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Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they require extensive discharge data for model training. However, future tokamaks will begin operations without any prior data, making it difficult to train data-driven disruption predictors and select appropriate hyperparameters during the early operation period. In this period disruption prediction also aims to support safe exploration of operation range and accumulate necessary data to develop advanced prediction models. Thus, predictors must adapt to evolving plasma states during this exploration phase. To address these challenges, this study further develops the enhanced convolutional autoencoder anomaly detection (E-CAAD) predictor and proposes a cross-tokamak adaptive transfer method based on E-CAAD. By training the E-CAAD model on data from existing devices, the predictor can effectively distinguish between disruption precursor and non-disruption samples in new device, enabling disruption prediction from the first shot on the new device. Additionally, adaptive learning from scratch and alarm threshold adaptive adjustment strategies are proposed to enable model automatically adapt to changes in the discharge scenario. The adaptive learning strategy enables the predictor to fully use scarce data during the early operation of the new device while rapidly adapting to changes in the discharge scenario. The threshold adaptive adjustment strategy addresses the challenge of selecting alarm thresholds on new devices where the validation set is lacking, ensuring that the alarm thresholds adapt to changes in the discharge scenario. Finally, the experiment transferring the model from J-TEXT to EAST exhibit that this method enables disruption prediction from the first shot on EAST, allowing the predictor to adapt to changes in the discharge scenario and maintain high prediction performance.

Unmitigated disruptions pose a much more serious threat when large-scale tokamaks are operating in the high performance regime. Machine learning based disruption predictors can exhibit impressive performance. However, their effectiveness is based on a substantial amount of training data. In future reactors, obtaining a substantial amount of disruption data in high performance regimes without risking damage to the machine is highly improbable. Using machine learning to develop disruption predictors on data from the low performance regime and transfer them to the high performance regime is an effective solution for a large reactor-sized tokamak like ITER and beyond. In this study, a number of models are trained using different subsets of data from the HL-2A tokamak experiment. A SHapley Additive exPlanations (SHAP) analysis is executed on the models, revealing that there are different, even contradicting, patterns between different performance regimes. Thus, simply mixing data among different performance regimes will not yield optimal results. Based on this analysis, we propose an instance-based transfer learning technique which trains the model using a dataset generated with an optimized strategy. The strategy involves instance and feature selection based on the physics behind differences in high- and low-performance discharges, as revealed by SHAP model analysis. The TrAdaBoost technique significantly improved the model performance from 0.78 BA (balanced accuracy) to 0.86 BA with a few high-performance operation data.

The magnetic properties and the growth mode of ultrathin Co films deposited on 1 ML (monolayer) Ni/Pt(111) at room temperature are investigated by using magneto-optical Kerr effect, low-energy electron diffraction and Auger electron spectroscopy. The Pt atoms act as domain wall pinning sites and the coercivity of the Ni-Co-Pt system can be adjusted by changing the annealing temperature because of the variety in concentrations of the alloy formation.

Locked modes (LMs) will be one of the major causes of disruptions in the ITER tokamak. Disruption mitigation systems (DMSs), such as massive gas injection (MGI) or shattered pellet injection (SPI), are expected to be deployed in pre-disruption discharges with large pre-existing locked modes. A series of target plasmas with an m/n  =  2/1 locked mode induced by resonant magnetic perturbation (RMP) penetration was terminated by an MGI on the J-TEXT tokamak. The penetration of the injected impurities during the process was diagnosed using a fast frame visible camera and a multi-channel polarimeter-interferometer (POLARIS) in combination. The electron temperature evolution during thermal quench (TQ) is also shown in detail. It is found that both the phase and width of the 2/1 mode have an effect on MGI shutdown dynamics. When the mode is larger than the critical width, the penetration depth and assimilation of impurities can be enhanced during pre-TQ, leading to a faster quenching process if the relative phase between the O-point of the 2/1 mode and the MGI valve is   +90°. Conversely, the penetration depth and assimilation of impurities are suppressed, leading to a slower TQ when the relative phase is   −90°. The toroidal radiation asymmetry is worse with the locked mode. The results suggest that the 3D effect between the injected impurities and the 2/1 locked mode is important during the disruption mitigation process. [ABSTRACT FROM AUTHOR]