Effect of negative triangularity on tearing mode stability in tokamak plasmas

Yang, Xu; Liu, Yueqiang; Xu, Wei; Xia, Guoliang

doi:10.1088/1741-4326/acc750

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Therefore, we need to ‘avoid’ the onset of tearing instability, not suppress it after it appears. To avoid its occurrence, physics research is also underway to investigate the onset cause or seed of instability 28 – 30 . However, calculating tearing stability requires massive computational simulations based on resistive magnetohydrodynamics or gyrokinetics, which are not suitable for real-time stability prediction and control during experiments.…”

Section: Mainmentioning

confidence: 99%

Avoiding fusion plasma tearing instability with deep reinforcement learning

Seo,

Kim,

Jalalvand

et al. 2024

Nature

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For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance1–4. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators5. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D6, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

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Section: Mainmentioning

confidence: 99%

Avoiding fusion plasma tearing instability with deep reinforcement learning

Seo,

Kim,

Jalalvand

et al. 2024

Nature

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show abstract

“…Therefore, we need to "avoid" the onset of tearing instability, not suppress it after appearing. To avoid its occurrence, physics research is also underway to investigate the onset cause or seed of instability [21][22][23]. However, calculating tearing stability requires massive computational simulations based on resistive magneto-hydrodynamics or gyro-kinetics, which are not suitable for real-time stability prediction and control during experiments.…”

Section: Introductionmentioning

confidence: 99%

Avoiding tokamak tearing instability with artificial intelligence

Kolemen

Seo

Conlin

et al. 2023

Preprint

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For stable and efficient fusion energy production using a tokamak reactor, maintaining high-pressure hydrogenic plasma without plasma disruption is essential. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to maneuver high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle avoidance problem for which artificial intelligence (AI) based on reinforcement learning has recently shown remarkable performance. However, the obstacles here, the tearing instability, are difficult to forecast and highly prone to terminating plasma operations. In our recent work, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators. This dynamic model not only predicts the possible onset of tearing instability during tokamak operation but can also be used as a training environment for AI that controls actuators to avoid instabilities. In this work, we demonstrate AI control based on reinforcement learning to lower the possibility of disruptive tearing instabilities in DIII-D, the largest magnetic fusion facility in the US. The controller maintained the tearing likelihood under a given threshold, under relatively unfavorable conditions of low safety factor and low torque.

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Effect of anisotropic thermal transport on tearing mode stability in negative versus positive triangularity plasmas

Yang,

Liu,

Yuan

et al. 2024

Physics of Plasmas

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The combined effects of anisotropic thermal transport and the plasma shaping, including negative triangularity, on the n = 1 (n is the toroidal mode number) tearing mode (TM) stability are numerically investigated utilizing the MARS-F code [Liu et al., Phys. Plasmas 7, 3681–3690 (2000)]. While varying the plasma boundary triangularity, the TM stability is found to be dictated by the competing effects of the Shafranov shift induced stabilization and the bad-curvature induced destabilization. The negative triangularity shape increases the Shafranov shift (stabilizing) in the plasma core but also enlarges bad-curvature regions (destabilizing) near the plasma edge, with the net effect being largely destabilizing for the TM as compared to the positive triangularity counter-part. Large negative triangularity however can also lead to more stabilization for the plasma core-localized TM. Anisotropic thermal transport reduces the stabilizing effect on the TM associated with the favorable averaged curvature, resulting in more unstable core-localized TMs in both negative and positive triangularity plasmas. But the opposite effect can also take place for the edge-localized TM in finite-pressure plasmas with negative triangularity.

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Effect of negative triangularity on tearing mode stability in tokamak plasmas

Cited by 3 publications

References 37 publications

Avoiding fusion plasma tearing instability with deep reinforcement learning

Avoiding fusion plasma tearing instability with deep reinforcement learning

Avoiding tokamak tearing instability with artificial intelligence

Effect of anisotropic thermal transport on tearing mode stability in negative versus positive triangularity plasmas

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