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Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available athttps://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

Journal of plasma physics 88(5), 895880502 (2022). doi:10.1017/S0022377822000769, Published by Cambridge Univ. Press, London

The potential formation of multi-mega-ampere beams of relativistic "runaway" electrons (REs) during sudden terminations of tokamak plasmas poses a significant challenge to the tokamak's development as a fusion energy source. Here, we use state-of-the-art modeling of disruption magnetohydrodynamics coupled with a self-consistent evolution of RE generation and transport to show that a non-axisymmetric in-vessel coil will passively prevent RE beam formation during disruptions in the SPARC tokamak, a compact, high-field, high-current device capable of achieving a fusion gain Q > 2 in deuterium-tritium plasmas.

Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its low performance (LP) data is key to success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q_{95}, \beta_{p} and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show how in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. These results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing on existing machines baseline scenario discharges of future tokamaks to collect more relevant disruptive data.

A disruption database characterizing the current quench of disruptions with ITER-like tungsten divertor has been developed on EAST. It provides a large number of plasma parameters describing the predisruptive plasma, current quench time, eddy current, and mitigation by massive impurity injection, which shows that the current quench time strongly depends on magnetic energy and post-disruption electron temperature. Further, the energy balance and magnetic energy dissipation during the current quench phase has been well analysed. Magnetic energy is also demonstrated to be dissipated mainly by ohmic reheating and inductive coupling, and both of the two channels have great effects on current quench time. Also, massive gas injection is an efficient method to speed up the current quench and increase the fraction of impurity radiation.

SPARC is being designed to operate with a normalized beta of $\beta _N=1.0$ , a normalized density of $n_G=0.37$ and a safety factor of $q_{95}\approx 3.4$ , providing a comfortable margin to their respective disruption limits. Further, a low beta poloidal $\beta _p=0.19$ at the safety factor $q=2$ surface reduces the drive for neoclassical tearing modes, which together with a frozen-in classically stable current profile might allow access to a robustly tearing-free operating space. Although the inherent stability is expected to reduce the frequency of disruptions, the disruption loading is comparable to and in some cases higher than that of ITER. The machine is being designed to withstand the predicted unmitigated axisymmetric halo current forces up to 50 MN and similarly large loads from eddy currents forced to flow poloidally in the vacuum vessel. Runaway electron (RE) simulations using GO+CODE show high flattop-to-RE current conversions in the absence of seed losses, although NIMROD modelling predicts losses of ${\sim }80$ %; self-consistent modelling is ongoing. A passive RE mitigation coil designed to drive stochastic RE losses is being considered and COMSOL modelling predicts peak normalized fields at the plasma of order $10^{-2}$ that rises linearly with a change in the plasma current. Massive material injection is planned to reduce the disruption loading. A data-driven approach to predict an oncoming disruption and trigger mitigation is discussed.

Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges from the DIII-D tokamak with the objective of devel...

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.

This paper presents the first experimental analysis of polarized synchrotron emission from relativistic runaway electrons (REs) in a tokamak plasma. Importantly, we show that the polarization information of synchrotron radiation can be used to diagnose spatially-localized RE pitch angle distributions. Synchrotron-producing REs were generated during low density, Ohmic, diverted plasma discharges in the Alcator C-Mod tokamak. The ten-channel Motional Stark Effect diagnostic was used to measure spatial profiles of the polarization angle $\theta_{\mathrm{pol}}$ and the fraction $\mathrm{f}_{\mathrm{pol}}$ of detected light that was linearly-polarized. Spatial transitions in $\theta_{\mathrm{pol}}$ of 90$\deg$---from horizontal to vertical polarization and vice versa---are observed in experimental data and are well-explained by the gyro-motion of REs and high directionality of synchrotron radiation. Polarized synchrotron emission is modeled with the synthetic diagnostic SOFT; its output Green's (or detector response) functions reveal a critical RE pitch angle at which $\theta_{\mathrm{pol}}$ flips by 90$\deg$ and $\mathrm{f}_{\mathrm{pol}}$ is minimal. Using SOFT, we determine the dominant RE pitch angle which reproduces measured $\theta_{\mathrm{pol}}$ and $\mathrm{f}_{\mathrm{pol}}$ values. The spatiotemporal evolutions of $\theta_{\mathrm{pol}}$ and $\mathrm{f}_{\mathrm{pol}}$ are explored in detail for one C-Mod discharge. For channels viewing REs near the magnetic axis and flux surfaces $q$ = 1 and 4/3, disagreements between synthetic and experimental signals suggest that the sawtooth instability may be influencing RE dynamics. Furthermore, other sources of pitch angle scattering, not considered in this analysis, could help explain discrepancies between simulation and experiment.

Six nominally repeat neon shattered pellet injection (SPI) shutdowns of stable DIII-D Super H-modes are studied to understand the 3D properties of the radiation and impurity transport. The radiation efficiency and radiation peaking determine whether first wall melting is expected following disruption mitigation in ITER. Previous studies make use of axisymmetric approximations to infer radiation efficiencies, but validating the high efficiency required by ITER necessitates improved accuracy, and this work contributes by exploring the 3D radiation and density structures that will inform forward modeling. When the neon shatter plume produced by the SPI reaches the plasma edge, m/n = 3/1 and 2/1 island O-points are observed to align with the injection trajectory in five out of six cases, suggesting that the injected material seeds the island O-points. Field aligned neon structures emitting Ne-I line radiation drift at 1 km s−1 in the ion diamagnetic drift direction during the pre-thermal quench, tracking the motion of the m/n = 2/1 island O-point. Neon fragments penetrate to the q = 2 surface by the time of the thermal quench. Techniques to constrain the 3D emissivity are explored, and one method constrains a 3D flux tube that is consistent with the radiation data, and when mapped to the interferometers, intersects the lasers that measure the highest density. The resulting structure derived from the radiation measurements exists near the 2/1 island X-point. In five repeatable discharges, the peak of the radiation in the toroidal direction exists in a 120° toroidal sector where the injection occurs, in contrast with the outlier discharge where the toroidal peak exists in the complementary 240° toroidal sector far from the injector, and where a 50% lower density rise is observed. The n = 1 phase behavior is markedly different in the outlier discharge, suggesting a possible dependence of the radiation structure and the assimilation efficiency on MHD.

A real-time disruption predictor using random forest was developed for high-density disruptions and used in the plasma control system (PCS) of the EAST tokamak for the first time. The disruption predictor via random forest (DPRF) ran in piggyback mode and was actively exploited in dedicated experiments during the 2019–2020 experimental campaign to test its real-time predictive capabilities in oncoming high-density disruptions. During dedicated experiments, the mitigation system was triggered by a preset alarm provided by DPRF and neon gas was injected into the plasma to successfully mitigate disruption damage. DPRF’s average computing time of ∼250 μs is also an extremely relevant result, considering that the algorithm provides not only the probability of an impending disruption, i.e. the disruptivity, but also the so-called feature contributions, i.e. explainability estimates to interpret in real time the drivers of the disruptivity. DPRF was trained with a dataset of disruptions in which the electron density reached at least 80% of the Greenwald density limit, using the zero-dimensional signal routinely available to the EAST PCS. Through offline analysis, an optimal warning threshold on the DPRF disruptivity signal was found, which allows for a successful alarm rate of 92% and a false alarm rate of 9.9%. By analyzing the false alarm causes, we find that a fraction (∼15%) of the misclassifications are due to sudden transitions of plasma confinement from H- to L-mode, which often occur during high-density discharges in EAST. By analyzing DPRF feature contributions, it emerges that the loop voltage signal is that main cause of such false alarms: plasma signals more apt to characterize the confinement back-transition should be included to avoid false alarms.

A new pseudolocal tomography algorithm is developed for soft X-ray(SXR) imaging measurements of the turbulent electron temperature fluctuations (δ Te) in tokamaks and stellarators. The algorithm overcomes the constraints of limited viewing ports on the vessel wall (viewing angle) and limited number of lines of sight (LOS). This is accomplished by increasing the number of LOS locally in a region of interest. Numerical modeling demonstrates that the wavenumber spectrum of the turbulence can be reliably reconstructed, with an acceptable number of viewing angles and LOS and suitable low SNR detectors. We conclude that a SXR imaging diagnostic for measurements of turbulent δ Te using a pseudolocal reconstruction algorithm is feasible.

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H–L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only three discharges are initially labeled by the user. Lower yet reasonable performance (∼75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

In this study, a full convolutional neural network is trained on a large database of experimental EAST data to classify disruptive discharges and distinguish them from non-disruptive discharges. The database contains 14 diagnostic parameters from the ∼104 discharges (disruptive and non-disruptive). The test set contains 417 disruptive discharges and 999 non-disruptive discharges, which are used to evaluate the performance of the model. The results reveal that the true positive (TP) rate is ∼ 0.827, while the false positive (FP) rate is ∼0.067. This indicates that 72 disruptive discharges and 67 non-disruptive discharges are misclassified in the test set. The FPs are investigated in detail and are found to emerge due to some subtle disturbances in the signals, which lead to misjudgment of the model. Therefore, hundreds of non-disruptive discharges from training set, containing time slices of small disturbances, are artificially added into the training database for retraining the model. The same test set is used to assess the performance of the improved model. The TP rate of the improved model increases up to 0.875, while its FP rate decreases to 0.061. Overall, the proposed data-driven predicted model exhibits immense potential for application in long pulse fusion devices such as ITER.

Disruption database and disruption warning database of the EAST tokamak had been established by a disruption research group. The disruption database, based on Structured Query Language (SQL), comprises 41 disruption parameters, which include current quench characteristics, EFIT equilibrium characteristics, kinetic parameters, halo currents, and vertical motion. Presently most disruption databases are based on plasma experiments of non-superconducting tokamak devices. The purposes of the EAST database are to find disruption characteristics and disruption statistics to the fully superconducting tokamak EAST, to elucidate the physics underlying tokamak disruptions, to explore the influence of disruption on superconducting magnets and to extrapolate toward future burning plasma devices. In order to quantitatively assess the usefulness of various plasma parameters for predicting disruptions, a similar SQL database to Alcator C-Mod for EAST has been created by compiling values for a number of proposed disruption-relevant parameters sampled from all plasma discharges in the 2015 campaign. The detailed statistic results and analysis of two databases on the EAST tokamak are presented.

Chinese Academy of Sciences Institute of Plasma Physics and Massachusetts Institute of Technology Plasma Science and Fusion Center have been collaborating on experimental advanced superconducting tokamak (EAST). Presented in this paper are RF, disruption, and thermal stress analyses of EAST antennas. Analyzed are I port four-strap and B port $2 \times 2$ strap antennas, which are currently installed on EAST. As for RF analysis, scattering parameters are checked to make sure that the antennas are loaded, and then electric field parallel to magnetic field are checked to find out if they are below the permissible level. As for disruption analysis, mechanical stresses for both straps with the support box and Faraday screen are obtained. As for thermal stress analysis, temperature and thermal stress for a typical strap and a Faraday tube were presented. All analyses were performed by COMSOL commercial finite-element analysis software package version 5 or 5.2.

This document is the product of a stellarator community workshop, organized by the National Stellarator Coordinating Committee and referred to as Stellcon, that was held in Cambridge, Massachusetts in February 2016, hosted by MIT. The workshop was widely advertised, and was attended by 40 scientists from 12 different institutions including national labs, universities and private industry, as well as a representative from the Department of Energy. The final section of this document describes areas of community wide consensus that were developed as a result of the discussions held at that workshop. Areas where further study would be helpful to generate a consensus path forward for the US stellarator program are also discussed. The program outlined in this document is directly responsive to many of the strategic priorities of FES as articulated in “Fusion Energy Sciences: A Ten-Year Perspective (2015–2025)” [1]. The natural disruption immunity of the stellarator directly addresses “Elimination of transient events that can be deleterious to toroidal fusion plasma confinement devices” an area of critical importance for the US fusion energy sciences enterprise over the next decade. Another critical area of research “Strengthening our partnerships with international research facilities,” is being significantly advanced on the W7-X stellarator in Germany and serves as a test-bed for development of successful international collaboration on ITER. This report also outlines how materials science as it relates to plasma and fusion sciences, another critical research area, can be carried out effectively in a stellarator. Additionally, significant advances along two of the Research Directions outlined in the report; “Burning Plasma Science: Foundations—Next-generation research capabilities”, and “Burning Plasma Science: Long pulse—Sustainment of Long-Pulse Plasma Equilibria” are proposed.

Improved understanding of the dynamics of runaway electrons can be obtained by measurement and interpretation of their synchrotron radiation emission. Models for synchrotron radiation emitted by relativistic electrons are well established, but the question of how various geometric effects -- such as magnetic field inhomogeneity and camera placement -- influence the synchrotron measurements and their interpretation remains open. In this paper we address this issue by simulating synchrotron images and spectra using the new synthetic synchrotron diagnostic tool SOFT (Synchrotron-detecting Orbit Following Toolkit). We identify the key parameters influencing the synchrotron radiation spot and present scans in those parameters. Using a runaway electron distribution function obtained by Fokker-Planck simulations for parameters from an Alcator C-Mod discharge, we demonstrate that the corresponding synchrotron image is well-reproduced by SOFT simulations, and we explain how it can be understood in terms of the parameter scans. Geometric effects are shown to significantly influence the synchrotron spectrum, and we show that inherent inconsistencies in a simple emission model (i.e. not modeling detection) can lead to incorrect interpretation of the images., Comment: 24 pages, 12 figures

Chinese Academy of Sciences Institute of Plasma Physics (ASIPP) Experimental Advanced Superconducting Tokamak (EAST) has designed and built a new outer divertor with an ITER-like cooling system. As...

Novel spatial, temporal, and energetically resolved measurements of bremsstrahlung hard-x-ray (HXR) emission from runaway electron (RE) populations in tokamaks reveal nonmonotonic RE distribution functions whose properties depend on the interplay of electric field acceleration with collisional and synchrotron damping. Measurements are consistent with theoretical predictions of momentum-space attractors that accumulate runaway electrons. RE distribution functions are measured to shift to a higher energy when the synchrotron force is reduced by decreasing the toroidal magnetic field strength. Increasing the collisional damping by increasing the electron density (at a fixed magnetic and electric field) reduces the energy of the nonmonotonic feature and reduces the HXR growth rate at all energies. Higher-energy HXR growth rates extrapolate to zero at the expected threshold electric field for RE sustainment, while low-energy REs are anomalously lost. The compilation of HXR emission from different sight lines into the plasma yields energy and pitch-angle-resolved RE distributions and demonstrates increasing pitch-angle and radial gradients with energy.

Models have been built using the finite-element software COMSOL to predict fields, currents, and the resulting loads and stresses acting on the C-mod advanced outer divertor (AOD). The AOD is being designed to operate with conditions of a 2.5-MA plasma current and a toroidal field of 9 T while being held at 600 °C, and so must survive a disruption that occurs in these conditions. In addition to the loads resulting from eddy currents induced in the divertor during a disruption, the divertor will at times need to carry up to 400 kA of halo current, which crosses the toroidal field as it returns to the plasma. The loads resulting from this scenario will be very high, and we have had to develop models to predict the fields, currents, and loads as accurately as possible to ensure that the strength of the new design can withstand these loads. First, the fields and eddy currents are predicted in a COMSOL model of C-mod given two inputs. The first input is the currents for the toroidal and poloidal field coils, which come from measured data taken during a discharge. The second input is the current in the plasma, which comes from another model that solves Maxwell's equations to reconstruct the plasma as 24 current carrying filaments. The advantage of this new modeling technique is that it provides the ability to create a model based on actual measured data and to model whichever type of disruption, whether a midplane disruption or a vertical displacement event, so that the model more accurately reflects operating conditions the AOD will see. In addition to the Lorenz forces due to the induced eddy currents in the AOD, the model can also calculate the large forces due to halo currents that cross C-mod's large toroidal field. The loads due to the halo currents will be the largest loads the divertor sees. With all the loads defined, a structural model of the divertor is run to determine stresses and displacements.

A compact multi-energy soft x-ray camera has been developed for time, energy and space-resolved measurements of the soft-x-ray emissivity in magnetically confined fusion plasmas. Multi-energy soft x-ray imaging provides a unique opportunity for measuring, simultaneously, a variety of important plasma properties (Te, nZ, ΔZeff, and ne,fast). The electron temperature can be obtained by modeling the slope of the continuum radiation from ratios of the available brightness and inverted radial emissivity profiles over multiple energy ranges. Impurity density measurements are also possible using the line-emission from medium- to high-Z impurities to separate the background as well as transient levels of metal contributions. This technique should be explored also as a burning plasma diagnostic in-view of its simplicity and robustness.

A disruption prediction algorithm, named DPRF (Disruption Prediction using Random Forests), has run in real-time in the DIII-D plasma control system (PCS) over more than 900 discharges. DPRF naturally provides a probability mapping associated to its predictions, i.e. the disruptivity signal, now incorporated in the DIII-D PCS. This paper discusses disruption prediction accomplishments in terms of shot-by-shot performances, by simulating alarms on each discharge as in the PCS framework. Depending on the optimised performance metric chosen to evaluate DPRF, we find that almost all disruptive discharges are detected in average with few hundred of milliseconds of warning time, but this comes at a high cost of false alarms produced. Performances are not satisfying ITER requirements, where the success rate has to be higher than 95%, but this is not completely unexpected. DPRF is trained on many years of major disruptions occurring during the flattop phase of the plasma current on DIII-D, but without any differentiation by cause. Furthermore, we find that DPRF is affected by a relatively high fraction of false alarms occurring during the first 500 milliseconds from the flattop onset. This subtle effect, more evident on the discharges where DPRF ran in real-time, can be marginalized by taking specific precautions on the validity range of the predictions, and performances do improve. Even if presently burdened by some limitations, DPRF provides an incredible and novel advantage. Thanks to the feature contribution analysis (e.g., the identification of which signals contributed to triggering an alarm), it is possible to interpret and explain DPRF predictions. It is the first time that such interpretability features is exploited by a disruption predictor: by uncovering the disruption events' causes, a better understanding of disruption dynamics is achieved, and a clear path toward the design of disruption avoidance strategies can be provided.

This paper reports on disruption prediction using a shallow machine learning method known as a random forest, trained on large databases containing only plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ~106 times throughout ~104 discharges (disruptive and non-disruptive) over the last four years of operation. It is found that a number of parameters (e.g. , , , ) exhibit changes in aggregate as a disruption is approached on one or more of these tokamaks. However, for each machine, the most useful parameters, as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices 'close to disruption' and 'far from disruption', it is found that the prediction algorithms differ substantially in performance among the three machines on a time slice-by-time slice basis, but have similar disruption detection rates (~80%–90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which development of a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing input signals, which may have implications for avoiding disruptive scenarios. To further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

This work reports observations of a tokamak plasma that experienced a thermal quench due to a large, transient high-Z influx but avoided a current quench. This is argued to be caused by the presence of lower hybrid range of frequency (LHRF) waves that sustain a non-thermal, current-carrying electron population. In Alcator C-Mod L-mode plasmas at kA, m?3, nearly all of the current can be sustained non-inductively by injecting 700 kW of LHRF power at 4.6 GHz and . A sudden influx of a large amount of tungsten, , triggers a cooling wave that propagates at 2?3 m s?1 all the way into the core, dropping on-axis T e from 3 keV to temperatures less than measurement floor of 50?eV. An off-axis reheat begins after 100?ms, but T e profiles remain hollow for 300?350?ms after the injection. Throughout this temperature evolution, the plasma density, current and shape remain unchanged to within 10%. Following the expulsion of the tungsten, the plasma returns to its baseline conditions and the plasma ends as planned with a controlled current ramp-down. Energy balance analysis shows the LHRF power continues to be absorbed in the plasma after the thermal quench, as a significant fraction of it is needed to be consistent with radiated power measurements. Examination of current relaxation time, , and fast-electron slowing down time, , indicate the LHRF must contribute to driving current, despite the low temperatures, as the current remains nominally stationary despite ms and ms for relativistic electrons. These measurements represent an important existence proof of a possible technique for avoidance of disruptions caused by sudden, unplanned influx of impurities in the form of dust or flakes of high-Z wall material. Implications and suggestions for future experimental and modeling and simulation work are summarized.

Large, spontaneous m/n = 1/1 helical cores are predicted in tokamaks with extended regions of low- or reversed-magnetic shear profiles in a region within the q = 1 surface and an onset condition determined by constant (dp/dρ)/Bt2 along the threshold. These 3D modes occurred frequently in Alcator C-Mod during ramp-up when slow current penetration results in a reversed shear q-profile. The onset and early development of a helical core in C-Mod were simulated using a new 3D time-dependent equilibrium reconstruction, based on the ideal MHD equilibrium code VMEC. The reconstruction used the experimental density, temperature, and soft-X-ray fluctuations. The pressure profile can become hollow due to an inverted, hollow electron temperature profile caused by molybdenum radiation in the plasma core during the current ramp-up phase before the onset of sawteeth, which may also occur in ITER with tungsten. Based on modeling, it is found that a reverse shear q-profile combined with a hollow pressure profile reduces the onset condition threshold, enabling helical core formation from an otherwise axisymmetric equilibrium.Large, spontaneous m/n = 1/1 helical cores are predicted in tokamaks with extended regions of low- or reversed-magnetic shear profiles in a region within the q = 1 surface and an onset condition determined by constant (dp/dρ)/Bt2 along the threshold. These 3D modes occurred frequently in Alcator C-Mod during ramp-up when slow current penetration results in a reversed shear q-profile. The onset and early development of a helical core in C-Mod were simulated using a new 3D time-dependent equilibrium reconstruction, based on the ideal MHD equilibrium code VMEC. The reconstruction used the experimental density, temperature, and soft-X-ray fluctuations. The pressure profile can become hollow due to an inverted, hollow electron temperature profile caused by molybdenum radiation in the plasma core during the current ramp-up phase before the onset of sawteeth, which may also occur in ITER with tungsten. Based on modeling, it is found that a reverse shear q-profile combined with a hollow pressure profile reduces ...

A new outer divertor is being designed for installation on Alcator C-Mod. This divertor will be toroidally continuous such that the currents during a disruption will be driven in the toroidal direction and not cross Alcator’s large toroidal field and it eliminates leading edges. However, currents will still cross the poloidal fields, and so it is important to properly predict the poloidal fields in the area of the divertor so that we can properly predict the loads on the divertor during a disruption. To that end, an ANSYS model has been built which can predict the fields and field transients in C-Mod given two inputs, the currents for the toroidal and poloidal field coils which come from measured data taken during a discharge, and the current in the plasma, which comes from another model that solves Maxwell’s equations to reconstruct the plasma as 24 current carrying filaments. The advantage of using this method to predict fields is that it provides the ability to create a model based on actual me...

Recent progress in understanding of disruptions and in developing methods to avoid disruption damage is presented. Nearly complete mitigation of conducted heat loads has been achieved with high-Z gas jet shutdown. The resulting local radiation heat flash melting in the main chamber might be a concern in ITER, especially with beryllium walls. During the current quench, significant vessel forces can occur due to halo currents I-halo; however, these are found to fall reliably below a boundary of (halo current fraction times halo current peaking factor)

The retention of fuel in plasma facing components (PFCs) is important for the viability of fusion – both in terms of economics and safety. This study shows that a single, un-mitigated, disruption can remove >30× more fuel than that retained in a single, 1s, C-Mod discharge with molybdenum and tungsten PFCs. The fuel is recovered due to heating of the near-surface (∼100 μm) during the thermal and current quench periods of the disruption. A regression analysis of full-current disruptions in a dataset of 3200 discharges leads to a scaling of fuel recovered approximately proportional to W TH 1 × W MAG 2 where WTH and WMAG are the thermal and poloidal magnetic energy inside the vessel respectively. Scaling by surface area and disruption time scales to ITER indicate 5–10 MA plasmas with low thermal energy (e.g. during current rampdown) may be ideal for removing fuel from plasma-wetted surfaces.

In the Alcator C-Mod tokamak, relativistic runaway electron (RE) generation can occur during the flattop current phase of low density, diverted plasma discharges. Due to the high toroidal magnetic field (B-0 = 5.4 T), RE synchrotron radiation is measured by a wide-view camera in the visible wavelength range (lambda approximate to 400-900 nm). In this paper, a statistical analysis of over one thousand camera images is performed to investigate the plasma conditions under which synchrotron emission is observed in C-Mod. In addition, the spatiotemporal evolution of REs during one particular discharge is explored in detail via a thorough analysis of the distortion-corrected synchrotron images. To accurately predict RE energies, the kinetic solver CODE (Landreman et al 2014 Comput. Phys. Commun. 185 847-855) is used to evolve the electron momentum-space distribution at six locations throughout the plasma: the magnetic axis and flux surfaces q = 1, 4/3, 3/2, 2, and 3. These results, along with the experimentally-measured magnetic topology and camera geometry, are input into the synthetic diagnostic SOFT (Hoppe et al 2018 Nucl. Fusion 58 026032) to simulate synchrotron emission and detection. Interesting spatial structure near the surface q = 2 is found to coincide with the onset of a locked mode and increased MHD activity. Furthermore, the RE density profile evolution is fit by comparing experimental to synthetic images, providing important insight into RE spatiotemporal dynamics.

Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges to develop a classification algorithm for detecting disruptive phases in shots that eventually disrupt. Comparing the same methodology on different devices is crucial in order to have information on the portability of the developed algorithm and the possible extrapolation to ITER. Therefore, we use data from two very different tokamaks, DIII-D and Alcator C-Mod. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters, coming from both plasma diagnostics and equilibrium reconstructions, such as the normalized plasma internal inductance l i and the n = 1 mode amplitude normalized to the toroidal magnetic field. Using such dimensionless indicators facilitates a more direct comparison between DIII-D and C-Mod. We then choose a shallow Machine Learning technique, called Random Forests, to explore the databases available for the two devices. We show results from the classification task, where we introduce a time dependency through the definition of class labels on the basis of the elapsed time before the disruption (i.e. 'far from a disruption' and 'close to a disruption'). The performances of the different Random Forest classifiers are discussed in terms of several metrics, by showing the number of successfully detected samples, as well as the misclassifications. The overall model accuracies are above 97% when identifying a 'far from disruption' and a 'disruptive' phase for disrupted discharges. Nevertheless, the Forests are intrinsically different in their capability of predicting a disruptive behavior, with C-Mod predictions comparable to random guesses. Indeed, we show that C-Mod recall index, i.e. the sensitivity to a disruptive behavior, is as low as 0.47, while DIII-D recall is ~0.72. The portability of the developed algorithm is also tested across the two devices, by using DIII-D data for training the forests and C-Mod for testing and vice versa.

High pressure noble gas injection is a promising technique to mitigate the effect of disruptions in tokamaks. In this paper, results of mitigation experiments with low-Z massive gas injection (helium) on the EAST tokamak are reported. A fast valve has been developed and successfully implemented on EAST, with valve response time ≤150 μs, capable of injecting up to particles, corresponding to 300 times the plasma inventory. Different amounts of helium gas were injected into stable plasmas in the preliminary experiments. It is seen that a small amount of helium gas () can not terminate a discharge, but can trigger MHD activity. Injection of 40 times the plasma inventory impurity () can effectively radiate away part of the thermal energy and make the electron density increase rapidly. The mitigation result is that the current quench time and vertical displacement can both be reduced significantly, without resulting in significantly higher loop voltage. This also reduces the risk of runaway electron generation. As the amount of injected impurity gas increases, the gas penetration time decreases slowly and asymptotes to (~7 ms). In addition, the impurity gas jet has also been injected into VDEs, which are more challenging to mitigate that stable plasmas.

High-pressure noble gas jet injection is a mitigation technique which potentially satisfies the requirements of fast response time and reliability, without degrading subsequent discharges. Previously reported gas jet experiments on DIII-D showed good success at reducing deleterious disruption effects. In this paper, results of recent gas jet disruption mitigation experiments on Alcator C-Mod and DIII-D are reported. Jointly, these experiments have greatly improved the understanding of gas jet dynamics and the processes involved in mitigating disruption effects. In both machines, the sequence of events following gas injection is observed to be quite similar: the jet neutrals stop near the plasma edge, the edge temperature collapses and large MHD modes are quickly destabilized, mixing the hot plasma core with the edge impurity ions and radiating away the plasma thermal energy. High radiated power fractions are achieved, thus reducing the conducted heat loads to the chamber walls and divertor. A significant (2 × or more) reduction in halo current is also observed. Runaway electron generation is small or absent. These similar results in two quite different tokamaks are encouraging for the applicability of this disruption mitigation technique to ITER.

High-pressure gas-jet injection disruption mitigation is investigated on Alcator C-Mod. The experimental results are encouraging for ITER. Gas-jets delivers reliable and substantial disruption mitigation of thermal loads and halo currents at the ITER field and pressure. The gas injection is compatible with a metallic wall. The gas-jets provide sufficient radiative energy dissipation on a timescale even faster than required for ITER. Runaway electrons are suppressed, however a better understanding of this suppression is still required for ITER. Present 0-D calculations of the gas delivery and global plasma response match important experimental data such as overall quench time and the current quench L/R time. Frictional dissipation may be important for gas delivery down the long tubes that will probably be required for ITER. However deep gas and/or impurity penetration is not required for mitigation, relieving technical requirements for the gas pressure in ITER. Initial studies with the non-ideal MHD simulation NIMROD show that radiation-induced MHD plays a critical role in thermal quench particle and energy dynamics. The application of a newly available version of NIMROD, with more complete atomic physics, shows promise in matching the experimental trends and could be an invaluable tool for increasing our confidence in designing efficient disruption mitigation for ITER.

Energetic minority protons with ~100 keV effective temperature are routinely created in Alcator C-Mod plasmas with the application of ion-cyclotron-range-of-frequency (ICRF) heating. A multichannel compact neutral particle analyzer (CNPA) is used to make measurements of these distributions in Alcator C-Mod's unique and reactor-relevant operating space via active and passive charge-exchange techniques. A radially injected 50 keV diagnostic hydrogen neutral beam is used for active analysis. Using a detailed model that accounts for beam, halo and impurity electron donors, core proton temperatures of ~30–120 keV are directly measured for the first time in lower density (ne0 ~ (0.8–1.5) × 1020 m−3) Alcator C-Mod plasmas with up to only ~0.5 MW of ICRF power. The model found that the minority proton temperatures are peaked spatially away from r/a = 0, even for an on-axis resonance. Additionally, noticeable phase-space anisotropy is seen as expected for ICRF heating. The measured effective temperatures also scale approximately with the Stix parameter. The CNPA temperature measurements are compared with several leading simulation packages such as the TORIC/FPPRF and AORSA/CQL3D full-wave/Fokker–Planck (FW/FP) solvers. Preliminary comparisons with the AORSA/CQL3D code which include results from a new synthetic diagnostic show good agreement and demonstrate that accurate tracking of the minority distribution during iterations of the FW and FP solvers is required to simulate Alcator C-Mod's energetic minority populations with accuracy. Physically, poor wave focusing and preferential heating of trapped energetic protons are found to move the fast proton temperature profiles off-axis. These FW/FP analyses represent the first comparison between predictions of these detailed codes and core minority tail experimental measurements on Alcator C-Mod.

This paper reviews the physics and technology of wave-particle-interaction experiments in the ion cyclotron range of frequencies (ICRF) and the lower hybrid (LH) range of frequencies (LHRF) on the ...

An overview of the diagnostics installed on the Alcator C-Mod tokamak is presented. Approximately 25 diagnostic systems are being operated on C-Mod. The compact design of the machine and the cryostat enclosing the vacuum vessel and magnetic field coils make access challenging. Diagnostics are used to study four focus areas: transport, plasma boundary, waves, and macrostability. There is significant overlap between these topics, and they all contribute toward the burning plasma and advanced tokamak thrusts. Several advanced and novel diagnostics contribute to the investigation of C-Mod plasmas, e.g., electron cyclotron emission, phase-contrast imaging, gas puff imaging, probe measurements, and active magnetohydrodynamic antennas.

Global and local transport experiments in ohmic, L-mode and H-mode regimes on the Alcator C-Mod tokamak are summarized. For ohmic plasmas, earlier results derived for energy confinement scaling in ...

We describe some of the engineering solutions required to produce a diverted tokamak capable of operation with a toroidal field of 8 T and plasma currents of up to 2 MA. Some design details of the toroidal field magnet, the ohmic heating magnet, the metal plasma-facing components, the rf heating and current drive systems, and the power and liquid nitrogen cooling systems are discussed. Vacuum, vessel bake, boronization, and wall-cleaning systems are also discussed. Finally, disruption research results from Alcator C-Mod are presented.

H-mode research on Alcator C-Mod is described, with a focus on the edge transport barrier (ETB). ETB pedestals are characterized using several diagnostics, leading to a thorough description of prof...

The design, calibration, and installation of disruption halo current sensors for the Experimental Advanced Superconducting Tokamak are described in this article. All the sensors are Rogowski coils that surround conducting structures, and all the signals are analog integrated. Coils with two different cross-section sizes have been fabricated, and their mutual inductances are calibrated. Sensors have been installed to measure halo currents in several different parts of both the upper divertor (tungsten) and lower divertor (graphite) at several toroidal locations. Initial measurements from disruptions show that the halo current diagnostics are working well.

CAS IPP and MIT PSFC are collaborating on Experimental Advanced Superconducting Tokamak (EAST), the first tokamak with superconducting toroidal and poloidal magnets and a testbed for technologies proposed for the ITER project. Presented in this paper are RF, disruption and thermal analyses of EAST antennas. All were performed by COMSOL commercial software package Version 5. Analyzed are the I port 4 strap and B port 2 × 2 strap antennas, which are currently installed on EAST.

The Advanced Divertor eXperiment (ADX) [1] is a compact, high field (> 6.5 tesla), high power density tokamak, proposed by the Plasma Science and Fusion Center (PSFC) and collaborators, designed specifically to develop and test advanced divertor configurations that can accommodate the extreme plasma heat exhaust densities anticipated in next-step plasma fusion devices. ADX will also develop and test advanced technologies for Lower Hybrid Current Drive (LHCD) and Ion Cyclotron Range of Frequency (ICRF) heating, including the ability to deploy RF launch structures on the high-field-side for the first time. This potential game-changing innovation is expected to provide efficient heating and high efficiency, off-axis current drive while minimizing impurity production via plasma-launcher interactions [2, 3]. This combination of advanced divertors and innovative RF systems places unique demands on ADX's vacuum vessel (VV), which must have an integrated design that can incorporate the required poloidal field coil set and embedded infrastructure for RF feeds to the high-field-side vacuum vessel wall. Much of the ADX poloidal field (PF) coil system, toroidal field (TF) magnet and structural design is based on the successes of the C-Mod tokamak program, with the capability to operate at up to 8 tesla on axis - a rigid vacuum vessel providing structural support for the PF coils, and a liquid nitrogen cooled, demountable TF magnet. However, five separate axisymmetric structural shells and one inner cylinder are bolted together to form the VV in a novel configuration for ADX. This unique design accommodates the poloidal coil configurations required to produce the proposed advanced divertor shapes while at the same time providing flexibility for implementing alternative coil configurations. This paper describes ADX's vacuum vessel, coil system design and in-vessel components.