with R Clark (UCSD), V Glukhov, M Nurgaliev, G Subbotin, A Kachkin, D Sorokin (Next Step Fusion), M Austin (UT Austin), J Chen, L Zeng, and T Rhodes (UCLA)

The status of the plasma state is vital to knowing the health, stability, and control metrics of a fusion reactor’s plasma; in this project, our focus is primarily limited to whether a plasma is in L or H mode. Additionally, we restrict ourselves to using only reactor-relevant diagnostics (or, at the very least, diagnostics that are predicted to survive working in a reactor-relevant environment). This task utilizes multiple different Machine Learning (ML) tools to help perform this classification task, including some well-known binary classification tools like gradient boosted classifiers, decision trees, and support vector machines; we also seek to use unsupervised learning classifiers to identify patterns in our data to aid us in this classification.

Diagnostics such as Electron Cyclotron Emission and Profile Reflectometers offer reactor-relevant methods of measuring the temperature and density profiles. Machine learning techniques can be applied to do some feature extraction from these profile measurements to identify the presence of an H-mode pedestal. Alternatively, a more turbulence-focused approach can be used; the Radial Icterometer Polarimeter (RIP) at DIII-D offers fast measurements of both the density and magnetic fluctuations, which have been shown to capture the stark change in turbulence during LH and HL transitions. Spectrogram methods in ML can be used to identify when such transitions occur to aid in our H-mode detection tool.

This work is ongoing as a survey of possible diagnostics or diagnostic ensembles can be used in performing plasma state classification.

with GF Subbotin, DI Sorokin, MR Nurgaliev, AA Granovskiy, EV Adishchev, EN Khairutdinov (Next Step Fusion), R Clark (UCSD), H Shen, W Choi, and J Barr (General Atomics)

Reinforcement Learning (RL) is a powerful tool in machine learning and is considered one of the three main branches of machine learning research (the others being supervised and unsupervised machine learning). It is unique in that it does not start with any training data, rather it operates with an agent and an environment. The RL agent is an algorithm that engages with the environment and receives rewards based on achieving some pre-defined goals and learns through repeat interaction with the environment to continuously maximize the reward it receives.

In our project, we sought to create a controller that can control the shape of a plasma in the DIII-D tokamak through feedback control of the shape coils. To create an RL controller, we used our plasma simulator, NSFsim, as an environment for an RL agent, typically just a neural network, to interact with. Extensive efforts went into developing and validating NSFsim as a proper plasma simulator for DIII-D. With a validated plasma simulator, we trained the RL agent to target specific plasma shapes with the reward function encouraging the agent to hit a specific plasma boundary, magnetic axis, and x-point location.

To date we have had multiple successful deployments of our RL controller on the DIII-D tokamak performing accurate shape control. Further work on this topic will amount to seeking greater controller accuracy through the use developing and validating higher fidelity plasma simulators and the expansion of the RL controller to perform additional controls including but not limited to temperature and density profile control.

with Randall Clark (UCSD), Maxim Nurgaliev, Eduard Khayrutdinov, Georgy Subbotin (Next Step Fusion), and Anders Welander(General Atomics)

Accurate modeling of plasma shape is essential for the design and operation of fusion pilot plants. In this project, we validate NSFsim, a new free-boundary Grad-Shafranov and transport solver developed from the DINA code, by benchmarking its equilibrium reconstruction performance against real DIII-D experimental data, EFIT reconstructions, and GSevolve simulations. NSFsim is configured for the DIII-D geometry and diagnostics and demonstrates robust performance across five distinct plasma shapes: lower and upper single-null, double-null, inner-wall limited, and negative triangularity. The comparisons include magnetic flux contours, time evolution of flux at key locations, and synthetic magnetic diagnostics. Our results show that NSFsim achieves accuracy comparable to GSevolve and EFIT, while offering a flexible, machine learning–friendly platform that supports reinforcement learning applications for plasma shape control. This validation establishes NSFsim as a reliable simulation tool for magnetic equilibrium modeling at DIII-D and paves the way for future development of data-driven control strategies.

with S.D.V. Williams (UCSD) and M.N. Gudorf (Georgia Tech)

This project introduces a novel approach to understanding plasma turbulence by applying periodic orbit theory—a framework from nonlinear dynamics—to magnetically confined plasmas. We demonstrate this method using a simplified model of trapped ion mode (TIM) turbulence, governed by the LaQuey-Mahajan-Rutherford-Tang (LMRT) equation. By identifying doubly periodic coherent spacetime structures (fundamental building blocks of turbulent motion), we construct a minimal “alphabet” of turbulent behavior. These exact solutions are extracted numerically and then used to reconstruct large-scale turbulent dynamics through a process of clipping, gluing, and shadowing. This enables accurate calculation of average transport quantities such as fluctuation energy, anomalous diffusion, and turbulent energy flux. Our results establish a general, model-independent framework that connects chaotic plasma dynamics with a finite set of recurring structures, opening pathways for predictive modeling and control of turbulence in fusion plasmas.

Future efforts on this project will be directed towards extending the numerical tools and analytical techniques so that more complex plasma turbulence models may be treated. This includes the application of AI/ML and PINNs (Physics-Informed Neural Networks) to pattern recognition, as well as the construction of an ideal metric for shadowing. Additionally, the field-theoretic nature of this formulation of turbulence calls for the exploration of analytic tools from quantum field theory, differential geometry, and group theory.

with J.-K. Park (PPPL), C. Paz-Soldan (Columbia U.), E. Kolemen (Princeton U.), Y. Liu (General Atomics), N.C. Logand (LLNL), and H. Frerichs (UW Madison)

This project extends the predictive capabilities of non-axisymmetric (3D) field physics with integrated scenario optimizations to demonstrate the scientific feasibility of 3D magnetic perturbations for transport and instability control in long-pulse high-performance tokamak plasmas. A key example of this is the suppression of the edge-localized modes (ELMs) by resonant 3D magnetic perturbations (RMPs). ELMs constitute a major challenge to the operation of fusion-grade tokamak plasmas such as ITER due to the large transient heat loads they deliver to plasma-facing components (PFCs). US scientists have pioneered the development of the 3D-field approach to ELM control since its first discovery and have a crucial role in exporting the technique to international facilities such as the KSTAR tokamak in Korea, the EAST tokamak in China, and the AUG tokamak in Germany. Recent international collaborations have enabled the project team to compile comprehensive 3D databases, develop testable criteria for the accessibility and threshold conditions of ELM suppression, improve understanding of underlying particle transport for predicting pedestal profile modifications and heat flux to PFCs, and adaptively control 3D fields to restore confinement under ELM suppression. These recent accomplishments have exposed opportunities for US scientists to resolve remaining challenges, such as the compatibility of 3D ELM suppression with core optimized scenarios as well as heat loads to PFCs for long pulses.

Enhanced physics models, databases, and experimental analysis infrastructures developed by this project team will be all leveraged utilizing the strong partnerships among the multi-institutional US groups and international collaborators. Predictive capabilities for 3D edge perturbations, transport, and ELM suppression will be extended and validated over international databases with analysis utilizing US cutting-edge 3D simulations. Intelligent control algorithms for the 3D field operational windows and associated profile alterations will be developed and integrated into TRANSP for long pulse optimization. This novel ‘3D TRANSP’ will be tested and refined on KSTAR with the new tungsten divertors, in preparation for ITER applications. In parallel, the 3D field physics basis will be extended to optimize fast ion losses and heat flux to divertors. Understanding of turbulent transport across 3D magnetic topologies, including edge and core island chains as well as stochastic field lines, will also be extended. All these developments will be integrated to establish optimized long pulse scenarios with 3D fields in KSTAR. The predictive 3D scenario optimizations will be then combined with real-time, actively probing, adaptive controllers to demonstrate ELM-free H-mode scenarios in KSTAR for long pulses of up to 300s with 𝛽N>2.0, minimal core MHD, maximized confinement, and minimized heat loads to plasma-facing components.

with C.A. Mehta and E.G. Kostadinova (Aubrun U.)

To this day we do not have definitive answers to two of the most profound questions in science: how and when did life on Earth begin? The synthesis of organic compounds during meteoritic impacts on the surface of the Earth is a process that could have started life on Earth as early as the Hadean Earth (4.6 to 4 billion years ago). This project aims to investigate the formation of organic molecules in a laboratory plasma environment similar to the one created by meteorites entering the oxygen-poor atmosphere of the Early (Hadean) Earth. The proposal includes experiments which will be carried out at the DIII-D National Fusion Facility.

There are two main hypotheses for the initial creation of organic compounds on Earth: (i) they were created by external processes and brought to Earth by meteorites or (ii) they were created in the plasma environment of lightnings occurring inside the Earth’s atmosphere. While organics-rich meteorites have been found on Earth, most meteorites entering the atmosphere ablate, potentially losing all their mass, including organics that were present, which makes this mechanism less efficient. On the other hand, the plasma environment created by atmospheric lightnings provides favorable conditions for efficient synthesis of organics if the needed precursor compounds are present. However, it is generally thought that the Early Earth’s atmosphere was poor in compounds needed for the formation of organics. A third scenario has been proposed, which combines these two mechanisms. It is suggested that organics could have formed in the plasma tail of ablating meteorites as they travel through Earth’s atmosphere due to the interaction of compounds disassociated from the meteorite and compounds already present in the atmosphere. This hybrid mechanism is both possible to have happened in the Early Earth and is likely efficient method for organics synthesis.

Using principles of physics, chemistry, and geology, our team plans to study the production of organics and their precursors from atmospheric ionization in laboratory conditions, thus, testing the hypothesis that life could have originated as carbonaceous meteoroids traveled through the oxygen-poor atmosphere of the Early Earth. We plan to utilize the DIII-D tokamak, which can produce plasma as hot and as dense as the plasma surrounding meteorites during atmospheric entries. Specifically, we will investigate the interaction of carbonaceous materials in plasma conditions that promote the synthesis of urea – a key ingredient in the origin of life – or its precursors, such as ammonia. Urea, or carbamide, is an organic compound consisting of two –NH2 groups joined by a carbonyl (C=O) functional group. Here we propose two types of experiments: (i) launching carbon pellets and (ii) exposing stationary carbon rods in a hydrogen or deuterium plasma with nitrogen and/or methane puffs. The initial goal is to study the conditions needed to synthesize ammonia molecules, which are precursors for the formation of urea, and other organic compounds. Then, the synthesis of various organics will be explored using carbon-based samples and nitrogen and/or methane puffs. Finally, the experimental data will be compared against theoretical models and numerical simulations of meteorites ablating in the Early-Earth’s atmosphere. 

with E.G. Kostadinova (Aubrun U.)

In 1859, during Solar Cycle 10, a massive solar flare caused a geomagnetic storm, called the Carrington event, which was so intense that it short-circuited telegraphs all over the world, causing unprecedented panic and chaos. In 1967, during the rapid rise of Solar Cycle 20, a solar energetic particle event caused severe disruption of high-frequency communication in the polar cap, which misleadingly seemed like a disruption caused by an incoming intercontinental ballistic missile. As this event occurred during the Cold War, it could have had catastrophic societal impacts if it wasn’t for the quick reaction from space weather scientists. In 2019, the Sun entered Solar Cycle 25, which is currently increasing solar activity with expected peak in 2025. As a result, just this February (of 2022), SpaceX lost 49 small satellites due to a solar storm event.

It has been shown that up to 50% of the energy released during solar flares is carried by accelerated suprathermal electrons. However, what mechanisms guide the acceleration and transport of these energetic electrons is a long-standing mystery. Analysis of spacecraft data from several missions established a clear relationship between the occurrence of energetic electrons in the solar wind and the formation of magnetic island islands after magnetic reconnection events, suggesting that electron acceleration is caused by changes in the magnetic field structure. As spacecraft measurements are limited to a low number of data points along their trajectory, while self- consistent 3D simulations are prohibitively time-consuming, the development of a lab experiment to study fast electrons in plasma with magnetic islands is sorely needed.

This project aims to investigate the origin and transport properties of energetic electrons in magnetized plasma in the presence of magnetic island structures. The proposal includes experiments at the DIII-D tokamak using coil perturbations (to manipulate island structure) and an Electron Cyclotron Heating and Current Drive (ECH/ECCD) electron ‘tagging’ technique (to quantify the properties of suprathermal electrons). Specifically, we will examine how the flux and energy of suprathermal electrons change as a function of island size, location, structure, and dynamics. The results will be compared against spacecraft data from the ESA’s Cluster mission and NASA’s Magnetospheric Multiscale mission. The main scientific goal of this project is to establish how magnetic islands affect the acceleration and transport of energetic electrons in laboratory and space magnetized plasmas.

The proposed experiments are ideally suited to the capabilities of the DIII-D tokamak for several reasons. First, magnetic islands can be formed and manipulated using perturbation coils. Second, DIII-D is equipped with world-class diagnostics capable of quantifying the properties of energetic electrons. Finally, an electron ‘tagging’ technique, using ECH/ECCD pulses, was recently developed and successfully used to study energetic electron transport within specific locations in the DIII-D plasma. The team plans to conduct a series of experiments where the electron tagging technique, in combination with various diagnostics, will be used to determine how properties of energetic electrons change for different configurations of the magnetic island topology. In addition, the team will conduct a series of simulations to determine the vacuum magnetic field structure, the location and characteristics of ECH/ECCD pulses, and the probability for electron transport as a function of magnetic field topology. Finally, simulation results and experimental data will be compared against available spacecraft data to test the hypothesis that magnetic island formation during solar flares leads to the occurrence and transport of energetic electrons.

with E.G. Kostadinova (Aubrun U.) and R. Smirnov (UC San Diego)

The Frontier Plasma Science experiments proposed by our team will model the processes occurring during spacecraft atmospheric entries and meteorite planetary collisions by inserting material targets into the Scrape off Layer (SOL) and edge plasma of a tokamak. This project will investigate important plasma-material processes during high-enthalpy atmospheric entries, including heat flux generation and material ablation. Of particular interest is to explore heat and particle flux detachment in front of the sample using concepts and techniques developed in the latest studies of divertor detachment. Exploration missions to the Solar System’s gaseous giants and hyperbolic re- entries into the Earth’s atmosphere require spacecrafts that can withstand high velocity (>10 km/s), high heat fluxes (>10MW/m2), and corresponding enthalpies. Ablative materials have been used as thermal shields to protect the spacecraft from the severe heating during entry. However, the development of high enthalpy ablating materials is challenging due to the lack of adequate ground testing facilities. In this project we will assess the performance of candidate shielding materials in a laboratory environment with multiple in-situ diagnostics, which is enabled by the progress in the tokamak research. Modern tokamaks feature relatively long discharges (~10 s) with well controlled stable plasma conditions at the edge where the heat flux and the flow speed are similar to those experienced during atmospheric entries. 

The experiments will be carried out at the DIII-D National Fusion Facility in San Diego, CA operated by General Atomics (GA) for the DoE. DIII-D offers an ideal environment for this project due to inherent properties of the tokamak plasma – rotation of the core and edge plasma and fast flow in the SOL. Any object launched radially from the wall with zero toroidal speed incurs motion relative to the plasma comparable to the entry velocity for the Galileo probe to Jupiter. This approach provides the ability to study plasma-material interactions in the projectile frame of reference, avoiding the need to build a hypervelocity projectile launcher. Therefore, all the diagnostics can be localized near the injection point and focus on the projectile, while it still exhibits only minimal speeds in the laboratory frame. Additionally, the high plasma temperatures in the tokamak are comparable to the temperatures expected for gas giant spacecraft entries. 

DIII-D has one of the strongest research programs in the area of Plasma-Material Interactions (PMI), thanks to the versatile suite of diagnostic tools. The core of the PMI diagnostics is the Divertor Materials Evaluation System (DiMES) which can be configured to expose objects smaller than ~5 cm. Probe size ranging from a few 100 um to 1 cm will be adjusted to ensure the safe exposure of heat shield material, avoiding plasma disruption. The DIII-D core and edge plasma conditions will be varied in a heated L-mode discharge to provide stable plasma and allow for scans over a range of entry-relevant conditions experienced by the probe material. Scaling techniques will be developed to extrapolate these results to larger projectiles, longer exposures, and a wider parameter space. This will allow for comparison with experimental data from previous space flight missions and other on-ground testing facilities. 

with E. Howell (Tech-X) and E.G. Kostadinova (Auburn University)

This research project focuses on understanding how magnetically confined plasmas in tokamaks respond to small, three-dimensional (3D) magnetic field perturbations—both those that occur naturally as intrinsic field errors and those that are deliberately applied to enhance plasma performance and control. Such perturbations can lead to profound changes in how heat, particles, and radiation interact with the materials lining the vacuum vessel, which is a critical concern for next-generation fusion reactors like ITER and the planned New Tokamak User Facility (NTUF). A central objective of this work is to develop predictive models and control strategies for mitigating edge-localized modes (ELMs)—transient instabilities at the plasma boundary that can damage reactor walls and limit performance. The project aligns closely with key priorities outlined in the DOE’s “Fusion Energy Sciences: A Ten-Year Perspective (2015–2025),” especially the goals of controlling transient events and managing the plasma-material interface.

Our work is structured around four complementary research thrusts. First, we are advancing realistic, multi-mode modeling of the plasma response to externally applied 3D magnetic fields using resistive magnetohydrodynamic (MHD) simulations, including novel applications of Fractional Laplacian Spectral techniques to capture anomalous transport in stochastic magnetic fields. Second, we are using the NIMROD code to simulate plasma response under applied 3D perturbations and validating these predictions against experimental data from DIII-D and KSTAR, including through the development of synthetic diagnostics. Third, we are exploring the physics of magnetic island bifurcations, a key nonlinear phenomenon that affects confinement and transport, again using NIMROD to model their evolution and impact on electron transport. Finally, we are investigating innovative scenarios for creating cold, dense, and radiating plasma mantles at the plasma edge, which are essential for managing heat and particle exhaust in burning plasmas. Collectively, this work aims to provide a pathway to integrated operating scenarios that mitigate ELMs and support stable, radiative plasma boundaries—key ingredients for the success of ITER and future fusion power plants. 

with C. Holland (UC San Diego)

SMARTS (Surrogate Models for Accurate and Rapid Transport Solutions) is one of 12 SciDAC-5 partnerships between the Offices of Fusion Energy Sciences and Advanced Scientific Computing Research in the US Department of Energy Office of Science. 

The ultimate objective of the SMARTS (Surrogate Models for Accurate and Rapid Transport Solutions) project is to provide the modeling and simulation capabilities needed to resolve this gap by substantially advancing our ability to accurately predict multi-species particle and thermal confinement in tokamak burning plasmas. To do so, we will combine demonstrated advances in GPU acceleration of gyrokinetic codes, applications of Bayesian optimization and surrogate models to transport in fusion plasmas, and tokamak-integrated modeling capabilities. Through this approach, we will deliver: 

• SMARTSsolver: a next-generation open-source HPC-compatible transport solver which will support a hierarchy of transport model fidelities and use surrogate models to provide the accurate and rapid transport solutions needed for practical WFM design and optimization.

• Verified and validated transport predictions against currently used production transport modeling capabilities and experimental data from U.S. tokamaks.

• Extensive predictions of multi-species density and temperature profiles in burning plasmas, examining how reactor-relevant actuators such as radiofrequency heating and deep pellet fueling will impact confinement of different particle species in a variety of pulsed and steady-state FPP-relevant operating scenarios, and thereby overall plasma confinement.

• An open, publicly accessible, curated database of gyrokinetic simulations available for the entire community to use for future studies, including model development, benchmarking, and verification.

with J. Candy (General Atomics) and C. Holland (UC San Diego)

The AToM project philosophy is to enhance and extend present modeling capabilities by leveraging and integrating existing research. AToM supports numerous physics codes, frameworks, and tools which have been developed independently by the fusion community, often through years or decades of research. AToM seeks to streamline the integration of these tools, to further their development, and to deploy and support the resulting software.

The goal of the AToM project is to support, integrate, and build upon a wide spectrum of existing research activities in the US fusion program, and guide the integration of high performance computing resources to enable a broad range of new physics capabilities. A number of computational tools, including a workflow manager (OMFIT), computational framework (IPS), and high performance simulation codes (GYRO/CGYRO, NEO, TGYRO, and COGENT) enable simulations of complex plasma behavior, and extensive validation against experimental data. AToM targets advanced integrated simulations which couple core, pedestal and scrape-off-layer physics in order to predict, and further optimize, performance of the fusion plasma.

The key thrusts of the AToM project are:

1. AToM environment, performance and packaging

2. Physics component integration

3. Validation and uncertainty quantification

4. Physics and scenario exploration

5. Data and metadata management

6. Liaisons to SciDAC partnerships

with J.-K. Park (PPPL)

The aim of this proposed project is to develop a unified physics basis and predictive capability for the control of edge-localized modes (ELMs) with optimized non-axisymmetric (3D) fields, by leveraging the unique research capabilities of international tokamak facilities. The proposed tasks first address high-priority research and physics questions, and then move onto operational demonstrations. The ultimate goal is a capability to optimize 3D coils and plasma scenarios for ELM control in ITER and next-step devices, culminating in demonstrated long-pulse stationary ELM suppressed discharges on KSTAR. 

The proposal is organized by into six cross-cutting tasks. First several open questions regarding macro-scale parameter compatibility with edge field penetration and ELM suppression will be addressed via cross-machine study. Work focuses on the critical parameters of plasma axisymmetric shape, flow, and edge q-profiles, which are known to fundamentally determine the accessibility of 3D ELM suppression (Task 1). This task will use targeted experiments on several tokamaks to progressively establish the access boundaries and viable operating space, enabling the development of empirical and predictive scaling laws for 3D ELM suppression. Additional key parameters (such as density and toroidal field) that strongly affect the edge field penetration threshold will also be incorporated into penetration scaling laws. Other effects that limit the stable ELM suppressed operating window, such as core field penetration, H-L back-transition, and confinement degradation, will also be parametrized into scaling laws to enable robust operating space prediction (Task 2). These two tasks will provide datasets to study underlying transport mechanisms, which will be elucidated by leveraging the unique diagnostics of international facilities and simulating the observed effects with both neoclassical and turbulent transport models (Task 3). Next the implications on divertor heat flux of variations within the stable operating windows will be addressed, with a central aim being long-pulse ELM stable operation enabled via reduced divertor heat flux (Task 4). In addition to open-loop prediction and control, closed-loop feedback control will be developed to allow the plasma to automatically remain in the stable parameter ranges and configurations developed in earlier tasks (Task 5). Finally, fundamental understanding and predictive scaling laws gained in the previous sections are applied to the development of next-generation more distant coil sets for reactor-relevant long-pulse 3D tokamak operation. KSTAR is chosen to be a focus device for the project as a whole, featuring prominently in all six tasks. The common physics basis (Tasks 1-3) however will be developed by exploiting the unique capabilities of AUG and EAST as well as KSTAR, and will be further levered by existing datasets and experience from US facilities such as DIII-D. The design on 3D coils (Task 6) will be mainly performed on COMPASS-U as well as KSTAR, motivated by plans by these facilities to invest in advanced coil sets for future, but also will be explored on ex-vessel options feasible in reactor scale.

with M.O. Hanson (UC San Diego, M.S. student).

The L-H transition is a complex phenomenon involving changes in the turbulence near the edge of magnetically-confined plasmas. Studies of turbulent de-correlation length reductions and Reynolds stress energy transfer rates, correlated with changes in the edge plasma rotation profile, indicate that the applied power and torque from various types of external heating sources is one of several key control parameter for causing the plasma to bifurcate to a state with markedly improved confinement, known as the High Confinement mode (H-mode) compared to the Low Confinement mode (L-mode). Other key control parameters are the 2D and 3D boundary shape as well as the impact of small helical resonant 3D magnetic perturbation, referred to as Resonant Magnetic Perturbations (RMPs), while non-resonant 3D magnetic perturbations (NRMP) do not affect the power level needed to trigger the L-H transition. The intriguing physics question centers on developing an understanding of how changes in the 2D boundary shape, with and without RMPs or NRMPs, affect the power and torque required to trigger the L-H transition.

Initially, a database will be compiled consisting of DIII-D discharges with and without L-H transitions for various heating power and torque levels with different 2D shapes and different levels of RMP and NRMP fields. Since the conventional thinking of what controls the L-H power is governed by interactions between plasma profiles, turbulence, and flows, the database will be used to compare differences in plasma profiles and flows using a python analysis tool available on the iris computer cluster at GA. The goal of the project is to determine whether changes in the L-H transition are correlated with differences in plasma profiles and flows found during the database analysis and to develop a multi-parameter scaling expression that best fit the data in the database. The final objective of the project is to make a comparison between the new multi-parameter scaling expression and an empirical L-H power scaling (the Martin scaling [1,2]).

[1] Y.R. Martin, T. Takizuka, and ITPA CDBM H-mode Threshold Database Working Group, J. Phys. Conf. Ser. 123, 012033 (2008).

[2] ITER Organization, ITER Research Plan within the Staged Approach, ITR-18-003, 2018, www.iter.org/technical-reports?id=9

with M.N. Kot (U of Michigan,  SULI), G.P. Canal (U of Sao Paulo, Brazil)

3D perturbation fields are widely used for suppressing Edge Localized modes (ELMs) in H-mode tokamak discharges. DIII-D can apply n=1,2 and 3 magnetic perturbations using the existing I- and C-coil sets and up to n=6 using the planned M-coils. ITER’s design includes three rows of 9 coils each and will use n=3 and n=4 fields for ELM suppression. The upgraded TCABR tokamak (R0 = 0.62 m, a = 0.2 m, B0 ≤ 1.1T, IP ≤ 100 kA, max. discharge duration of 100 ms) operated at the University of Sao Paulo, Brazil, will have 3 rows of 18 internal coils located on the Low Field side and 3 rows of 18 coils each situated on the center post. In this work we study the effects on the edge plasma magnetic topology and the magnetic footprints on the divertor surfaces produced by the proposed TCA-BR non-axisymmetric coil sets using TRIP3D-SURFMN and M3D-C1 plasma response codes. The results from the application of low (n=3) and high (n>3) toroidal mode number perturbation fields from the TCA-BR coils are compared to the existing and proposed DIII-D coil sets as well as to the ITER ELM coils using a set of previously developed metrics.