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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Closed-loop simulation with Grad-Shafranov equilibrium evolution for plasma control system development

Plasma control design increasingly depends on fast simulations able to connect to operational plasma control system (PCS) software for iterative development. GSevolve is a nonlinear free-boundary simulation that evolves the Grad-Shafranov equilibrium including current and pressure profiles, and can connect to all versions of the DIII-D PCS operational on devices around the world. Its ability to simulate the evolution of current continuously from open to closed field lines enables GSevolve to simulate full-discharge control from breakdown through plasma termination. GSevolve is compliant with the Plasma Control System Simulation Platform (PCSSP), a Matlab/Simulink®-based environment that serves as the primary framework for development of the ITER Plasma Control System (PCS). The code employs a novel response function-updating scheme to achieve fast simulations. The plasma evolves linearly until a significant shape change triggers a nonlinear update of the response while enforcing a prescribed accuracy. Old responses are recalled if the plasma changes back to an old shape. The equilibrium is a function of a state vector containing axisymmetric conductor currents and parameters related to parallel and perpendicular currents, which evolves according to an efficient state space circuit formulation. Control design and validation results are presented for simulations of DIII-D and EAST, connected to their corresponding versions of the DIII-D PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Artificial Intelligence/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)

This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Enabling co-simulation of tokamak plant models and plasma control systems

A system for connecting the Plasma Control System and a model of the tokamak Plant in closed loop co-simulation for plasma control development has been in routine use at DIII-D for more than 20 years and at other fusion labs that use variants of the DIII-D PCS for approximately the last decade. Here, co-simulation refers to the simultaneous execution of two independent codes with the exchange of data - Plant actuator commands and tokamak diagnostic data - between them during execution. Interest in this type of PCS-Plant simulation technology has also been growing recently at other fusion facilities. In fact, use of such closed loop control simulations is assumed to play an even larger role in the development of both the ITER Plasma Control System (PCS) and the experimental operation of the ITER device, where they will be used to support verification/validation of the PCS and also for ITER pulse schedule development and validation. We describe the key use cases that motivate the co-simulation capability and the features that must be provided by the Plasma Control System to support it. These features could be provided by the PCS itself or by a model of the PCS. If the PCS itself is chosen to provide them, there are requirements imposed on its architecture. If a PCS model is chosen, there are requirements imposed on the initial implementation of this simulation as well as long-term consequences for its continued development and maintenance. We describe these issues for each use case and discuss the relative merits of the two choices. Several examples are given illustrating uses of the co-simulation method to address problems of plasma control during the operation of DIII-D and of other devices that use the DIII-D PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Novel aspects of plasma control in ITER

ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Progress and plan of KSTAR plasma control system upgrade

The plasma control system (PCS) has been one of essential systems in annual KSTAR plasma campaigns: starting from a single-process version in 2008, extensive upgrades are done through the previous 7 years in order to achieve major goals of KSTAR performance enhancement. Here, major implementations are explained in this paper. In consequences of successive upgrades, the present KSTAR PCS is able to achieve ~48 s of 500 kA plasma pulses with full real-time shaping controls and real-time NB power controls. It has become a huge system capable of dealing with 8 separate categories of algorithms, 26 actuators directly controllable during the shot, and real-time data communication units consisting of +180 analog channels and +600 digital input/outputs through the reflective memory (RFM) network. The next upgrade of the KSTAR PCS is planned in 2015 before the campaign. An overview of the upgrade layout will be given for this paper. The real-time system box is planned to use the CERN MRG-Realtime OS, an ITER-compatible standard operating system. New hardware is developed for faster real-time streaming system for future installations of actuators/diagnostics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗
DOE OSTI2017

Overview of the preliminary design of the ITER plasma control system

An overview of the Preliminary Design of the ITER Plasma Control System (PCS) is described here, which focusses on the needs for 1st plasma and early plasma operation in hydrogen/helium (H/He) up to a plasma current of 15 MA with moderate auxiliary heating power in low confinement mode (L-mode). Candidate control schemes for basic magnetic control, including divertor operation and kinetic control of the electron density with gas puffing and pellet injection, were developed. Commissioning of the auxiliary heating systems is included as well as support functions for stray field topology and real-time plasma boundary reconstruction. Initial exception handling schemes for faults of essential plant systems and for disruption protection were developed. The PCS architecture was also developed to be capable of handling basic control for early commissioning and the advanced control functions that will be needed for future high performance operation. A plasma control simulator is also being developed to test and validate control schemes. To handle the complexity of the ITER PCS, a systems engineering approach has been adopted with the development of a plasma control database to keep track of all control requirements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Advances and challenges in KSTAR plasma control toward long-pulse, high-performance experiments

An overview of advances and progress on the KSTAR plasma control improvement is given. The enhancement of the KSTAR plasma control system (PCS) continues in order to enable implementations of more sophisticated control algorithms and capabilities of integrated controls on magnets, gas, heating and off-normal event handling. Features and functionalities on the generic magnetic controls, kinetic controls and new fault scheme design are added for the operation space expansion, mainly regarding recent achievements of higher plasma current up to 1.0–1.2MA, high-performance long pulse up to 90s, and discharges relevant to ITER. Technical challenges and prospective upgrades in the near future are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Implementation of AI/DEEP learning disruption predictor into a plasma control system

Abstract This paper reports on advances in the state‐of‐the‐art deep learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced in a 2019 NATURE publication [ https://doi.org/10.1038/s41586‐019‐1116‐4 ]. In particular, the predictor now features not only the “disruption score,” as an indicator of the probability of an imminent disruption, but also a “sensitivity score” in real time to indicate the underlying reasons for the imminent disruption. This adds valuable physics interpretability for the deep learning model and can provide helpful guidance for control actuators now implemented into a modern plasma control system (PCS). The advance is a significant step forward in moving from modern deep learning disruption prediction to real‐time control and brings novel AI‐enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII‐D vetted in the earlier NATURE publication. In addition to “when” a shot is predicted to disrupt, this paper addresses reasons “why” by carrying out sensitivity studies. FRNN is accordingly extended to use more channels of information, including measured DIII‐D signals such as (i) the “n1rms” signal that is correlated with the n = 1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics; (ii) the bolometer data indicative of plasma impurity control; and (iii) “q‐min”—the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Real-time plasma monitoring framework for advanced plasma control and ML-research in DIII-D

Real-time and adaptive plasma control is crucial for robust tokamak operation, requiring sensitivity and tolerance measurements of the plasma state. This paper presents the implementation of an integrated real-time plasma monitoring framework on the DIII-D tokamak to support advanced control approaches, including machine-learning (ML) methods. The system is built on the SHIELD framework, a high-performance modular architecture that provides a unified pipeline for integrating diverse diagnostics. The framework leverages high-bandwidth digitizers, fast numerical processing, and deterministic, low-latency interconnects to stream high-fidelity data from diagnostics such as electron cyclotron emission (ECE), beam emission spectroscopy (BES), CO interferometers, and a visible tangential divertor camera (TangTV). The system’s validity is demonstrated through direct comparisons of real-time and offline data. Furthermore, we present two key applications of the developed plasma monitoring system with ML-based plasma control strategies, including real-time divertor detachment and active Alfvén Eigenmode control. As a result, this work presents a robust and scalable approach for integrating high-frequency, multidimensional diagnostics into advanced control algorithms for future fusion devices.

AI/ML↗
DOE OSTI2020

Controlled plasma–droplet interactions: a quantitative study of OH transfer in plasma–liquid interaction

Plasmas in contact with liquids are a rich source of OH radicals and have been extensively studied in the last decade to leverage the ability to generate chemically reactive species in gas phase plasmas to decompose organics. Multiphase transfer of OH radicals is highly transport limited and to overcome transport limits, the plasma activation of aerosols, small liquid droplets, interspersed in the plasma has been proposed. In this work, we report a combined experimental and modeling study of a controlled plasma–droplet interaction experiment using a diffuse RF glow discharge in He + 0.2% H 2 O with detailed plasma diagnostics, ex situ analysis of the plasma-induced chemistry in the droplet containing formate, droplet trajectory and size measurements. This enables a quantitative study of the reactivity transfer of OH from the gas phase plasma to the liquid phase and how its diffusion limitations impact formate decomposition in the water droplet. For a droplet with a diameter of 36 μm, we observed 50% reduction in formate concentration in the droplets after plasma treatment for droplet residence times in the plasma of ~10 ms. These short droplet residence times in the plasma allow in some cases for droplet size reductions of ~5% in spite gas temperatures of 360 K. A one-dimensional reaction–diffusion model was used to calculate the OH transport and formate oxidation inside the droplet and was able to predict the conversion of formate by plasma in a droplet without any fitting parameters. The model further shows that formate conversion is dominated by near-interfacial reactions with OH radicals and is limited by diffusion of formate in the droplet. Here, the results show that a controlled plasma–micro-droplet reactor as reported in this study might be an excellent tool for detailed quantitative plasma–liquid interaction studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Recent advances in plasma control and physics research in the Large Helical Device

The Large Helical Device (LHD), the largest superconducting helical system in the world, is equipped with advanced heating and diagnostic tools, facilitating plasma control and physics research. Data assimilation was employed for electron temperature control using a real-time Thomson scattering system and real time prediction code. A virtual LHD environment enabled visualization of escaping high-energy tritium ions and demonstrated that these ions impact the rear side of the divertor plate. Pioneering results crucial to plasma control have also been achieved. Real-time wall conditioning using Lithium granule dropping improved bulk ion energy and particle transport while simultaneously enhancing the heavy impurity transport. Progress has also been made in the investigation of turbulence-driven transport. At the confinement bifurcation, ion-scale turbulence decreased, while electron-scale turbulence increased. A change in the anisotropy of turbulent eddies was also observed at the confinement bifurcation. Coexistence of local and non-local turbulence was identified in electron-scale turbulence. Non-local turbulence exhibited the rapid spatial propagation of perturbations throughout the plasma, while local turbulence followed the temperature gradient. A transition between drift-wave turbulence and magnetohydrodynamics (MHD) turbulence was observed with the turbulence minimized at the transition condition. Machine learning analysis was employed to evaluate the temperate and density conditions of this turbulence transition. Then, real-time control of fueling and heating was applied to maintain the turbulence transition condition, improving the energy confinement enhancement factor by 20%. In addition, evidence was obtained for collisionless ion heating by energetic-ion-driven geodesic acoustic modes and MHD bursts. These achievements represent unique contributions to the development of fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Collaborative Research: Fundamental Studies of Plasma Control Using Surface Embedded Electronic Devices

The research program was collaborative between the researchers at the University of Texas at Dallas and the University of Texas at Austin. The primary subject of this program was to investigate the possibility of active control of secondary electron emission (SEE) from surfaces in contact with plasmas and thereby actively control plasmas. Very few studies of ion-induced electron emission (IIEE) from semiconductors exist, and those that do exist primarily used high-energy ion beams in the experiments. Furthermore, those few studies took extreme measures to ensure that the measurements were performed on atomically clean surfaces because of the surface sensitivity of the IIEE process. Even a small exposure to air can change the IIEE yield significantly. In addition, much of the existing data for IIEE from semiconductors was obtained in the 1950s and ‘60s, when semiconductor materials were first being refined. As a result, nearly all of that data is for p-type Ge and Si. Before this investigation, experimental data on n-type materials was virtually non-existent. While the basic theory assumed that IIEE yields ought to be substantially independent of doping type and concentration, recent measurements of near atmospheric pressure plasmas and of breakdown suggested otherwise. These indirect measurements were made on surfaces that were not atomically clean and seemed to indicate that deep sub-surface changes to the bulk conduction band electron density could lead to substantial variations in the IIEE yield. Exactly in contradiction to the generally accepted theory. Insufficient direct data existed to settle the matter. We performed both experimental measurements and theoretical calculations of IIEE yields from both Si and Ge in order to help clarify whether or not conduction band electrons substantially change the IIEE yield. We used three wafers of each material to carry out the investigation: a heavily doped p-type, an intrinsic and a heavily doped n-type wafer. There was approximately a factor of 10 15 difference in the conduction band electron densities of the p-type and n-type Si wafers and a factor of 10 10 for Ge. We investigated semiconductor surfaces that were both chemically cleaned (not atomically clean) and sputter cleaned (much closer to atomically clean), since such measurements are more relevant to recent indirect measurements. In addition to IIEE measurements, X-ray and ultraviolet photoelectron spectroscopies (XPS and UPS) were utilized to better understand the results.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Parallelized real-time physics codes for plasma control on DIII-D

A real-time safe multi-threading library was developed on the DIII-D plasma control system to optimize the real-time TORBEAM and real-time STRIDE physics codes. These physics codes are crucial for future fusion power plant operation as they provide information about electron cyclotron wave propagation and heating as well as inform about ideal plasma stability limits. The real-time TORBEAM code executed consistently in under 20 ms while the real-time STRIDE code computes in 100 ms. The multi-threading library developed in this work can be applied to other real-time physics-based codes that will be crucial for the next generation of fusion devices.

DIII-D↗
DOE OSTI2024

Tailoring tokamak error fields to control plasma instabilities and transport

A tokamak relies on the axisymmetric magnetic fields to confine fusion plasmas and aims to deliver sustainable and clean energy. However, misalignments arise inevitably in the tokamak construction, leading to small asymmetries in the magnetic field known as error fields (EFs). The EFs have been a major concern in the tokamak approaches because small EFs, even less than 0.1%, can drive a plasma disruption. Meanwhile, the EFs in the tokamak can be favorably used for controlling plasma instabilities, such as edge-localized modes (ELMs). Here we show an optimization that tailors the EFs to maintain an edge 3D response for ELM control with a minimized core 3D response to avoid plasma disruption and unnecessary confinement degradation. We design and demonstrate such an edge-localized 3D response in the KSTAR facility, benefiting from its unique flexibility to change many degrees of freedom in the 3D coil space for the various fusion plasma regimes. This favorable control of the tokamak EF represents a notable advance for designing intrinsically 3D tokamaks to optimize stability and confinement for next-step fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems

Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.

AI-based models↗
DOE OSTI2024

Virtual Tokamak for Test and Development of Plasma Control Applied to NSTX-U

Safe and efficient operation of tokamak experiments depends on shot preparation prior to the experiment to maximize performance and avoid issues that are predictable consequences of known physics. The General Atomics TokSys toolbox is used to test and develop the plasma control system (PCS) on numerous tokamaks around the world. Tokamaks that use a version of the DIII-D PCS can connect it to a TokSys simulation and control a virtual version of the tokamak with the real PCS. Recent upgrades to the simulation include more detailed profile and transport modeling to support design and implementation of optimized scenarios. The simulation combines a module called “Profiles” that simulates the 1D profiles of density, pressure, and current and a module called “GSevolve'' that evolves the 2D Grad-Shafranov equilibrium. The “Profiles'' module primarily uses simplified models for fueling, heating, and current. The TRANSP code provides precomputed diffusion coefficients and heating/current drive profiles from radio-frequency and neutral beam sources for the scenarios being simulated. The fidelity of a simulation can be enhanced by analyzing it with TRANSP and making corrections to the precomputed values and iterating until convergence. Simulations of the National Spherical Torus Upgrade (NSTX-U) are presented. It is shown that the simulation reproduces the experimental profile evolution. Here, the simulation has also been upgraded to reproduce the experimental outcome at points of bifurcation. This is demonstrated with a simulation of vertical displacement in NSTX-U.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗