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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating nonlocal heat transport in directly driven chromium spheres using x-ray spectroscopy

We report on experiments investigating heat transport in laser-generated plasmas using directly driven chromium spheres. The spheres are fielded at the OMEGA laser facility and are driven with laser intensities of 5×10 14 Wcm −2 . Plasma conditions in the corona and scattered light are measured experimentally and compared against predictions from two-dimensional (2D) radiation-hydrodynamic simulations using different heat transport models. Spectroscopic analysis of x-ray self-emission is used as an additional diagnostic. X-ray emission is integrated over a large region of the plasma, probing regions that are not observed by localized optical Thomson scattering. In particular, x-ray emission peaks near the plasma critical density, so emission from optically thin lines provides information on plasma conditions where nonlocal transport is most likely to be significant. Three common heat transport models are considered: local transport with flux limiters f = 0.15 and f = 0.03, and the nonlocal Schurtz–Nicolai–Busquet (SNB) model. Consistent with previous work, both the high-flux (f = 0.15) and SNB models show good agreement with experimentally measured plasma conditions in the corona despite overpredicting laser absorption, whereas the low-flux (f = 0.03) model fails to match any experimental data. Conditions inferred from x-ray self-emission line ratios support this conclusion during the period of laser peak power, although synthetic spectra for all models fail to match the experiment during the transient portions of the pulse. For these reasons, the low-flux model is again rejected.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fermilab Booster loss modelling and rebalancing using Bayesian methods

Fermilab Booster is being upgraded for the PIP-II project to support 20Hz ramp rate at higher intensities. Loss trip limits determine the achievable peak power. To meet PIP-II requirements, losses need to be halved as compared to current levels. Losses primarily occur at injection and transition crossing, with both gradually increasing and threshold-like intensity-dependent behaviors. The existing simulation models are not yet good enough for quantitative loss predictions. In practice, it will be necessary to tune up the Booster using iterative methods and operator intuition. In this paper we present an effort to systematically model Booster losses using active learning (Bayesian exploration) techniques, and subsequently to rebalance them for higher trip limit margins. We first created several sets of spatially and temporally isolated orbit and optics knobs, and trained Gaussian process models for each beam loss monitor as well as beam current. This is a complex task due to safety and timing requirements – we discuss mitigations such as uncertainty constraints and approximate fitting. Once models are stable, we perform large-scale single and multi-objective tuning using scalarized objectives made up of critical beam loss locations. Our results demonstrate significant rebalancing of losses, increasing trip margins, as well as an overall improvement in beam transmission efficiency. We are exploring how to combine existing simulations with experimental data and automate the collection procedure so that more advanced surrogate models can be created over time.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2026

Qudit Designs and Where to Find Them

Unitary t-designs are some of the most versatile tools in quantum information theory. Their applications range from randomized benchmarking and shadow tomography, to more fundamental ones such as emulating quantum chaos and establishing exponential separations between classical and quantum query complexity. While unitary designs originating from a group structure, such as the Clifford group, have proven to be incredibly useful for qubit systems, unfortunately, this is no longer true for qudits. In fact, the classification of finite-group representations rules out the existence of unitary 2-designs for arbitrary qudit dimensions. This severely limits the applicability of standard quantum information primitives when it comes to qudit systems. We overcome these limitations with a three-fold contribution. First, we introduce a general technique to construct families of weighted state t-designs in arbitrary qudit dimensions. These weighted state-designs generalize classical shadow tomography protocol from qubits to qudits. Second, we introduce a Clifford character RB that allows us to benchmark the qudit Clifford group in any dimension, including non-prime-power dimensions. And third, we establish bounds on the quantum circuit complexity of generating approximate unitary-designs from native gates in existing quantum hardware such as high-spin and cavity-QED qudits. Our work further highlights the analogy between spin and optical coherent states by proving that spin-GKP codewords form a state 2-design while spin coherent states do not; in direct analogy with the optical case. This work is structured as a pedagogical and self-contained introduction to unitary designs and their applications to qudit systems.

Anand, Namit [NASA, Ames; Unlisted, US] (ORCID:000↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
DOE OSTI2022

A fiber-coupled dispersion interferometer for density measurements of pulsed power transmission line electron sheaths on Sandia’s Z machine

A fiber-coupled Dispersion Interferometer (DI) is being developed to measure the electron density of plasmas formed in power flow regions, such as magnetically insulated transmission lines, on Sandia National Laboratories (SNL’s) Z machine [D. B. Sinars et al., Phys. Plasmas 27, 070501 (2020)]. The diagnostic operates using a fiber-coupled 1550 nm CW laser with frequency-doubling to 775 nm. The DI is expected to be capable of line-average density measurements between ∼1013 and 1019 cm−2. Initial testing has been performed on a well-characterized RF lab plasma [A. G. Lynn et al., Rev. Sci. Instrum. 80, 103501 (2009)] at the University of New Mexico to quantify the density resolution lower limits of the DI. Initial testing of the DI has demonstrated line-average electron density measurements within 9% of results acquired via a 94 GHz mm wave interferometer for line densities of ∼1 × 1014 cm−2, despite significant differences in probe beam geometries. The instrument will next be utilized for measurements on a ∼1 MA-scale pulsed power driver {MYKONOS [N. Bennett et al., Phys. Rev. Accel. Beams 22, 120401 (2019)] at SNL} before finally being deployed on SNL’s Z machine. The close electrode spacing (mm scale) on Z requires probe beam sizes of ∼1 mm, which can only be obtained with visible or near infrared optical systems, as opposed to longer wavelength mm wave systems that would normally be chosen for this range of density.

Hines, N. R. (ORCID:0000000315316465)↗
DOE OSTI2020

Spin–orbit torque switching of a ferromagnet with picosecond electrical pulses

The development of approaches that can efficiently control the magnetization of magnetic materials is central to the creation of fast and low-power spintronic devices. Spin transfer torque can be used to electrically manipulate magnetic order in devices, but is typically limited to nanosecond timescales. Alternatively, spin–orbit torque can be employed, and switching with current pulses down to ~200 ps has been demonstrated. However, the upper limit to magnetization switching speed remains unestablished. Here, we show that photoconductive switches can be used to apply 6-ps-wide electrical pulses and deterministically switch the out-of-plane magnetization of a common thin cobalt film via spin–orbit torque. We probe the ultrafast magnetization dynamics due to spin–orbit torques with sub-picosecond resolution using the time-resolved magneto-optical Kerr effect (MOKE). We also estimate that the magnetization switching consumes less than 50 pJ in micrometre-sized devices.

42 ENGINEERING↗
DOE OSTI2025

Efficient on-chip terahertz generation and detection with GaN photoconductive emitters

Photoconductive emitters for terahertz generation hold promise for highly efficient down-conversion of optical photons because it is not constrained by the Manley-Rowe relation. Existing terahertz photoconductive devices, however, faces limits in efficiency due to the semiconductor properties of commonly used GaAs materials. Here, we demonstrate that large bandgap semiconductor GaN, characterized by its high breakdown electric field, facilitates the highly efficient generation of terahertz waves in a coplanar stripline waveguide. Towards this goal, we investigated the excitonic contribution to the electro-optic response of GaN under static electric field both through experiments and first-principles calculations, revealing a robust excitonic Stark shift. Using this electro-optic effect, we developed a novel ultraviolet pump-probe spectroscopy for in-situ characterization of the terahertz electric field strength generated by the GaN photoconductive emitter. Our findings show that terahertz power scales quadratically with optical excitation power and applied electric field over a broad parameter range. We achieved an optical-to-terahertz conversion efficiency approaching 100% within the 0.03–1 THz bandwidth at the highest bias field (116 kV/cm) in our experiment. Further optimization of GaN-based terahertz generation devices could achieve even greater optical-to-terahertz conversion efficiencies.

Uzundal, Can B↗
DOE OSTI2024

High energy electron diffraction instrument with tunable camera length

Ultrafast electron diffraction (UED) stands as a powerful technique for real-time observation of structural dynamics at the atomic level. In recent years, the use of MeV electrons from radio frequency guns has been widely adopted to take advantage of the relativistic suppression of the space charge effects that otherwise limit the temporal resolution of the technique. Nevertheless, there is not a clear choice for the optimal energy for a UED instrument. Scaling to beam energies higher than a few MeV does pose significant technical challenges, mainly related to the inherent increase in diffraction camera length associated with the smaller Bragg angles. In this study, we report a solution by using a compact post-sample magnetic optical system to magnify the diffraction pattern from a crystal Au sample illuminated by an 8.2 MeV electron beam. Our method employs, as one of the lenses of the optical system, a triplet of compact, high field gradients (>500 T/m), small-gap (3.5 mm) Halbach permanent magnet quadrupoles. Shifting the relative position of the quadrupoles, we demonstrate tuning the magnification by more than a factor of two, a 6× improvement in camera length, and reciprocal space resolution better than 0.1 Å –1 in agreement with beam transport simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Advancing extreme pulsed lasers through measurements: summary of the pulsed laser metrology workshop

In this report we present consensus findings produced during the pulsed laser metrology (PLM) workshop (Boulder, CO, USA, August 2025). The workshop was a meeting of 35 scientists from private industry, academia, and U.S. government to compile a list of the metrology needs to support extreme pulsed laser development and implementation. We define ‘extreme pulsed lasers’ loosely as those with peak powers above 1 TW or multi-Joule pulse energies. Such lasers are being increasingly used and investigated for use in high energy particle acceleration, short wavelength radiation generation, laser-based fusion, etc. The rapid growth of these fields and the proliferation of extreme lasers for these applications, coupled with the extreme electric fields they generate and relative scarcity of such lasers, presents unique challenges to accuracy in their measurement. The PLM workshop was a two-day event designed to hear from users, developers, and researchers on the current state of optical metrology associated with these extreme pulsed lasers. The goal was to identify the most important measurement challenges limiting development and implementation of these lasers in practical applications. We report here the conclusions and recommendations of this workshop. The hope is that this document will become the first of many iterations which track the status of measurement needs supporting extreme pulsed lasers.

Lasers↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

3D Printing of Inconel 718 with Enhanced Boron Composition as a Novel Solar Absorber Tube Material in the Concentrated Solar Power (CSP) System

The growing demands for elevated efficiency in the solar energy industry led researchers to focus on the development of functional solar absorber tube material in concentrated solar power (CSP) systems, when molten salts are adopted as the heat transfer fluid. In this study, the typical solar absorber tube material, Inconel 718, was enhanced with boron to achieve a higher solar absorptivity in the visible light spectrum. Combined with an additive manufacturing (AM) method, the boron composition exceeded the traditional manufacturing limit of 60 ppm without microstructural defects. The boron-enhanced Inconel 718 exhibited a high solar absorptivity of nearly 90 % while maintaining a high thermal cycle fatigue resistance after thermal cycling treatment between 550°C and 720°C. The boron composition was increased to the manufacturing failure point, and the effects of different boron compositions on mechanical properties, microstructure, and optical properties were studied. The provided microstructure-property map in this study delivers high potentials of functional AM-printed alloy material in CSP applications.

13 HYDRO ENERGY↗
DOE OSTI2021

Passive Low-Energy Nuclear-Recoil Detection with Color Centers

Crystal-damage events such as tracks and point defects have been used to record and detect radiation for a long time and recently they have been proposed as a means for dark-matter detection. Color centers can be read out optically and we propose a scheme based on selective plane illumination microscopy for micrometer-scale imaging of large volumes corresponding to kilogram mass detectors. This class of detectors would be passive and would operate at room temperature and we call this the passive low-energy optical color-center nuclear-recoil (PALEOCCENE) detection method. We apply these concepts to the detection of reactor neutrinos using coherent elastic neutrino nucleus scattering (CE ν NS). Crystal-damage formation energies are intrinsically on the order of 25 eV, resulting in similarly low nuclear-recoil thresholds. Furthermore, this would enable the observation of reactor-neutrino CE ν NS with detectors as small as 10 g. Additionally, a competitive search for spin-dependent dark-matter scattering down to a dark-matter mass of 0.3 GeV could be possible. Passive crystal detectors might also be attractive for nuclear-nonproliferation safeguards if used to monitor reactor power and to put limits on plutonium production. The passive nature and small footprint of the proposed detectors implies that these might fit well within accepted reactor-safeguards operations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Transitions between electron emission and gas breakdown mechanisms across length and pressure scales

This Perspective outlines theoretical, simulation, and experimental studies linking electron emission and gas breakdown. Many studies have investigated field emission-driven microscale gas breakdown, including recent reviews [Garner et al., IEEE Trans. Plasma Sci. 48, 808–824 (2020); Fu et al., Plasma Res. Express 2, 013001 (2020)]. This Perspective focuses on generalizing field emission-driven microscale gas breakdown to consider the contribution of other forms of electron emission, specifically thermionic and space-charge limited. Recent theoretical studies have unified thermionic, field, and space-charge limited emission with and without collisions to derive “nexuses” where the individual solutions match, indicating transitions in the mechanisms. Reducing device size to nanoscale at atmospheric pressure leads to a transition from field emission to space-charge limited emission for nitrogen at ~250 nm. This Perspective summarizes the derivation of these nexuses and future extensions. We next describe simulation and theoretical studies for field emission-driven microscale gas breakdown and highlight how the nexus theory may be integrated to account for temperature, space-charge, and pulse parameters. Finally, we summarize the development of optical techniques to assess microscale gas breakdown and recent nanoscale experiments at atmospheric pressure that suggest that space-charge may begin to contribute to field emission prior to gas breakdown. We highlight the combination of theory, simulation, and experiment to link electron emission and gas breakdown mechanisms across length, pressure, and temperature scales for applications that include vacuum electronics, pulsed power, and medicine.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Coupled surface plasmon–phonon polariton nanocavity arrays for enhanced mid-infrared absorption

Abstract Resonant optical cavities are essential components in mid-infrared applications. However, typical film-type cavities require multilayer stacks with a micron-thick spacer due to mid-infrared wavelengths, and their performance is limited by narrow frequency tunability and angular sensitivity. We propose and experimentally demonstrate the subwavelength-scale (≈ λ 0 /150) resonant nanocavity arrays that enhance the absorption spectrum of the device in the mid-infrared (10–12 microns) via excitation of coupled surface plasmon–phonon polaritons. The proposed metal–insulator–polar dielectric (gold–silicon–silicon carbide) structure supports a guided mode of the coupled surface polaritons in the lateral direction while vertically confining the mid-infrared wave within the 80 nm thick dielectric spacer. In particular, the subwavelength-scale (≈ λ 0 /10) gratings are imposed to form Fabry–Pérot cavity arrays displaying angle-insensitive and frequency-tunable absorption of up to 80% of the optical power in the mid-infrared. Our work should benefit diverse mid-infrared applications and novel designs of polariton-based photonic devices.

42 ENGINEERING↗
DOE OSTI2021

High-performance photonic transformers for DC voltage conversion

Abstract Direct current (DC) converters play an essential role in electronic circuits. Conventional high-efficiency DC voltage converters, especially step-up type, rely on switching operation, where energy is periodically stored within and released from inductors and/or capacitors connected in a variety of circuit topologies. Since these energy storage components, especially inductors, are fundamentally difficult to scale down, miniaturization of switching converters proves challenging. Furthermore, the resulting switching currents produce significant electromagnetic noise. To overcome the limitations of switching converters, photonic transformers, where voltage conversion is achieved through light emission and detection processes, have been demonstrated. However, the demonstrated efficiency is significantly below that of the switching converter. Here we perform a detailed balance analysis and show that with a monolithically integrated design that enables efficient photon transport, the photonic transformer can operate with a near-unity conversion efficiency and high voltage conversion ratio. We validate the theory with a transformer constructed with off-the-shelf discrete components. Our experiment showcases near noiseless operation and a voltage conversion ratio that is significantly higher than obtained in previous photonic transformers. Our findings point to the possibility of a high-performance optical solution to miniaturizing DC power converters and improving the electromagnetic compatibility and quality of electrical power.

42 ENGINEERING↗
DOE OSTI2021

Simulating highly eccentric common envelope jet supernova impostors

We conduct three-dimensional hydrodynamical simulations of eccentric common envelope jet supernova (CEJSN) impostors, i.e. a neutron star that crosses through the envelope of a red supergiant star on a highly eccentric orbit and launches jets as it accretes mass from the envelope. Because of numerical limitations, we apply a simple prescription where we inject the assumed jets’ power into two opposite conical regions inside the envelope. We find the outflow morphology to be very complicated, clumpy, and non-spherical, having a large-scale symmetry only about the equatorial plane. The outflow morphology can substantially differ between simulations that differ by their jets’ power. We estimate by simple means the light curve to be very bumpy, to have a rise time of one to a few months, and to slowly decay in about a year to several years. These eccentric CEJSN impostors will be classified as ‘gap’ objects, i.e. having a luminosity between those of classical novae and typical supernovae (termed also ILOTs for intermediate luminosity optical transients). We strengthen a previous conclusion that CEJSN impostors might account for some peculiar ILOTs, in particular those that might repeat over time-scales of months to years.

79 ASTRONOMY AND ASTROPHYSICS↗