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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Efficient Optimization of Plasma Radiation Detector Configurations using Imperfect Inference Models

The configurations of instruments fielded on an experiment affect the amount of information captured and the quality of subsequent inference. Here, we investigate the problem of optimizing plasma x-ray radiation detectors in a magneto-inertial fusion experiment at Sandia National Laboratories. It is impossible to directly measure properties such as the temperature of the thermonuclear fusion plasma produced in these experiments because of the extreme environment and destructive nature of the experiment. Among other diagnostics, several detectors are placed with significant standoff from the fusion target to capture the x-rays emitted by the fusion plasma, which can be used to infer some of its properties. To optimize the configuration of these detectors, a high-fidelity model (HFM) is used for simulating outputs and a low-fidelity model (LFM) is used for inference. We develop methods based on A- and L-optimality criteria that are efficient to compute while explicitly accounting for the discrepancy between the HFM and the LFM. The method allows us to find detector configurations that perform similarly to or better than the configuration obtained using an existing sampling-based optimization method while decreasing computational time by a factor of 50. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian optimization↗
DOE OSTI2021

Magnetic field effects on laser energy deposition and filamentation in magneto-inertial fusion relevant plasmas

We report on experimental measurements of how an externally imposed magnetic field affects plasma heating by kJ-class, nanosecond laser pulses. The experiments reported here took place in gas cells analogous to magnetized liner inertial fusion targets. We observed significant changes in laser propagation and energy deposition scale lengths when a 12T external magnetic field was imposed in the gas cell. We find evidence that the axial magnetic field reduces radial electron thermal transport, narrows the width of the heated plasma, and increases the axial plasma length. Reduced thermal conductivity increases radial thermal gradients. This enhances radial hydrodynamic expansion and subsequent thermal self-focusing. Our experiments and supporting 3D simulations in helium demonstrate that magnetization leads to higher thermal gradients, higher peak temperatures, more rapid blast wave development, and beam focusing with an applied field of 12T.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Direct Experimental Proof of the Principal Role of Reduced High-Mode Hydrodynamic Mix in Recent Ignition Success on NIF

This Letter details the role of reduced hydrodynamic mix in achieving the first fusion plasma to satisfy the Lawson criterion for ignition. Using novel 3D measurements of the temperature and density of the burning deuterium-tritium plasma across a series of experiments, we demonstrate that hydrodynamic mixing from small-scale capsule defects was a key degradation mechanism inhibiting ignition. A series of five layered deuterium-tritium experiments using 1.9 MJ of laser energy demonstrates a 2 × reduction of the thermonuclear yield when a lower quality capsule, typical of preignition experiments, is used. Radiation hydrodynamic calculations consistent with the observed mix profile show that the mixed fuel’s reduced compression and increased radiative loss explain the yield reduction. Furthermore, a hydroscaling argument suggests that a ∼ 3 MJ laser would be needed to reach ignition with such a capsule, well beyond the current capabilities of high energy laser systems. As such, this Letter gives critical insight into the physics of mix in self-heated fusion plasmas and establishes a foundation to evaluate capsule quality needs of future inertial fusion designs that aim at ignition and high gain.

High-energy-density plasmas↗
DOE OSTI2021

Suppression of first-wall interaction in negative triangularity plasmas on TCV

Magnetically confined fusion plasmas with negative triangularity (δ) exhibit greater L-mode confinement than with positive δ. Recent experiments in the TCV and DIII-D tokamaks have correlated the confinement improvement to a reduction of fluctuations within the plasma core. We report on fluctuation measurements in the scrape-off layer (SOL) for -0.61 < δ < +0.64 in limited and diverted ohmic L-mode plasmas; these reveal a strong reduction in SOL fluctuation amplitudes at δ < -0.25, and, surprisingly, an almost full suppression of plasma interaction with the main-chamber first-wall, which could have important implications for the prospects of using negative δ plasmas as a reactor solution. An exploration of several physical mechanisms suggests that a reduced connection length—intrinsic to negative δ plasmas—plays a critical role in the origin of this phenomenon.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Suppression of first-wall interaction in negative triangularity plasmas on TCV

Magnetically confined fusion plasmas with negative triangularity (d) exhibit greater L-mode confinement than with positive d. Recent experiments in the TCV and DIII-D tokamaks have correlated the confinement improvement to a reduction of fluctuations within the plasma core. We report on fluctuation measurements in the scrape-off layer (SOL) for −0.61 < d < +0.64 in limited and diverted ohmic L-mode plasmas; these reveal a strong reduction in SOL fluctuation amplitudes at d < −0.25, and, surprisingly, an almost full suppression of plasma interaction with the main-chamber first-wall, which could have important implications for the prospects of using negative d plasmas as a reactor solution. An exploration of several physical mechanisms suggests that a reduced connection length—intrinsic to negative d plasmas—plays a critical role in the origin of this phenomenon.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.

convolutional neural networks↗
DOE OSTI2023

Shifting and Splitting of Resonance Lines due to Dynamical Friction in Plasmas

We report a quasilinear plasma transport theory that incorporates Fokker-Planck dynamical friction (drag) and pitch angle scattering is self-consistently derived from first principles for an isolated, marginally unstable mode resonating with an energetic minority species. It is found that drag fundamentally changes the structure of the wave-particle resonance, breaking its symmetry and leading to the shifting and splitting of resonance lines. In contrast, scattering broadens the resonance in a symmetric fashion. Comparison with fully nonlinear simulations shows that the proposed quasilinear system preserves the exact instability saturation amplitude and the corresponding particle redistribution of the fully nonlinear theory. Even in situations in which drag leads to a relatively small resonance shift, it still underpins major changes in the redistribution of resonant particles. This novel influence of drag is equally important in plasmas and gravitational systems. In fusion plasmas, the effects are especially pronounced for fast-ion-driven instabilities in tokamaks with low aspect ratio or negative triangularity, as evidenced by past observations. The same theory directly maps to the resonant dynamics of the rotating galactic bar and massive bodies in its orbit, providing new techniques for analyzing galactic dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Scrape-off layer stability, turbulence and transport in MAST-U (Final Report)

U.S. Department of Energy (DOE) supported work at Lodestar Research Corporation under grant DE-SC0019270 is summarized in this final report. The research conducted under this grant explored the properties of the edge plasma in spherical torus magnetic fusion devices. Harnessing the energy that is released in fusion reactions would provide a safe and abundant source of power to meet the growing energy needs of the world population. The edge and scrape-off layer (SOL) region of fusion plasmas is the region where the hot core plasma transitions to a cooler surrounding boundary plasma. The properties of this boundary plasma determine how interactions with material surfaces will impact both the overall device performance and the lifetime of crucial components such as the walls of the device chamber and other hardware. The research carried out studied filamentary plasma instabilities and turbulent transport in the edge and SOL of the MAST U spherical torus.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Final Technical Report for the SciDAC Center for the Study of Plasma Microturbulence

This final technical report for the SciDAC Center for the Study of Plasma Microturbulence documents contributions from MIT to the project over the period of the award (February 15, 2011 to February 14, 2020). This project focused on the development of neoclassical and gyrokinetic simulation capabilities relevant for the description of particle, momentum and energy transport in tokamak fusion plasmas. Extensive validation against experimental results from Alcator C-Mod and DIII-D experiments was performed. Overall areas of emphasis included gyrokinetic simulations, experiments, and observation of density gradient driven Trapped Electron Mode (TEM) turbulence relevant to burning plasmas; theory and gyrokinetic simulations of the parametric variation of the nonlinear upshift of the TEM critical density gradient; identification of a new mechanism for the isotope effect due to density gradient driven TEM turbulence; new synthetic diagnostics based on full-wave simulations which enabled unprecedented agreement between gyrokinetic simulations and measured fluctuation spectra with and without strong electron heating; development of new continuum pedestal neoclassical and turbulence codes; formulation and implementation of the first gyrokinetic exact Fokker-Planck collision operator in gyrokinetic turbulence simulations; development of new spectral approaches in velocity space for highly efficient treatments of collisions; new model collision operators implemented in the discontinuous Galerkin gyrokinetic edge turbulence code GKEYLL; and a new model and spectral code to study the interaction of ion- and electron-scale turbulence, which will serve as an efficient test-bed for multi-rate and multi-scale methods. These fundamental advances contribute to the development of a validated, predictive capability for turbulent and collisional particle, momentum and energy transport in both the core and edge of magnetically confined fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Dynamics and Power Balance of Near Unity Target Gain Inertial Confinement Fusion Implosions

The change in the power balance, temporal dynamics, emission weighted size, temperature, mass, and areal density of inertially confined fusion plasmas have been quantified for experiments that reach target gains up to 0.72. It is observed that as the target gain rises, increased rates of self-heating initially overcome expansion power losses. This leads to reacting plasmas that reach peak fusion production at later times with increased size, temperature, mass and with lower emission weighted areal densities. Analytic models are consistent with the observations and inferences for how these quantities evolve as the rate of fusion self-heating, fusion yield, and target gain increase. At peak fusion production, it is found that as temperatures and target gains rise, the expansion power loss increases to a near constant ratio of the fusion self-heating power. Finally, this is consistent with models that indicate that the expansion losses dominate the dynamics in this regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Implementation of higher-order velocity mapping between marker particles and grid in the particle-in-cell code XGC

The global total-f gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas or to couple with a core gyrokinetic code while functioning as an edge gyrokinetic code, implements a five-dimensional continuum grid to perform the dissipative operations, such as plasma collisions, or to exchange the particle distribution function information with a core code. To transfer the distribution function between marker particles and a rectangular two-dimensional velocity-space grid, XGC employs a bilinear mapping. The conservation of particle density and momentum is accurate enough in this bilinear operation, but the error in the particle energy conservation can become undesirably large and cause non-negligible numerical heating in a steep edge pedestal. In the present work we update XGC to use a novel mapping technique, based on the calculation of a pseudo-inverse, to exactly preserve moments up to the order of the discretization space. Here we describe the details of the implementation and we demonstrate the reduced interpolation error for a tokamak test plasma using first- and second-order elements with the pseudo-inverse method and comparing with the bilinear mapping.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A physics-constrained deep learning surrogate model of the runaway electron avalanche growth rate

A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic Fokker–Planck equation. The resulting PINN is able to evaluate the runaway probability function across a broad range of parameters in the absence of any synthetic or experimental data. This surrogate of the adjoint relativistic Fokker–Planck equation is then used to infer the avalanche growth rate as a function of the electric field, synchrotron radiation and effective charge. Predictions of the avalanche PINN are compared against first principle calculations of the avalanche growth rate with excellent agreement observed across a broad range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Three-dimensional electron temperature measurement of inertial confinement fusion hotspots using x-ray emission tomography

Here we present a novel approach to reconstruct three-dimensional (3D) electron temperature distributions of inertially confined fusion plasma hotspots at the National Ignition Facility. Using very limited number of two-dimensional (2D) x-ray imaging lines of sight, we perform 3D reconstructions of x-ray emission distributions from different x-ray energy channels ranging from 20 to 30 keV. 2D time-integrated x-ray images are processed using the algebraic reconstruction technique to reconstruct a 3D hotspot x-ray emission distribution that is self-consistent with the input images. 3D electron temperatures are computed using the energy channel ratios. We demonstrate the high accuracy and applicability of this method with different complex hotspot geometries in both synthetic and experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Dependence of high- Z redeposition on the field-to-surface pitch angle and other sheath parameters in tokamaks

Accurately predicting redeposition is vital for high-Z plasma-facing component (PFC) survivability in magnetic confinement fusion. In this study, we categorize high-Z redeposition into three mechanisms: geometric-driven (prompt), sheath-driven (local), and scrape-off-layer-driven (far) redeposition. To investigate these mechanisms, we employ Monte Carlo transport codes to simulate azimuthally symmetric tungsten source erosion and redeposition in a tokamak. By iteratively analyzing critical parameters, we evaluate redeposition scaling for each mechanism. Specifically, we investigate the impact of magnetic-field-to-PFC pitch angle assumptions on PFC losses into the scrape-off layer. Our findings reveal significant pitch angle sensitivity due to an asymmetric prompt vs local redeposition trade-off. These results enhance our understanding of redeposition phenomena in fusion plasma environments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Reversal of Simple Hydrogenic Isotope Scaling Laws in Tokamak Edge Turbulence

The role of nonadiabatic electrons in regulating the hydrogenic isotope-mass scaling of gyrokinetic turbulence in tokamak fusion plasmas is assessed in the transition from ion-dominated core transport regimes to electron-dominated edge transport regimes. We propose a new isotope-mass scaling law that describes the electron-to-ion mass-ratio dependence of turbulent ion and electron energy fluxes. Here, the massratio dependence arises from the nonadiabatic response associated with fast electron parallel motion and plays a key role in altering—and in the case of the DIII-D edge, favorably reversing—the naive gyro-Bohm scaling behavior. In the reversed regime hydrogen energy fluxes are larger than deuterium fluxes, which is the opposite of the naive prediction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

MAPS: the MFEM Anisotropic Plasma Solver

Simulating magnetically confined fusion plasmas presents a uniquely challenging problem due to the nonlinear anisotropic heat conduction. We introduce the MAPS (MFEM Anisotropic Plasma Solver) tool, which uses a high-order finite element method to compute transport solutions on unstructured meshes. We show results for a set of three 2-D verification tests, two of which demonstrate the expected convergence properties for various mesh resolutions and polynomial degrees. We then discuss the convergence rate for the third test.

Barnett, Rhea [ORNL] (ORCID:0000000317527979)↗