Cristina Rea

In preparation for ARC, supplementary offline tools are being developed alongside a real-time warning system in order to reduce its disruptivity. This paper introduces Off-Normal SIMulations (ONSIMs) to the MOSAIC pulse-simulating framework with that intent in mind. These modules probe simulated equilibria with a library of distinct perturbations, and can characterize disruptivity based on the scenario’s resiliency to them. This paper addresses the development of a module for simulating radiative collapses, with a specific focus on validating for the first time the power balance dynamics characteristic of these events. The simulations are performed using the TORAX transport simulator and are benchmarked against radiative collapse precursors on Alcator C-Mod. It is found that transport models that include sufficient turbulence effects, like a surrogate of the QuaLiKiz (Bourdelle et al 2015 Plasma Phys. Control. Fusion 58 014036; Citrin et al 2017 Plasma Phys. Control. Fusion 59 124005) transport model, are capable of reproducing the relevant radiative collapse dynamics for an L-mode discharge. It is also found that these dynamics are relatively insensitive to the shape of the collapse-inciting impurity profile, which bodes well for the robustness when probing various equilibria. The benchmarking reported here is part of a larger ongoing validation effort, which lays the groundwork for radiative collapse ONSIMs. Future work will be done to translate validations like this to a general implementation of ONSIMs to SPARC and ARC scenarios.

Commonwealth Fusion Systems (CFS) plans to operate a tokamak power plant called ARC in the early 2030s. Tokamak plasmas have stability limits that, if crossed, lead to a rapid termination of the plasma, referred to as a disruption. Disruptions pose a melt risk to the first wall resulting from thermal and non-thermal particle heat fluxes, and an electromagnetic loading risk on all metal components within the equilibrium coils. A comprehensive set of models is used herein to provide an assessment of both mitigated and unmitigated ARC disruption loads. A preliminary massive gas injection system is baselined and a runaway electron mitigation coil option is proposed to close possible gaps in the baseline. It is predicted that all ARC disruption loads are within a factor of 2 of the disruption loads in SPARC, a tokamak presently under construction by CFS, and therefore SPARC provides an opportunity to calibrate models, test solutions and inform the design of ARC. The goal for ARC is disruption-free operation, however, the pragmatic design target is to withstand one mitigated disruption per day, and to restart the plasma following mitigation in tens of seconds without interrupting the power output. Unmitigated disruptions must be rare, and experience with unmitigated disruption impacts in SPARC will better define what rare means. The implications of this strategy for plasma disruptivity and disruption prediction are discussed, and operating the ARC scenario on SPARC is expected to refine the ARC final design and operational plan.

Over the last decade, tearing research progress has been accelerated thanks to the demonstrated efficacy of machine learning (ML) techniques—specifically for predicting and controlling tearing modes (TMs) in tokamak plasmas. These approaches leverage extensive experimental data and sophisticated ML algorithms to enhance plasma stability and performance. Additionally, the threat of TMs degrading plasma confinement and leading to disruptions has driven for years research under the International Tokamak Physics Activity, aimed at designing a successful trigger for the ITER's disruption mitigation system. In this review, we systematically explain the challenge behind tearing onset prediction, and summarize the latest achievements of ML in the tokamak tearing literature. We focus on the advancements that large statistical data-analyses and ML have afforded in interpreting the physics governing tearing onset, predicting onset, and developing algorithms to avoid and suppress TMs. Open challenges are discussed, including the need for reliable and reproducible TM prediction and control, using a reduced set of pilot-plant appropriate diagnostics, and the identification of passively tearing-stable power plant scenarios.

The neoclassical tearing mode (NTM) stability metric—minimum marginally stable island width w m ∗ —was compared across 14651 inductive high-field tokamak pilot plant equilibria. Larger devices with reduced elongation and/or increased minor radius demonstrated an order-of-magnitude increase in w m ∗ , primarily due to a reduction in bootstrap drive. This work is part of an ongoing effort to ensure passive NTM-stability in the ARC tokamak, in which the technology to achieve active tearing-suppression with localised electron cyclotron current drive does not yet exist. The equilibrium scenarios in the database were Monte Carlo generated and normalised to the same > 400MW fusion power, minimum pressure scenario at a range of plasma currents, before tearing analysis using the modified Rutherford equation was applied for all resonant poloidal and toroidal m , n modes up to n = 4. Single-helicity toroidal Δ ′ calculations in resistive DCON set the minimum marginally stable island width, and a simple modal scaling proportional to − m 2 n − 1 was identified for high- m Δ ′ values. The dominant correlates of w m ∗ and Δ ′ across the database were analysed using interpretable machine learning techniques.

The SPARC tokamak is a compact, high-field, and high-current device that will rely on disruption mitigation and prevention strategies to address the risks associated with its high stored energy densities. In order to implement these strategies, a system will be in place to both predict the onset of disruptive instabilities and inform their avoidance. This paper details the design of an off-normal warning system that accommodates many of the needs that a similar system on SPARC may need for triggering disruption alarms. One of the more novel accommodations is the introduction of a control-oriented metric for optimizing the sensitivity of warning alarms, the ‘damage’ metric, which acts to minimize the accumulated damage to the machine associated with disruptions. Another includes the extension of the points-based stability model (Gerhardt et al 2013 Nucl. Fusion 53 063021) to accommodate ease of use in day-to-day operation, improving both its interpretability and tunability through reduced input-space complexity and flexible point-assignment mappings. The benefits of these designs are demonstrated through the development and testing of alarms for detecting radiative collapses and vertical displacement events on Alcator C-Mod. Their compatibility with triggering the disruption mitigation system based on their distribution of warning times is investigated, and found to be limited by the durations of the events they are designed to detect on C-Mod. These detectors may be applicable to SPARC as well, and extrapolations of their expected performances to a SPARC-like environment are made.

In this chapter, we review the progress in MHD stability, disruptions and control in magnetic fusion research that has occurred over the past (more than) one and a half decades since the publication by Hender et al in 2007 on the same topic as part of the update of ITER Physics Basis. During this period, remarkable progress has been achieved in the understanding of the basic physics and overall control of MHD instabilities through a wide spectrum of dedicated experiments, theory and modeling. The sawtooth activities are probably today one of the best understood of MHD events and very robust control schemes have been developed for reliable operation of tokamaks through core heating. Similarly, significant improvements have been achieved in understanding and control of neoclassical tearing modes, resistive wall modes or locked modes and their control through ECCD or error field control. The field of disruption prediction through application of artificial intelligence, machine learning or deep learning methods, which had already started at the time of the 2007 review, has progressed significantly due to general progress in these fields and application of newer, more sophisticated algorithms. However, although remarkable progress has been achieved in the field of Disruptions, their understanding, prediction, possible avoidance and mitigation still remain probably the most active fields of R&D globally in this field. This is especially because reactor grade machines like ITER and DEMO will be much less tolerant in respect of disruptions and runaway currents, and their occurrences must be either avoided altogether or minimized to an acceptable value without causing any significant hindrance to robust machine operations. This review is intended to present a broad spectrum of the R&D that has occurred in this field in support of ITER, which will also be of immense significance for all future machines, especially reactors like DEMO.

The ‘density limit’ is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot plant concepts must operate near or above the Greenwald limit to achieve their objectives. Evidence has also grown that the Greenwald scaling—in its remarkable simplicity—may not capture the full complexity of the density limit. In this study, we assemble a multi-machine database to quantify the effectiveness of the Greenwald limit as a predictor of the L-mode density limit and compare it with data-driven approaches. We find that a boundary in the plasma edge involving dimensionless collisionality and pressure, ν ∗ , edge limit = 3.5 β T , edge − 0.40 , achieves significantly higher accuracy (false positive rate (FPR) of 2.3% at a true positive rate (TPR) of 95%) of predicting density limit disruptions than the Greenwald limit (FPR of 13.4% at a TPR of 95%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. This boundary can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.

Analyses of the DIII-D ITER Baseline Scenario database support that the disruptive m,n=2,1 magnetic islands are pressure gradient driven, non-linear instabilities seeded in a sequence of stochastic transient magnetic perturbations, and that the current profile relaxation does not affect the m,n=2,1 island onset rate. At low torque, these Neoclassical Tearing Modes are most commonly seeded by non-linear 3-wave coupling when the differential rotation between the q=1 & q=2 rational surfaces approaches zero. Lack of statistically significant difference between the current profiles of stable and unstable states, as well as lack of correlation between the tearing mode onset rate and the current profile relaxation both reject causality between the current profile evolution and the 2,1 magnetic island onsets in these plasmas. These support that preserving the differential rotation between the q=1 and q=2 rational surfaces is key to long pulse stable operation in the plasma scenario planned for ITER, while optimization of the current profile within the explored parameter space may lead to much weaker improvements than sustaining the differential rotation.