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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Design of a multi-detector, single line-of-sight, time-of-flight system to measure time-resolved neutron energy spectra

In the dynamic environment of burning, thermonuclear deuterium–tritium plasmas, diagnosing the time-resolved neutron energy spectrum is of critical importance. Strategies exist for this diagnosis in magnetic confinement fusion plasmas, which presently have a lifetime of ~10 12 longer than inertial confinement fusion (ICF) plasmas. In this work, we present a novel concept for a simple, precise, and scale-able diagnostic to measure time-resolved neutron spectra in ICF plasmas. The concept leverages general tomographic reconstruction techniques adapted to time-of-flight parameter space, and then employs an updated Monte Carlo algorithm and National Ignition Facility-relevant constraints to reconstruct the time-evolving neutron energy spectrum. Reconstructed spectra of the primary 14.028 MeV n DT peak are in good agreement with the exact synthetic spectra. The technique is also used to reconstruct the time-evolving downscattered spectrum, although the present implementation shows significantly more error.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2000

Coronal Diagnostics

Ultraviolet emission line ratios have long provided powerful diagnostics for the density, temperature, elemental abundances and ionization state of astrophysical plasmas. With the current generation of X-ray satellites, these techniques can be applied to X-ray spectra. It is important to remember that, any such line ratio provides an average value along the line of sight, and different diagnostics will provide differently weighted averages. Several assumptions are often made implicitly when emission line ratios are analysed. In particular the optical depth is generally assumed to be negligible. This paper considers some examples of the opportunities provided by photon scattering for new diagnostic tools, and it considers the dangers of applying the standard methods when the optical depth is significant.

Raymond, J. C.↗
DOE OSTI2025

Error field measurements with rotating RMP fields for DIII-D H-mode

3D magnetic sensors are employed to identify the amplitude and toroidal phase of error fields (EF) by analyzing the torque balance for magnetic islands entrained by rotating resonant magnetic perturbations (RMPs) in DIII-D H-mode plasmas. This technique of torque balance allows for efficient error field identification, offering a valuable tool for scenario-specific and optimized error field compensation (EFC) and requires only magnetic diagnostics. The torque balance used in this work includes the contribution from electromagnetic torque due to error fields, wall response, RMP fields, and viscous. Results show that viscous torque plays a crucial role, particularly during locked modes and H-mode plasmas, ensuring accurate data fits with lower residuals. The torque balance technique reveals that the L- and H-mode plasmas have distinct EF configurations, and consistent with a model-based EF analysis including MHD response in IPEC and the SURFMN EF simulation. This technique shows great robustness in measuring the intrinsic EF amplitude regardless of its amplitude or toroidal phase. Repeated discharges with EFC disparities exhibit consistent results of intrinsic error field within a reasonable range near the “standard” error field compensation. Additionally, the use of a rotating n = 1 resonant magnetic perturbation offers the advantage of reducing disruption risks by entraining saturated magnetic islands. These findings are instrumental for optimizing EF correction in fusion devices, thereby enhancing tearing mode suppression and overall plasma stability.

3D magnetic sensors↗
DOE OSTI2024

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Proton imaging of high-energy-density laboratory plasmas

Proton imaging has become a key diagnostic for measuring electromagnetic fields in high-energy-density (HED) laboratory plasmas. Compared to other techniques for diagnosing fields, proton imaging is a measurement that can simultaneously offer high spatial and temporal resolution and the ability to distinguish between electric and magnetic fields without the protons perturbing the plasma of interest. Consequently, proton imaging has been used in a wide range of HED experiments, from inertial-confinement fusion to laboratory astrophysics. An overview is provided on the state of the art of proton imaging, including a discussion of experimental considerations like proton sources and detectors, the theory of proton-imaging analysis, and a survey of experimental results demonstrating the breadth of applications. As a result, topics at the frontiers of proton-imaging development are also described, along with an outlook on the future of the field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1990

Diagnostic evaluations of microwave generated helium and nitrogen plasma mixtures

The goal of this work is to continue the development to fundamentally understand the plasma processes as applied to spacecraft propulsion. The diagnostic experiments used are calorimetric, dimensional, and spectroscopic measurements using the TM 011 and TM 012 modes in the resonance cavity. These experimental techniques are highly important in furthering the understanding of plasma phenomena and of designing rocket thrusters. Several experimental results are included using nitrogen and helium gas mixtures.

Haraburda, Scott S.↗
NASA NTRS1980

Plasma double-layers produced by ponderomotive force

Non-linear rectification of the RF electric fields excited in a convectively amplifying electron beam-plasma interaction results in a dc electric field, and a corresponding ponderomotive force acting on the charged particles to produce a space-charge separation, i.e. a type of plasma double-layer. This paper analyses the effect, taking into account plasma electron temperature and electron-neutral collisions. To measure such double-layers, an electron beam probe constitutes a convenient non-perturbing diagnostic technique, but great care is required in its calibration. An analysis is presented taking into account significant non-linear effects which had been neglected in previous work. The paper concludes with some preliminary experimental results illustrating the use of the technique in a beam-plasma interaction for which a weak double-layer electric field is predicted.

Levine, J. S.↗
NASA NTRS1981

Radiometry using synchrotron radiation

It is possible to use synchrotron radiation from electron synchrotrons and electron storage rings as an absolute source, especially in the VUV and soft X-ray regions where other standards are difficult to find. At the NBS, an electron storage ring (SURF-II) has been used to calibrate spectrometers and photometers utilized in solar and aeronomy research and in fusion plasma diagnostics. The radiation incident on these spectrometers can be calculated to uncertainties of 3%, and a technique to exactly determine the number of electrons orbiting in the ring is currently being developed to reduce this uncertainty. Detector calibrations between 5 and 55 nm are routinely performed at SURF-II and transfer standard detectors with 6-10% uncertainties over the range 5-254 nm are supplied.

Saloman, E. B.↗
DOE OSTI2023

Modeling of a spatially resolved ion temperature diagnostic for inertial confinement fusion

Here, the performance of modern laser-driven inertial confinement fusion (ICF) experiments is degraded by contamination of the deuterium–tritium (DT) fuel with high-Z material during compression. Simulations suggest that this mix can be described by the ion temperature distribution of the implosion, given that such contaminants deviate in temperature from the surrounding DT plasma. However, existing neutron time-of-flight (nTOF) diagnostics only measure the spatially integrated ion temperature. This paper describes the techniques and forward modeling used to develop a novel diagnostic imaging system to measure the spatially resolved ion temperature of an ICF implosion for the first time. The technique combines methods in neutron imaging and nTOF diagnostics to measure the ion temperature along one spatial dimension at yields currently achievable on the OMEGA laser. A detailed forward model of the source and imaging system was developed to guide instrument design. The model leverages neutron imaging reconstruction algorithms, radiation hydrodynamics and Monte Carlo simulations, optical ray tracing, and more. The results of the forward model agree with the data collected on OMEGA using the completed diagnostic. The analysis of the experimental data is still ongoing and will be discussed in a separate publication.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1999

Diagnostic for Plasma Enhanced Chemical Vapor Deposition and Etch Systems

In order to meet NASA's requirements for the rapid development and validation of future generation electronic devices as well as associated materials and processes, enabling technologies ion the processing of semiconductor materials arising from understanding etch chemistries are being developed through a research collaboration between Stanford University and NASA-Ames Research Center, Although a great deal of laboratory-scale research has been performed on many of materials processing plasmas, little is known about the gas-phase and surface chemical reactions that are critical in many etch and deposition processes, and how these reactions are influenced by the variation in operating conditions. In addition, many plasma-based processes suffer from stability and reliability problems leading to a compromise in performance and a potentially increased cost for the semiconductor manufacturing industry. Such a lack of understanding has hindered the development of process models that can aid in the scaling and improvement of plasma etch and deposition systems. The research described involves the study of plasmas used in semiconductor processes. An inductively coupled plasma (ICP) source in place of the standard upper electrode assembly of the Gaseous Electronics Conference (GEC) radio-frequency (RF) Reference Cell is used to investigate the discharge characteristics and chemistries. This ICP source generates plasmas with higher electron densities (approximately 10(exp 12)/cu cm) and lower operating pressures (approximately 7 mTorr) than obtainable with the original parallel-plate version of the GEC Cell. This expanded operating regime is more relevant to new generations of industrial plasma systems being used by the microelectronics industry. The motivation for this study is to develop an understanding of the physical phenomena involved in plasma processing and to measure much needed fundamental parameters, such as gas-phase and surface reaction rates. species concentration, temperature, ion energy distribution, and electron number density. A wide variety of diagnostic techniques are under development through this consortium grant to measure these parameters. including molecular beam mass spectrometry (MBMS). Fourier transform infrared (FTIR) spectroscopy, broadband ultraviolet (UV) absorption spectroscopy, a compensated Langmuir probe. Additional diagnostics. Such as microwave interferometry and microwave absorption for measurements of plasma density and radical concentrations are also planned.

Cappelli, Mark A.↗
DOE OSTI2021

Non-neutral plasma manipulation techniques in development of a high-capacity positron trap

Preliminary experiments have been performed toward the development of a multi-cell Penning–Malmberg trap for the storage of large numbers of positrons (≥10 10 e + ). We introduce the master-cell test trap and the diagnostic tools for first experiments with electrons. The usage of a phosphor screen to measure the z-integrated plasma distribution and the number of confined particles is demonstrated, as well as the trap alignment to the magnetic field ( B = 3.1 T) using the m = 1 diocotron mode. The plasma parameters and expansion are described along with the autoresonant excitation of the diocotron mode using rotating dipole fields and frequency chirped sinusoidal drive signals. We analyze the reproducibility of the excitation and use these findings to settle on the path for the next generation multi-cell test device.

Singer, M.↗
DOE OSTI2025

VERITAS : A density-functional theory-based multiband kinetic model for understanding x-ray spectroscopy of dense plasmas

X-ray spectroscopy has long been a powerful diagnostic tool for hot, dilute plasmas, providing insights into plasma conditions by measuring line shifts and broadenings of atomic transitions. The technique critically depends on the accuracy of atomic physics models used to interpret spectroscopic measurements for inferring plasma properties such as free-electron density and temperature. Over the past decades, the atomic and plasma physics communities have developed robust atomic physics models to account for various processes in hot, dilute classical plasmas. While these models have been successful in that regime, their applicability becomes uncertain when interpreting x-ray spectroscopy experiments of above-solid-density plasmas. Given that finite-temperature density-functional theory (DFT) offers a more accurate description of dense plasma environments, we present the development of a DFT-based multi-band kinetic model, VERITAS, designed to improve the interpretation of x-ray spectroscopic measurements in high-density plasmas produced by laser-driven spherical implosions. This work details the VERITAS model and its application to both time-integrated and time-resolved x-ray spectra from implosion experiments on OMEGA. The advantages and limitations of the VERITAS model will also be discussed, along with potential directions for advancing x-ray spectroscopy of dense and superdense 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 OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Structured light approaches in laser-based plasma diagnostics

There is a growing demand for plasma diagnostics suitable for industrial plasma reactors employed in semiconductor nanofabrication, especially relevant to microelectronics and quantum information systems. Such reactors typically have limited optical access and pose considerable diagnostic challenges, including intense background emission, significant thermal loads, and contamination of optical viewports. In this study, we outline research into structured light techniques (laser beams with tailored spatial, temporal, or phase characteristics) that effectively overcome these issues using laser-induced fluorescence (LIF) as an example. The focus of presented diagnostics is on ion kinetics analysis within an industrial plasma source, although this approach is broadly applicable to other plasma systems and diagnostic contexts. We present a confocal LIF implementation using an axicon-generated Bessel annular beam, achieving spatial resolutions of approximately 5 mm at a focal distance of 300 mm, with potential improvements to about 1 mm. This approach matches conventional orthogonal LIF performance but requires only one optical port. Wavelength-modulation LIF employs nonlinear laser wavelength tuning to measure spectral line derivatives, suppressing background emission and enhancing details of spectral line shape. Additionally, we present new results on applying vortex beams (laser beams carrying orbital angular momentum, OAM) for LIF measurements in an industrial plasma device. These measurements enable simultaneous axial and tangential velocity determination using a single laser beam and have been tested with xenon ion transition. Initial quantification of results was performed. Together, these structured-light approaches provide robust, background-resilient, multi-dimensional diagnostics for complex plasma environments.

Romadanov, Ivan [Princeton Plasma Physics Laborato↗
DOE OSTI2024

Versatile multi-energy hard x-ray camera to study confined and unconfined fast electron dynamics and anisotropies (Invited)

A powerful and flexible hard x-ray (HXR) camera has been recently installed and tested on the WEST tokamak (CEA, France) in collaboration with the Princeton Plasma Physics Laboratory. The diagnostic is a pinhole camera fielded with a 2D pixel detector equipped with a 1 mm thick CdTe sensor. The novelty of this diagnostic technique is the detector’s capability of adjusting the threshold energy at the pixel level. This innovation provides great flexibility in the energy configuration, allowing simultaneous space, energy, and time resolved x-ray measurements. The novel camera has been used to measure the core radiation from non-Maxwellian (fast) electrons accelerated by Lower Hybrid (LH) waves and also the beam–target emission of tungsten in the divertor region produced by fast electron losses interacting with the target. In addition, anisotropic hard x-ray emission has been detected for the first time at the WEST core and edge plasma, with opposite toroidal intensity trends. Finally, experimental vertical and toroidal HXR profiles have been successfully reproduced with the LH code LUKE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Applications of machine learning to a compact magnetic spectrometer for high repetition rate, laser-driven particle acceleration

Accurately and rapidly diagnosing laser–plasma interactions is often difficult due to the time-intensive nature of the analysis and will only become more so with the rise of high repetition rate lasers and the desire to implement feedback on a commensurate timescale. Diagnostic analysis employing machine learning techniques can help address this problem while maintaining a high degree of accuracy. We report on the application of machine learning to the analysis of a scintillator-based electron spectrometer for experiments on high intensity, laser–plasma interactions at the Colorado State University Advanced Lasers and Extreme Photonics facility. Our approach utilizes a neural network trained on synthetic data and tested on experiments to extract the accelerated electron temperature. By leveraging transfer learning, we demonstrate an improvement in the neural network accuracy, decreasing the network error by 50%.

Swanson, K. K. (ORCID:0000000286880568)↗