Characterization of low-frequency inter-ELM modes of H-mode discharges at ASDEX Upgrade

Vanovac, B.; Wolfrum, E.; Hoelzl, M.; Willensdorfer, M.; Cavedon, M.; Harrer, G.; Mink, F.; Denk, S. S.; Freethy, S. J.; Dunne, M.; Mänz, P.; Luhmann, Neville C.

doi:10.1088/1741-4326/aada20

Cited by 14 publications

(16 citation statements)

References 38 publications

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“…This is a major improvement over previous methods, where only average mode velocities are manually estimated from data, as described for example in [36]. The observed velocities of about 0.5 − 1.0km/s are similar to poloidal velocities reported at ASEDX upgrade [37,36]. We note here that the reported references are relative to the lab frame.…”

Section: Time [Ms]supporting

confidence: 59%

“…By detecting individual ELM filaments on a frame-by-frame basis, the machine-learningbased detector allows us to infer the filament velocity also on a frame-by-frame basis. This is a major improvement over previous methods, where only average mode velocities are manually estimated from data, as described for example in [36]. The observed velocities of about 0.5 − 1.0km/s are similar to poloidal velocities reported at ASEDX upgrade [37,36].…”

Section: Time [Ms]supporting

confidence: 53%

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Machine-Learning enabled analysis of ELM filament dynamics in KSTAR

Jacobus¹,

Choi²,

Kube³

2022

Preprint

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The emergence and dynamics of filamentary structures associated with edge-localized modes (ELMs) inside tokamak plasmas during high-confinement mode is regularly studied using Electron Cyclotron Emission Imaging (ECEI) diagnostic systems. Such diagnostics allow us to infer electron temperature variations, often across a poloidal cross-section. Previously, detailed analysis of these filamentary dynamics and classification of the precursors to edge-localized crashes has been done manually. We present a machine-learning-based model, capable of automatically identifying the position, spatial extend, and amplitude of ELM filaments. The model is a deep convolutional neural network that has been trained and optimized on an extensive set of manually labeled ECEI data from the KSTAR tokamak. Once trained, the model achieves a 93.7% precision and allows us to robustly identify plasma filaments in unseen ECEI data. The trained model is used to characterize ELM filament dynamics in a single H-mode plasma discharge. We identify quasi-periodic oscillations of the filaments size, total heat content, and radial velocity. The detailed dynamics of these quantities appear strongly correlated with each other and appear qualitatively different during the pre-crash and ELM crash phases.

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Section: Time [Ms]supporting

confidence: 59%

Section: Time [Ms]supporting

confidence: 53%

Machine-Learning enabled analysis of ELM filament dynamics in KSTAR

Jacobus¹,

Choi²,

Kube³

2022

Preprint

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“…The edge turbulence can be effectively enhanced by the CM [15][16][17][18][19][20], which plays a key role in the reduction of ELMing energy loss. There are many kinds of CMs, such as the quasi-coherent mode (QCM) in C-Mod [21,22] dominated by resistive ballooning mode (RBM) and drift-Alfven wave (DAW), the highfrequency coherent (HFC) mode in DIII-D [23] driven by kinetic ballooning mode (KBM), the edge harmonic oscillation (EHO) in DIII-D [24] and JET [25] related to the edge pressure or pressure gradient, the weakly coherent mode (WCM) in C-Mod [26] and ASDEX Upgrade [27,28] driven by a drift-wave instability, etc.…”

Section: Introductionmentioning

confidence: 99%

The simulation of ELMs mitigation by pedestal coherent mode in EAST using BOUT++

Li¹,

Xia²,

Zou³

et al. 2022

Nucl. Fusion

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A general phenomenon that the edge localized modes (ELMs) can be effectively mitigated with the enhanced coherent modes (CMs) has been observed on EAST. For this phenomenon, the experimental statistical analysis and electromagnetic (EM) simulations have been performed. There is a threshold value of the CM intensity in the experiments, which plays a key role in ELMs mitigation. Through the ELITE and conventional BOUT++ analysis, we found that when the insignificant ELM and enhanced CM co-exist, the pedestal is located in unstable P-B region and the ELM is relatively large. The simulation results only using the experimental profiles without considering other factors cannot reproduce the no significant ELM experiment. The CM enhances the edge turbulence, which can control ELMs. Therefore, the effects of CM are considered to explain the ELM mitigation. Modifying the three-field reduced model in BOUT++, an imposed perturbation is added as the CM. The simulation results indicate that: without the CM, the ELM size belongs to the relative large ELM region; after considering the CM, the ELM is mitigated and the energy loss is reduced by about 44.5%. Analysis shows that the CM enhances the three-wave nonlinear interactions in the pedestal and reduces the phase coherence time (PCT) between the pressure and potential, which lead the perturbation to tend to be ‘multiple-mode’ coupling. The competition of free energy between the multiple modes leads to the lack of obvious filament structures and the decreased energy loss. The above reveals that there is a competitive relationship between turbulence and ELMs, and the CM-enhanced turbulence can effectively reduce ELM energy loss. In addition, through the parameter scanning, there is a threshold of the amplitude A, which is consistent with the statistical results in the experiments.

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“…This mode showing no ballooning structure is present in D, H and He discharges. In the period the high frequency mode is present also a low frequency mode ( f ≈ 4 -8 kHz) located in the upper half of the pedestal is observed and has been characterised using ECE imaging [31]. The mode sometimes has a slowing down characteristic during the ELM cycle, which may be attributed to the widening of the pedestal.…”

Section: Divertor and Edgementioning

confidence: 99%

Overview of physics studies on ASDEX Upgrade

et al. 2019

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The ASDEX Upgrade (AUG) programme, jointly run with the EUROfusion MST1 task force, continues to significantly enhance the physics base of ITER and DEMO. Here, the full tungsten wall is a key asset for extrapolating to future devices. The high overall heating power, flexible heating mix and comprehensive diagnostic set allows studies ranging from mimicking the scrape-off-layer and divertor conditions of ITER and DEMO at high density to fully non-inductive operation (q 95 = 5.5, ) at low density. Higher installed electron cyclotron resonance heating power 6 MW, new diagnostics and improved analysis techniques have further enhanced the capabilities of AUG. Stable high-density H-modes with MW m−1 with fully detached strike-points have been demonstrated. The ballooning instability close to the separatrix has been identified as a potential cause leading to the H-mode density limit and is also found to play an important role for the access to small edge-localized modes (ELMs). Density limit disruptions have been successfully avoided using a path-oriented approach to disruption handling and progress has been made in understanding the dissipation and avoidance of runaway electron beams. ELM suppression with resonant magnetic perturbations is now routinely achieved reaching transiently . This gives new insight into the field penetration physics, in particular with respect to plasma flows. Modelling agrees well with plasma response measurements and a helically localised ballooning structure observed prior to the ELM is evidence for the changed edge stability due to the magnetic perturbations. The impact of 3D perturbations on heat load patterns and fast-ion losses have been further elaborated. Progress has also been made in understanding the ELM cycle itself. Here, new fast measurements of and E r allow for inter ELM transport analysis confirming that E r is dominated by the diamagnetic term even for fast timescales. New analysis techniques allow detailed comparison of the ELM crash and are in good agreement with nonlinear MHD modelling. The observation of accelerated ions during the ELM crash can be seen as evidence for the reconnection during the ELM. As type-I ELMs (even mitigated) are likely not a viable operational regime in DEMO studies of ‘natural’ no ELM regimes have been extended. Stable I-modes up to have been characterised using -feedback. Core physics has been advanced by more detailed characterisation of the turbulence with new measurements such as the eddy tilt angle—measured for the first time—or the cross-phase angle of and fluctuations. These new data put strong constraints on gyro-kinetic turbulence modelling. In addition, carefully executed studies in different main species (H, D and He) and with different heating mixes highlight the importance of the collisional energy exchange for interpreting energy confinement. A new regime with a hollow profile now gives access to regimes mimicking aspects of burning plasma conditions and lead to nonlinear interactions of energetic particle modes despite the sub-Alfvénic beam energy. This will help to validate the fast-ion codes for predicting ITER and DEMO.

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Characterization of low-frequency inter-ELM modes of H-mode discharges at ASDEX Upgrade

Cited by 14 publications

References 38 publications

Machine-Learning enabled analysis of ELM filament dynamics in KSTAR

Machine-Learning enabled analysis of ELM filament dynamics in KSTAR

The simulation of ELMs mitigation by pedestal coherent mode in EAST using BOUT++

Overview of physics studies on ASDEX Upgrade

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