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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 OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Experimental characterization of discharge plasma dynamics in a square capillary for prospective applications in laser wakefield acceleration

Research on plasma generated by electrical discharge in gas-filled capillaries plays an important role in advancing laser wakefield accelerators. We present a comprehensive study of the temporal and spatial distribution of plasma density within a short, 1 cm long, square-shaped capillary filled with hydrogen gas. Two transverse capillary sizes, 500 μm and 300 μm, were investigated achieving peak plasma densities of 0.9 × 10 18 cm −3 and 2.5 × 10 18 cm −3 , respectively, at the capillary center. In addition, we explore how these distributions depend on discharge parameters, specifically discharge current and gas flow through the capillary. Plasma density was determined by analyzing the Stark broadening of the hydrogen Balmer-alpha line. The results obtained were compared with the theoretical models and simulations. The comparison between the model predictions and the experimental data at the transient ionization stage of the discharge reveals a discrepancy of a factor of ∼1.3–2.1, depending on the capillary size, which is thoroughly discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Pseudoatom molecular dynamics plasma microfields

Spectral lines are powerful diagnostic tools for both laboratory and astrophysical plasmas, as their shape is sensitive to the plasma environment. The low-frequency component of the electric microfield is an important input for semi-analytic line broadening codes. Here, in this paper, we detail a new method of calculating plasma microfields using configuration-resolved pseudoatom molecular dynamics. This approach accounts for both quantum atomic structure and N-body effects, similar to density functional theory molecular dynamics, but with less computational cost. We present pseudoatom microfields at plasma conditions relevant for recent high energy density laboratory astrophysics experiments conducted at the Sandia Z-Machine, National Ignition Facility, and Linac Coherent Light Source. Compared to established microfield codes we find moderate deviations at solid density conditions and strong agreement at lower plasma densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Advances in 3D transient plasma dynamics and control through MHD and hybrid fluid-kinetic simulations with JOREK

Transient phenomena and their control are of high relevance in magnetic confinement fusion plasmas to guarantee a stable and safe plasma operation. Interpretative simulations can maximize the insights gained from experiments on present machines and predictive simulations can help in the preparation of design, mitigation techniques and operational scenarios for future devices. In this article, we provide an overview of recent advances and novel scientific results obtained with the 3D non-linear hybrid fluid-kinetic code JOREK, covering physics of plasma transients from the core to the scrape-off layer (SOL) both for tokamak and stellarator devices. Substantial progress was made in the physics understanding, model validation with experiments and experiment interpretation, thus, giving confidence for predictions to devices like DTT, ITER and DEMO. The topics addressed comprise a wide range: the edge physics of new operation scenarios and edge localized mode suppression; major disruptions with a focus on runaway electrons and vertical displacement events as well as disruption mitigation by shattered pellet injection; the physics mechanisms and operational limits of the flux pumping regime for sawtooth control; MHD limits of stellarators and work towards incorporating advanced edge/SOL/exhaust dynamics; continuing improvements of the code for more efficient hybrid simulations on conventional and accelerated high performance computing architectures.

disruptions↗
DOE OSTI2026

Autoregressive long-horizon prediction of plasma edge dynamics *

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

autoregressive deep learning↗
DOE OSTI2025

Quantum Information for Fusion Energy Sciences (Final Technical Report)

The simulation of plasma dynamics is a critical area of Fusion Energy Sciences (FES) due to it’s usefulness in predicting, controlling, and confining plasmas in the context of potential fusion reactors. The simulation of plasmas is a computationally difficult problem in both classical and quantum physics, motivating investigation into the potential of quantum computers to simulate these systems. This project took several concrete steps towards this goal by developing tools for improving the control, characterization, and calibration of quantum gates on a superconducting quantum computer, developing error suppression and mitigation tools to reduce errors on the quantum computer, and utilizing these advancements to simulate reduced models of plasma dynamics on the quantum computer. In order to efficiently simulate plasma physics, an optimal control method which synthesizes, directly at the pulse level, any quantum gate on qubit and qutrit systems was developed. Using four superconducting transmon quantum processors at Rigetti and LLNL, it was demonstrated that any arbitrary quantum gate on qubits and qutrits could be implemented with high fidelity, leading to a significantly reduced length of a gate sequence. A problem of interest in FES is the nonlinear optical process of laser pulse compression within a plasma. Since quantum physics is linear, simulating nonlinear operations is not naturally feasible on a quantum computer, however it is possible to simulated a quantized version of the nonlinear process. A quantization approach to convert nonlinear wave-wave interaction problems to Hamiltonian simulation problems was developed and demonstrated using two qubits on a Rigetti device. In this experiment, a number of error suppression and mitigation techniques were investigated to determine how best to utilize the finite quantum resources. This study provides an example of how plasma problems may be solved on near-term, noisy quantum computing platforms and identified a promising set of techniques. Building on the insights of these experiments, the investigation turned to linear electron-plasma wave physics. A connection was identified between a local one-dimensional lattice spin model and linear wave phenomena, allowing a plasma physics problem to be efficiently mapped to the quantum computer. In this framework, reflection and transmission of plasma waves at a sharp boundary was studied, as well as the propagation of waves through an inhomogeneous plasma medium. In addition to the suite of error suppression and mitigation techniques developed, this experiment introduced the use of a digital-analog gate scheme designed to efficiently simulate the plasma Hamiltonian. With hardware available at the conclusion of the project, simulation at the scale of 9 qubits and 15 timesteps (60 entangling layers) was achieved.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Demonstration of full-scale spatiotemporal diagnostics of solid-density plasmas driven by an ultra-short relativistic laser pulse using an X-ray free-electron laser

Understanding the complex plasma dynamics in ultra-intense relativistic laser–solid interactions is of fundamental importance for applications of laser–plasma-based particle accelerators, the creation of high-energy-density matter, understanding planetary science, and laser-driven fusion energy. However, experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics. Over the last decade, the advent of femtosecond brilliant hard X-ray free-electron lasers (XFELs) has opened new horizons to overcome these limitations. Here, for the first time, we present full-scale spatiotemporal measurements of solid-density plasma dynamics, including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse, ultrafast heating and ionization at the main pulse arrival, the laser-driven blast wave, and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction. These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering, resonant X-ray emission spectroscopy, and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.

Huang, Lingen [Helmholtz-Zentrum Dresden-Rossendor↗
DOE OSTI2024

Quantum Information for Fusion Energy Sciences (Final Technical Report)

The simulation of plasma dynamics is a critical area of Fusion Energy Sciences (FES) due to it’s usefulness in predicting, controlling, and confining plasmas in the context of potential fusion reactors. The simulation of plasmas is a computationally difficult problem in both classical and quantum physics, motivating investigation into the potential of quantum computers to simulate these systems. This project took several concrete steps towards this goal by developing tools for improving the control, characterization, and calibration of quantum gates on a superconducting quantum computer, developing error suppression and mitigation tools to reduce errors on the quantum computer, and utilizing these advancements to simulate reduced models of plasma dynamics on the quantum computer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Developing ML/AI Methods for High-Throughput Characterization of Multiple-Sensor Streams of Tokamak Dynamics for High-Speed Control (Final Report)

This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Initial position optimization in molecular dynamics simulations for a Coulomb system

A new algorithm for molecular dynamics (MD) simulations is developed to optimize plasma particle distributions at given initial temperatures. By combining velocity scaling and reassignment, the method effectively eliminates the initial rise and oscillation in temperatures observed with randomly distributed positions. These rises and oscillations are undesired numerical artifacts observed in conventional plasma MD simulations, arising from unoptimized particle positions. The algorithm demonstrates temperature relaxation without initial rises or oscillations, as well as precise flow velocity relaxation, enabling accurate measurement of relaxation times. The code is accelerated using graphics processing units for parallel processing, enhancing the study of plasma dynamics. The proposed method for distributing physically valid particles in MD simulations enables accurate studies of intrinsic collision processes in plasmas, including the dynamics of strongly coupled plasmas, plasma–wave interactions, and transport phenomena in magnetized plasmas. The paper concludes with a discussion of potential applications and future enhancements to the algorithm.

Jo, Jawon (ORCID:0009000924193285)↗
DOE OSTI2025

Large radiation back-flux from Monte Carlo simulations of fusion neutron–material interactions

Abstract Fusion power reactors will generate intense neutron fluxes into plasma-facing and structural materials (SMs). Radiation back-fluxes, generated from neutron–material interactions under these fluxes, can dramatically impact the plasma dynamics, e.g. by seeding runaway electrons during disruptions via Compton scattering of background electrons by wall-emitted gamma radiation. Here, we quantify these back-fluxes, including neutrons, gamma rays, and electrons, using Monte Carlo calculations for a range of SM candidates and first wall (FW) thicknesses. The radiation back-flux magnitudes are remarkably large, with neutron and gamma radiation back-fluxes on the same order of magnitude as the incident fusion neutron flux. Electron back-fluxes are two orders of magnitudes lower, but are emitted at sufficiently high energies to impact the sheath and boundary plasma dynamics. Material configuration plays a key role in determining back-flux magnitudes. The SM chiefly determines the neutron back-flux magnitude, while the FW thickness principally attenuates the gamma ray and electron back-fluxes. In addition to prompt back-fluxes, which are emitted immediately after fusion neutrons impact the surface, significant delayed gamma ray and electron back-fluxes arise from nuclear decay processes in the activated materials. These delayed back-flux magnitudes range from 2% to 7% of the prompt back-fluxes, and remain present during transients when fusion no longer occurs. During disruptions, build-up of delayed gamma radiation back-flux represents potential runaway electron seeding mechanisms, posing additional challenges for disruption mitigation in a power reactor compared with non-nuclear plasma operations. This work highlights the impact of these radiation back-fluxes plasma performance and demonstrates the importance of considering back-flux generation in materials selection for fusion power reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

An implicit-explicit time splitting strategy for the far SOL plasma fluid model with DG-FEM discretization

We consider a far scrape-off layer (SOL) plasma fluid model of ions that is governed by a Braginskiitype model: a one-dimensional, nonlinear system of advection-diffusion equations coupled with a diffusion equation for neutral particles. Our motivation for studying this system arises from the coupling between the edge plasma and radio-frequency (RF) heating, where solving a far SOL plasma fluid model provides critical insights into edge plasma dynamics. Numerical simulations of plasma fluid models require advanced computational techniques to achieve both efficiency and accuracy, especially when resolving the boundary layer in magnetically confined plasmas. In this work, we propose an implicit-explicit time operator splitting strategy that allows for an efficient solution algorithm, where the diffusive terms are treated semi-implicitly requiring only a linear solve, while the advection part is handled explicitly using a strong-stability-preserving Runge-Kutta (SSP-RK3) scheme. This leads to a fully decoupled system in which the diffusion and advection sub-problems can be solved separately, simplifying the overall solution procedure and allowing for efficient parallelization, which is particularly relevant for exploring the impact of RF heating on the SOL plasma. The main challenge of the discretization is due to the strong coupling between diffusion and advection, particularly through the boundary conditions. This makes implementation of such a scheme in an accurate and stable manner nontrivial. We discuss in detail how to split the equations and manage boundary conditions to maintain stability and well-posedness for each subsystem. We also describe a spatial discretization approach, based on the discontinuous Galerkin finite element method (DG-FEM) and present numerical results for a one-dimensional system.

Burkovska, Olena [ORNL] (ORCID:0000000163101130)↗
DOE OSTI2025

Development of a wide bandwidth heterodyne dispersion interferometer for electron density measurement of atmospheric pressure plasmas

One of the challenges of electron density measurements of an atmospheric pressure plasma (APP) with a laser interferometer is the significant and unwanted phase shift caused by changes in the neutral gas density. These unwanted phase shifts can be mitigated and plasma density measured using an interferometer architecture called a dispersion interferometer (DI). A DI is composed of two nonlinear orientation patterned GaAs crystals for frequency doubling and measures the phase shift induced by a plasma in the interference signal between two second-harmonic beams. Measurement of plasma dynamics or a short plasma pulse in less than a millisecond is enabled with heterodyne detection in a DI using an acousto-optic cell with a frequency of 40 MHz. This heterodyne DI (HDI) is targeted to measure APPs in an electron density range of 10 20 –10 24 m −3 . Finally, the HDI achieves a line-integrated density resolution of 2 × 10 15 m −2 (a phase resolution of 0.005°) with a time constant of 1 μs using ensemble averaging techniques.

atmospheric pressure plasma↗
DOE OSTI2025

Testing strong-field QED with the avalanche precursor

A two-beam high-power laser facility is essential for the study of one of the most captivating phenomena predicted by strong-field quantum electrodynamics (QED) and yet unobserved experimentally: the avalanche-type cascade. In such a cascade, the energy of intense laser light can be efficiently transformed into high-energy radiation and electron–positron pairs. The future 50-petawatt-scale laser facility NSF OPAL will provide unique opportunities for studying such strong-field QED effects, as it is designed to deliver two ultra-intense, tightly focused laser pulses onto the interaction point. In this work, we investigate the potential of such a facility for studying elementary particle and plasma dynamics deeply in the quantum radiation-dominated (RD) regime, and the generation of QED avalanches. With three-dimensional particle-in-cell simulations, we demonstrate that QED avalanche precursors can be reliably triggered under realistic laser parameters and layout (namely, focusing f/2, tilted optical axes, and non-ideal co-pointing) with the anticipated capabilities of NSF OPAL. We demonstrate that seed electrons can be efficiently injected into the laser focus by using targets of three types: a gas of heavy atoms, an overcritical plasma, and a thin foil. A strong positron and high-energy photon signal is generated in all cases. The cascade properties can be identified from the final particle distributions, which have a clear directional pattern. At increasing laser field intensity, such distributions provide signatures of the transition, first, to the RD interaction regime, and then to a QED avalanche. Our findings can also be used for designing related future experiments.

Lasers↗
DOE OSTI2025

Century: Zap Energy’s 100-kW-Scale Repetitive Sheared-Flow-Stabilized Z -Pinch System with Liquid Metal Cooling

Zap Energy is developing the sheared-flow-stabilized (SFS) Z-pinch concept for commercial applications. The SFS Z pinch relies on plasma self-organization, in the sense that plasma dynamics play a critical role in confinement. Using plasma axial current for confinement and compression eliminates the need for external confinement or heating technologies. This compact magnetic confinement technology could, in turn, provide the basis for a cost-effective deuterium-tritium fusion power plant. In addition to a robust experimental program pushing plasma performance towards breakeven conditions, Zap Energy has parallel programs developing power handling systems suitable for future power plants. Technologies under development include high average-power repetitive pulsed power, high duty-cycle cathodes, and liquid metal wall systems. Century is the name of Zap Energy’s first effort to integrate these three components into an operational system capable of firing non-reacting hydrogen SFS Z-pinch plasmas into a liquid-metal-lined container at sustained repetition rates on the order of 0.1 Hz. Here, the pulsed power driver and liquid metal heat exchanger are both designed to sustain input powers of 100 kW. Construction and initial operations with an interim ~10 kW liquid metal heat exchanger are described.

Century↗