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.

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, nu & lowast;,edgelimit=3.5 beta 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.

PIT VIPER cables are rare-Earth barium copper oxide (REBCO)-based high-temperature superconducting (HTS) cables developed at the Massachusetts Institute of Technology and Commonwealth Fusion Systems. The cable design consists of a twisted copper former with grooves filled with stacks of REBCO tape, enclosed in a structural jacket. The assembly is then filled with solder in a vacuum pressure impregnation (VPI) process. One side effect of the VPI process, however, is potential damage to the superconductor that reduces its critical current and critical exponent. Damage may result from two mechanisms: prolonged exposure to elevated temperatures, and physical erosion of the copper stabilizer layer of the HTS tape, which leaves parts of the REBCO layer unprotected. In this experiment, the effect of extended time scales ($>2$ h) of flowing tin-lead-based solder exposure on HTS in PIT VIPER cables was tested, exploring for the first time exposure to flowing solder at a time scale that is particularly relevant to large-scale magnet manufacturing. During this study, two experimental samples were manufactured for electrical testing: one 2.5-m-long straight cable and one 20-m-long coiled cable. Each cable was exposed to molten tin-lead solder for 2.5 h during the VPI process, and then, electrically tested in a liquid nitrogen bath. Critical current, $n$-value, and resistance were measured. Critical current is compared to modeled values determined from characterization of the tape used, and degradation is assessed from this comparison. The measured critical current, when tested in a liquid nitrogen bath and under self-field, was uniform within reasonable experimental error. This result derisks solder degradation for the manufacturing of SPARC cable magnets.

The SPARC tokamak is a high-field, B-t0 similar to 12 T, medium-sized, R-0 = 1.85 m, tokamak that is presently under construction in Devens, MA, led by Commonwealth Fusion Systems. It will be used to de-risk the high-field tokamak path to a fusion power plant and demonstrate the commercial viability of fusion energy. SPARC's first campaign plan is to achieve Q(fus) > 1 using an ICRF-heated, <10 MW, high current, I-p similar to 8.5 MA, L-mode fueled by D-T gas injection, and its second campaign will investigate H-mode operations in D-D. To facilitate plasma control and scientific learning, a targeted set of similar to 50 plasma diagnostics are being designed and built for operation during these campaigns. While nearly all diagnostics are based on established techniques, the pace of deployment, relative to the first plasma, and the harshness of the thermal, electromagnetic, and radiation environment are unprecedented for medium-sized tokamaks. An overview of the SPARC diagnostic set is given, providing context to further details communicated by the SPARC team in companion publications that are system-specific. The system engineering philosophy for SPARC diagnostics is outlined, and the design and engineering verification process for components inside and outside the primary vacuum boundary are described. Diagnostics are mounted directly to the vacuum vessel as well as housed within a series of eight midplane and 24 off-midplane replaceable port plugs. With limited exceptions, signal conditioning, digitization electronics and cameras as well as lasers and microwave sources are localized to a series of five Diagnostic Lab spaces, totaling similar to 350 m(2), located >15 m from the center of the tokamak, on the other side of a 2.4 m concrete shielding wall. A series of 31 large-scale penetrations have been included in the SPARC Tokamak Hall to facilitate integration of early campaign diagnostics and to provide upgradability.

Since the last IAEA-FEC in 2021, significant progress on the development of long pulse steady state scenario and its related key physics and technologies have been achieved, including the reproducible 403 s long-pulse steady-state H-mode plasma with pure radio frequency (RF) power heating. A thousand-second time scale (similar to 1056 s) fully non-inductive plasma with high injected energy up to 1.73 GJ has also been achieved. The EAST operational regime of high beta(P) has been significantly extended (H-98y2 > 1.3, beta(P) similar to 4.0, beta(N) similar to 2.4 and n(e)/n(GW) similar to 1.0) using RF and neutral beam injection (NBI). The full edge localized mode suppression using the n = 4 resonant magnetic perturbations has been achieved in ITER-like standard type-I ELMy H-mode plasmas with q(95) approximate to 3.1 on EAST, extrapolating favorably to the ITER baseline scenario. The sustained large ELM control and stable partial detachment have been achieved with Ne seeding. The underlying physics of plasma-beta effect for error field penetration, where toroidal effect dominates, is disclosed by comparing the results in cylindrical theory and MARS-Q simulation in EAST. Breakdown and plasma initiation at low toroidal electric fields (<0.3 V m(-1)) with EC pre-ionization is developed. A beneficial role on the lower hybrid wave injection to control the tungsten concentration in the NBI discharge is observed for the first time in EAST suggesting a potential way toward steady-state H-mode NBI operation.

The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with closing the core-edge integration knowledge gap being the overarching mission. New experimental validation of high-fidelity, multi-channel, non-linear gyrokinetic turbulent transport models for ITER provides strong confidence it will achieve Q 10 operation. Experiments identify options for easing H-mode access in hydrogen, and give new insight into the isotopic dependence of transport and confinement. Analysis of 2,1 islands in unoptimized low-torque IBS demonstration discharges suggests their onset time occurs randomly in the constant beta phase, most often triggered by non-linear 3-wave coupling, thus identifying an NTM seeding mechanism to avoid. Pure deuterium SPI for disruption mitigation is shown to provide favorable slow cooling, but poor core assimilation, suggesting paths for improved SPI on ITER. At the boundary, measured neutral density and ionization source fluxes are strongly poloidally asymmetric, implying a 2D treatment is needed to model pedestal fuelling. Detailed measurements of pedestal and SOL quantities and impurity charge state radiation in detached divertors has validated edge fluid modelling and new self-consistent 'pedestal-to-divertor' integrated modeling that can be used to optimize reactors. New feedback adaptive ELM control minimizes confinement reduction, and RMP ELM suppression with sustained high core performance was obtained for the first time with the outer strike point in a W-coated, compact and unpumped small-angle slot divertor. Advances have been made in integrated operational scenarios for ITER and power plants. Wide pedestal intrinsically ELM-free QH-modes are produced with more reactor-relevant conditions, Low torque IBS with W-equivalent radiators can exhibit predator-prey oscillations in T-e and radiation which need control. High-beta(P) scenarios with q(min) > 2, q(95)-7.9, beta(N) > 4, beta(T)-3.3% and H-98y2 > 1.5 are sustained with high density ((n) over bar = 7E19 m(-3), f(G)-1) for 6 tau(E), improving confidence in steady-state tokamak reactors. Diverted NT plasmas achieve high core performance with a non-ELMing edge, offering a possible highly attractive core-edge integration solution for reactors.

Tokamaks are often considered to be a leading candidate for near-term, cost-effective fusion energy, but these devices are susceptible to sudden loss of confinement events called disruptions. The threat of disruptions has garnered serious attention in research for the next generation of burning plasma experiments, such as ITER, but has received little treatment in economic studies of magnetic fusion energy. In this paper, we present a model for quantifying the effect of disruptions on the cost of electricity produced by a tokamak power plant (TPP). We outline the various ways disruptions increase costs and decrease revenues, introduce metrics to quantify these effects, and add them to a levelized cost of electricity (LCOE) model. Additionally, we identify several rate-limiting repair steps and introduce a classification system of disruption types based on the time to return to operations. We demonstrate how the LCOE model can be used to find the cost of electricity and the requirements for disruption handling of a TPP, and we further highlight where future research can have a strong impact in neutralizing the "showstopping" potential of disruptions.

The SPARC Toroidal Field Model Coil (TFMC) Program was a three-year effort between 2018 and 2021 that developed novel Rare Earth Yttrium Barium Copper Oxide (REBCO) superconductor technologies and then successfully utilized these technologies to design, build, and test a first-in-class, high-field (~20 T), representative-scale (~3 m) superconducting toroidal field coil. With the principal objective of demonstrating mature, large-scale, REBCO magnets, the project was executed jointly by the MIT Plasma Science and Fusion Center (PSFC) and Commonwealth Fusion Systems (CFS). The TFMC achieved its programmatic goal of experimentally demonstrating a large-scale high-field REBCO magnet, achieving 20.1 T peak field-on-conductor with 40.5 kA of terminal current, 815 kN/m of Lorentz loading on the REBCO stacks, and almost 1 GPa of mechanical stress accommodated by the structural case. Fifteen internal demountable pancake-to-pancake joints operated in the 0.5 to 2.0 nOhm range at 20 K and in magnetic fields up to 12 T. The DC and AC electromagnetic performance of the magnet, predicted by new advances in high-fidelity computational models, was confirmed in two test campaigns while the massively parallel, single-pass, pressure-vessel style coolant scheme capable of large heat removal was validated. The REBCO current lead and feeder system was experimentally qualified up to 50 kA, and the crycooler based cryogenic system provided 600 W of cooling power at 20 K with mass flow rates up to 70 g/s at a maximum design pressure of 20 bar-a for the test campaigns. Finally, the feasibility of using passive, self-protection against a quench in a fusion-scale NI TF coil was experimentally assessed with an intentional open-circuit quench at 31.5 kA terminal current.

Precise values for radiated energy in tokamak disruption experiments are needed to validate disruption mitigation techniques for burning plasma tokamaks like ITER and SPARC. Control room analysis of radiated power (P rad) on JET assumes axisymmetry, since fitting 3D radiation structures with limited bolometry coverage is an under-determined problem. In mitigated disruptions, radiation is toroidally asymmetric and 3D, due to fast-growing 3D MHD modes and localized impurity sources. To address this problem, Emis3D adopts a physics motivated forward modeling ('guess and check') approach, comparing experimental bolometry data to synthetic data from user-defined radiation structures. Synthetic structures are observed with the Cherab modeling framework and a best fit chosen using a reduced chi 2 statistic. 2D tomographic inversion models are tested, as well as helical flux tubes and 3D MHD simulated structures from JOREK. Two nominally identical pure neon shattered pellet injection (SPI) mitigated discharges in JET are analyzed. 2D tomographic inversions with added toroidal freedom are the best fits in the thermal quench (TQ) and current quench (CQ). In the pre-TQ, 2D reconstructions are statistically the best fits, but are likely over-optimized and do not capture the 3D radiation structure seen in fast camera images. The next-best pre-TQ fits are helical structures that extend towards the high-field side, consistent with an impurity flow under the magnetic nozzle effect also observed in JOREK simulations. Whole-disruption radiated fractions of 0.98+0.03/ -0.29 and 1.01+0.02/-0.17 are found, suggesting that the stored energy may have been fully mitigated by each SPI, although mitigation efficiencies well below ITER and SPARC requirements for high energy pulses are still within the large uncertainties. Emis3D is also used to validate JOREK SPI simulations, and confirms improvements in matching experiment from changes to impurity modeling. Time-dependent toroidal peaking factors are calculated and discussed.

In this article, the SPARC Toroidal Field Model Coil (TFMC) experimental tests are described. The tests include detailed comparisons to a hierarchy of electromagnetic and structural models of the coil. The tests confirm the ability of the no-insulation no-twist (NINT) configuration to provide highly stable dc operations with peak magnetic field in excess of 20 T at the rare earth yttrium barium copper oxide (REBCO) tape stacks. The advantages of the modular TFMC approach are validated including the ability to test and probe the REBCO and structural response of the coil in stages, a strategy that will be applied to large-scale coil production for SPARC. The electromagnetic models show high fidelity agreement to the dc and charging coil performance in terms of current distribution, voltages, and heating. The transient response of the TFMC to open-circuit events is examined. The coil exhibits the positive features of the NINT design with very low induced voltages and a predictable redistribution of current and resulting volumetric heating. However, highly localized damage occurs during a programmed open circuit at operational conditions similar to those used in the SPARC tokamak, in agreement with calibrated electromagnetic models. This reveals the cause and remedy to the thermal instability that gives rise to the damage. Therefore, the conclusion of the tests is that the NINT TFMC is confirmed as meeting the programmatic requirements for SPARC and, more generally, for high-field tokamaks.

The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.

A cross-machine comparison of global parameters that determine the runaway electron (RE) generation and loss process during tokamak start-up was carried out with the aim to extrapolate these to ITER. The study found that all considered discharges, also those that do not show signs of RE, are non-thermal at the start, i.e. have a streaming parameter larger than 0.1. During the current ramp-up the electric field, E, remains above the critical value, E (c), that allows RE in the plasma. The distinction to be made is not if RE can form but, if sufficient RE can form fast enough such that they are detected or start to dominate the dynamics of the tokamak discharge. The dynamics of the value of E, density and temperature during tokamak are key to the formation of RE. It was found that larger devices operate with E closer to E (c), due to their higher temperatures, hence the RE generation is relatively slower. The slower time scales for the formation of RE, estimated to be of the order of 100s of ms in ITER simplifies the development of avoidance schemes. The RE confinement time is also an important determinant of the entire process and is found to increase with the device size. The study also revealed that drift orbit losses, a mechanism often attributed as the main RE loss mechanism during the early tokamak discharge, are actually more difficult to achieve. RE losses might be more likely attributed to RE diffusion due to magnetic turbulence.

Asymmetric halo currents (HCs) can exert large net forces on the vacuum vessel and other components during disruptions on tokamaks. The displacements caused by these forces can then be amplified if these asymmetric forces rotate at frequencies resonant with the vessel. This paper reports on the investigation of a recently proposed scaling law for the disruption HC rotation frequency [Saperstein et al., “Halo current rotation scaling in post-disruption plasmas,” Nucl. Fusion 62, 026044 (2022)] that combines measurements on Alcator C-Mod with those on HBT-EP. We find that a new non-circular version of the scaling law [ ⟨ f rot ⟩ m / ⟨ m ⟩ ∝ 1 B T ( S / π )] takes into consideration the dependence of frot on the poloidal structure of the MHD instability (m) driving the asymmetry and describes the disruption-averaged rotation frequency on C-Mod. Disruption rotation is also found to be insensitive to the vertical position and impurity content of the plasma at the onset of the disruption. However, a stagnation in the time evolution of frot is occasionally observed. Observations are consistent with the dominance of poloidal rotation during the disruption, which is motivated by the poloidal drift nature of the scaling law.

Plasma disruption is a very dangerous event for future tokamaks and fusion reactors. Therefore, predicting disruption is crucial for ensuring the safety and performance of reactors. In this study, the features of two deep learning algorithms are integrated to establish a multi-scale hybrid network disruption predictor. Firstly, 43 diagnostic signals are extracted by a convolutional neural network (CNN), and the time information is learned by a long short-term memory network. The predictor is trained and tested on a database containing ∼ 10 4 non-all-metal wall discharges. Its area under the receiver operator characteristic curve (AUC), which is a common performance metric for deep learning algorithms, reaches 0.97, and the true positive rate is ∼ 95.3 % , while the false positive rate is ∼ 8 % . Since EAST was upgraded in 2020, the wall condition has been upgraded from non-all-metal to all-metal. To examine the robustness of the predictor, the EAST disruption predictor is migrated to the all-metal wall experiment for the first time. It is again tested under the all-metal wall experimental data, and its warning performance decreases significantly, with an AUC of only 0.79. To improve the robustness and sensitivity of the predictor against disruptions, the convolutional attention mechanism is introduced into the CNN. After training and testing with the same data set, the warning performance for the all-metal wall data is improved, with the AUC value increasing to 0.84. To further improve the robustness of the predictor, the t-distributed stochastic neighbor embedding algorithm is employed to explore the difference of the sample distribution before and after EAST upgradation, and the transfer learning algorithm is used to reduce this difference. By applying transfer learning to a small amount of discharges from the all-metal wall data, the warning performance in the all-metal wall experiment is further improved, and the AUC value increases to 0.93. Overall, the proposed data-driven predictor is promising for applications in future devices such as ITER.

In Izzo et al (2022 Nucl. Fusion 62 096029), state-of-the-art modeling of thermal and current quench (CQ) magnetohydrodynamics (MHD) coupled with a self-consistent evolution of runaway electron (RE) generation and transport showed that a non-axisymmetric (n = 1) in-vessel coil could passively prevent RE beam formation during disruptions in SPARC, a compact high-field tokamak projected to achieve a fusion gain Q > 2 in DT plasmas. However, such suppression requires finite transport of REs within magnetic islands and re-healed flux surfaces; conservatively assuming zero transport in these regions leads to an upper bound of RE current ∼1MA compared to ∼8.7MA of pre-disruption plasma current. Further investigation finds that core-localized electrons, within r/a<0.3 and with kinetic energies ∼0.2 – 15MeV , contribute most to the RE plateau formation. Yet only a relatively small amount of transport, i.e. a diffusion coefficient ∼18m2s−1 , is needed in the core to fully mitigate these REs. Properly accounting for (a) the CQ electric field’s effect on RE transport in islands and (b) the contribution of significant RE currents to disruption MHD may help achieve this.

Accurate reconstruction of the plasma equilibrium is imperative for successful operation of the SPARC tokamak. In order to assess the expected reconstruction accuracy throughout the duration of design-point discharges, the EFIT equilibrium reconstruction code was deployed for SPARC. Reconstructions from SPARC baseline scenarios were compared with free-boundary equilibria generated by FreeGS, Toksys, and the Tokamak Simulation Code. The key geometric areas of interest, where design constraints are imposed, included: the inner and outer midplane gaps, the X-point locations, as well as the strike point locations. Successful reconstructions of various reference discharges, using deviations in these key geometric quantities as metrics, were calculated from synthetic signals considering an optimized equilibrium magnetic sensor set. The optimization process for this sensor set combined a scan of randomized sensor placement with a linear perturbation analysis to determine critical sensor locations, while simultaneously conforming to design constraints on the sensor placement. This optimized set was also successful in performing equilibrium reconstructions with the addition of error to synthetic measurements of magnetic flux and magnetic field, as well as contributions from eddy currents in conducting structures. These methods represent a workflow of optimization and validation that balances the engineering constraints of sensor placement with achieving sufficient reconstruction fidelity for science and operations missions for SPARC.

The benefits of operating fusion devices, such as tokamaks and stellarators, at high fields make high-temperature superconducting magnets necessary to realize a compact fusion power system. Superconducting stellarators, such as W7-X, have used standard low-temperature superconductor technology niobium-titanium. ARPA-E has recently funded a two-year project led by the startup Type One Energy and involving the Fusion Technology Institute at the University of Wisconsin-Madison, the Plasma Science to design and fabricate the first non-planar high-temperature superconductor (HTS) rare-earth barium copper oxide (REBCO) coil for a high-field stellarator based on the SPARC tokamak’s VIPER cable concept. The design consists of a 1.5-turn non-planar REBCO coil supported by a pair of 3D printed stainless steel radial plates. The ultimate goals of the project are to determine if commercial REBCO tapes and additive manufacturing can be used to fabricate high field ( ⩾ 10 T ) non-planar coils with tight bending radii ( ≃ 100 m m ) and with a degradation of the critical current smaller than 20% with respect to the expected performance. In this work we present numerical analysis for non-planar coils (critical current, magnetic field map, Lorentz forces and quench aspects) at the operating conditions of 77 K and 20 K and the fabrication and testing in liquid nitrogen (77 K) of the first two non-planar demonstrators for stellarators based on a VIPER cable. The first demonstrator is a short NO n-planar V IP E R cab L e (cable demonstrator at which we will refer to as NOVEL ) equipped with 100 HTS REBCO tapes and with a critical current of 5700 A at 77 K (self-field); the second is a M ult I ple turns (1.5-turns) NO n-pl AN ar coil (coil demonstrator at which we will refer to as MINOAN ) equipped with 30 HTS tapes and with a critical current of 2100 A at 77 K (self-field). Both demonstrators were tested at 77 K (liquid nitrogen bath) and the results showed that—even after being bent into non-planar shapes with bend radii ≃ 100 m m —the degradation of the critical current I c was within 15%, meeting the expected goals of the project.

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student t process regression. We apply Student t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.

DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter- I p steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high- Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L–H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at ∼ 8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co- I p beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate β N in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.