Path-oriented early reaction to approaching disruptions in ASDEX Upgrade and TCV in view of the future needs for ITER and DEMO

Abstract

Routine reaction to approaching disruptions in tokamaks is currently largely limited to machine protection by mitigating an ongoing disruption, which remains a basic requirement for ITER and DEMO [1]. Nevertheless, a mitigated disruption still generates stress to device. Additionally, in future fusion devices, high-performance discharge time itself will be very valuable. Instead of reacting only on generic features, occurring shortly before the disruption, the ultimate goal is to actively avoid approaching disruptions at an early stage, sustain the discharges whenever possible and restrict mitigated disruptions to major failures. Knowledge of the most relevant root causes and the corresponding chain of events leading to disruption, the disruption path, is a prerequisite. For each disruption path, physics-based sensors and adequate actuators must be defined and their limitations considered. Early reaction facilitates the efficiency of the actuators and enhances the probability of a full recovery. Thus, sensors that detect potential disruptions in time are to be identified. Once the entrance into a disruption path is detected, we propose a hierarchy of actions consisting of (I) recovery of the discharge to full performance or at least continuation with a less disruption-prone backup scenario, (II) complete avoidance of the disruption to sustain the discharge or at least delay it for a controlled termination and, (III), only as last resort, a disruption mitigation. Based on the understanding of disruption paths, a hierarchical and path-specific handling strategy must be developed. Such schemes, testable in present devices, could serve as guideline for ITER and DEMO operation. For some disruption paths, experiments have been performed at ASDEX Upgrade and TCV. Disruptions were provoked in TCV by impurity injection into ELMy H-mode discharges and in ASDEX Upgrade by forcing a density limit in H-mode discharges. The new approach proposed in this paper is discussed for these cases. For the H-mode density limit sensors used so far react too late. Thus a plasma-state boundary is proposed, that can serve as an adequate early sensor for avoiding density limit disruptions in H-modes and for recovery to full performance.

Nonlocal transport in toroidal plasma devices

“…Clear resonance islands are seen for each harmonic, as well as a nonlinearly generated resonance at m/n = 5/2. The position of the m/n = 2 island agrees broadly with that measured in similar discharges [40] (note that Orbit normalization and the fact that ψ p (0) = 0, coincides with the usual tokamak ρ).…”

First-principles density limit scaling in tokamaks based on edge turbulent transport and implications for ITER

“…Experiments indicate that the cooling of tokamak boundary plasma together with the subsequent increase of the plasma edge collisionality is a strong factor limiting the maximum achievable density in tokamaks, providing a strong link between the edge collisionality and density limit [2][3][4][5][6][7]. A Multifaceted Asymmetric Radiation From the Edge (MARFE) is often observed when approaching the density limit [8][9][10][11][12][13], supporting the hypothesis of a strong edge physics role. Experiments also demonstrated that the Greenwald limit can be exceeded through pellet injection that mainly increases the core, and only weakly, the edge density [14].…”

Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

“…From this perspective, several machine learning methods have been developed. They cover a wide range of techniques from Fuzzy logic classifiers to neural networks [4][5][6][7][8][9][10][11][12][13][14][15]. In recent years, a new predictor, Advanced Predictor Of DISruptions (APODIS), based on Support Vector Machines (SVMs), has been deployed in the JET real-time network and has performed very satisfactorily in terms of both success rate and false alarms, in a long series of campaigns without any need for retraining [7].…”