating. Changes of edge magnetic topology after the transition are indicated by a gradual phase shift in the plasma response field from a linear magneto hydro dynamics modeling result to a vacuum one and a sudden increase of three-dimensional particle flux to the divertor. The transition threshold depends on the spectrum of RMPs and plasma rotation as well as perturbation amplitude. This means that edge topological changes resulting from nonlinear plasma response plays a key role in the suppression of ELM with RMPs. DOI: 10.1103/PhysRevLett.117.115001 Magnetic reconnection and the resultant topological change play an important role in plasma dynamics in both laboratory and space plasma physics research. The formation of an edge stochastic magnetic field caused by resonant magnetic perturbations (RMPs) is believed to be the reason for the suppression of periodic crash events near the plasma edge known as the edge localized mode (ELM) observed in the DIII-D tokamak [1]. The ELM causes transient heat loads to the plasma facing components and may degrade them on the next generation fusion device like ITER [2]. The reduction of free energy in the edge pressure gradient and current because of field stochasticity moves the plasma into a stable regime against the ELM [3]. This successful experiment motivated ELM control using RMPs in many other tokamaks [4][5][6][7]. However, the plasma response effect usually shields the external applied RMPs and may significantly reduce the magnetic field stochasticity [8][9][10][11], which makes this mechanism questionable. Different from topological change, the linear peelinglike magneto hydro dynamics (MHD) response has been found to play an important role in ELM control [12][13][14]. Nonlinear plasma response has been observed in the JET totamak [15]. The possible formation of a magnetic island near the plasma edge [16] with a toroidal Fourier mode number n ¼ 1 during ELM suppression by using n ¼ 2 RMP has been recently observed on DIII-D [17]. However, the key difference between ELM suppression and mitigation and the different roles of linear and nonlinear plasma response on ELM suppression are still not clear.In this Letter, we report the first observation of full ELM suppression using low n RMPs in slowly rotating plasmas with dominant radio-frequency (rf) wave heating, which is potentially important for the application of this method for a future fusion device. This is the first observation of full ELM suppression using RMPs in the medium plasma collisionality regime in EAST, and it expands beyond the previous observations of ELM suppression on DIII-D [1,3] and KSTAR [7]. It is found that not only the formation of a magnetic island near the edge [17] but also a critical leve...

This paper reports on disruption prediction using a shallow machine learning method known as a random forest, trained on large databases containing only plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ∼10 6 times throughout ∼10 4 discharges (disruptive and nondisruptive) over the last four years of operation. It is found that a number of parameters (e.g. P rad /P input , i , n/n G , B n=1 /B 0 ) exhibit changes in aggregate as a disruption is approached on one or more of these tokamaks. However, for each machine, the most useful parameters, as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices 'close to disruption' and 'far from disruption', it is found that the prediction algorithms differ substantially in performance among the three machines on a time slice-by-time slice basis, but have similar disruption detection rates (∼80%-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which development of a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing input signals, which may have implications for avoiding disruptive scenarios. To further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

A set of in-vessel resonant magnetic perturbation (RMP) coil has been recently installed in EAST. It can generate a range of spectrum, and there is a relatively large window for edge localized mode (ELM) control according to the vacuum field modeling of the edge magnetic island overlapping area. Observation of mitigation and suppression of ELM in slow rotating plasmas during the application of an n  =  1 RMP is presented in this paper. Strong ELM mitigation effect is observed in neutral beam injection heating plasmas. The ELM frequency increases by a factor of 5, and the crash amplitude and the particle flux are effectively reduced by a similar factor. Clear density pump-out and magnetic braking effects are observed during the application of RMP. Footprint splitting is observed during ELM mitigation and agrees well with vacuum field modelling. Strong ELM mitigation happens after a second sudden drop of plasma density, which indicates the possible effect due to field penetration of the resonant harmonics near the pedestal top, where the electron perpendicular rotation becomes flat and close to zero after the application of RMP. ELM suppression is achieved in a resonant window during the scan of the n  =  1 RMP spectrum in radio-frequency (RF) dominant heating plasmas. The best spectrum for ELM suppression is consistent with the resonant peak of RMP by taking into account of linear magnetohydrodynamics plasma response. There is no mode locking during the application of n  =  1 RMP in ELMy H-mode plasmas, although the maximal coil current is applied.

Enabled by recent advances in symmetry and electronic structure, researchers have observed signatures of unconventional threefold degeneracies in tungsten carbide, challenging a longstanding paradigm in nodal semimetals.

Intrinsic error field on EAST is measured using the ‘compass scan’ technique with different n  =  1 magnetic perturbation coil configurations in ohmically heated discharges. The intrinsic error field measured using a non-resonant dominated spectrum with even connection of the upper and lower resonant magnetic perturbation coils is of the order b r 2 , 1 / B T ≃ 10 − 5 and the toroidal phase of intrinsic error field is around 60 ° . A clear difference between the results using the two coil configurations, resonant and non-resonant dominated spectra, is observed. The ‘resonant’ and ‘non-resonant’ terminology is based on vacuum modeling. The penetration thresholds of the non-resonant dominated cases are much smaller than that of the resonant cases. The difference of penetration thresholds between the resonant and non-resonant cases is reduced by plasma response modeling using the MARS-F code.

In this study, a full convolutional neural network is trained on a large database of experimental EAST data to classify disruptive discharges and distinguish them from non-disruptive discharges. The database contains 14 diagnostic parameters from the ∼104 discharges (disruptive and non-disruptive). The test set contains 417 disruptive discharges and 999 non-disruptive discharges, which are used to evaluate the performance of the model. The results reveal that the true positive (TP) rate is ∼ 0.827, while the false positive (FP) rate is ∼0.067. This indicates that 72 disruptive discharges and 67 non-disruptive discharges are misclassified in the test set. The FPs are investigated in detail and are found to emerge due to some subtle disturbances in the signals, which lead to misjudgment of the model. Therefore, hundreds of non-disruptive discharges from training set, containing time slices of small disturbances, are artificially added into the training database for retraining the model. The same test set is used to assess the performance of the improved model. The TP rate of the improved model increases up to 0.875, while its FP rate decreases to 0.061. Overall, the proposed data-driven predicted model exhibits immense potential for application in long pulse fusion devices such as ITER.

In this study, a long short-term memory (LSTM) model is trained on a large disruption warning database to predict the disruption on EAST tokomak. To compare the performance of the proposed model with the previously reported full convolutional neural network (CNN) (Guo et al 2020 Plasma Phys. Control. Fusion 63 025008), the same data set and diagnostic signals are used. Based on the test set, the area under the receiver operating characteristic curve, i.e. the AUC value of the LSTM model is obtained as 0.87, and the true positive rate (TPR) is ~87.5%, while the false positive rate (FPR) is ~15.1%. Since the LSTM model is more sensitive to radiation fluctuations than CNN, the prediction performance of LSTM model is inferior to that of CNN model (for CNN, AUC ~0.92, TPR ~87.5%, FPR ~6.1%). However, the advance warning time of LSTM model is 14 ms earlier than that of CNN. To reduce the FPR and improve the performance of the model, more fast bolometer channels are added as the input signals of the LSTM model, including the radiation from the upper and lower edges and the plasma core. Consequently, for the same test set, the AUC value increases to 0.89, and the FPR decreases to ~9.4%, but the TPR also decreases to ~83.9%. In addition, the sensitivity of the model to radiation fluctuations caused by impurity behavior decreases significantly, and the warning time becomes 8.7 ms earlier as compared to that of the original model. Overall, it is proved that deep learning algorithms exhibit immense application potential in the disruption prediction of long-pulse fusion devices.

High pressure noble gas injection is a promising technique to mitigate the effect of disruptions in tokamaks. In this paper, results of mitigation experiments with lowZ massive gas injection (helium) on the EAST tokamak are reported. A fast valve has been developed and successfully implemented on EAST, with valve response time ⩽150 μs, capable of injecting up to 7 × 10 22 particles, corresponding to 300 times the plasma inventory.Different amounts of helium gas were injected into stable plasmas in the preliminary experiments. It is seen that a small amount of helium gas ( N He N plasma ) can not terminate a discharge, but can trigger MHD activity. Injection of 40 times the plasma inventory impurity ( N He 40 × N plasma ) can effectively radiate away part of the thermal energy and make the electron density increase rapidly. The mitigation result is that the current quench time and vertical displacement can both be reduced significantly, without resulting in significantly higher loop voltage. This also reduces the risk of runaway electron generation. As the amount of injected impurity gas increases, the gas penetration time decreases slowly and asymptotes to (∼7 ms). In addition, the impurity gas jet has also been injected into VDEs, which are more challenging to mitigate that stable plasmas.