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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A novel 3-D pulsating heat pipe module for high heat-flux applications

This study proposed a novel pulsating heat pipes (PHP) for high-flux thermal module. According to the open literature, the maximum value of the heat flux is 23.7 W∙cm –2 handled by PHP, which is far behind industry requirements. Therefore, the present study proposes a novel 3D PHP suitable for high heat-flux applications. The PHP were experimentally tested for different working fluids, including methanol and water. Furthermore, the experiments were performed to analyze the influence of various operating parameters, such as filling ratio (25–60%), heat load (100–800 W), and air flow rate 100–250 cubic feet per minute (CFM). In this work, the results showed that this PHP can sustain a heat flux up to 70 W∙cm –2 with the maximum case temperature under 80 °C, which is approximately three times higher than the available literature. Furthermore, the performance of the PHP module was compared against the commercial module with wicked heat pipe. The PHP exhibited about 7% higher heat dissipation compared to the commercial module. Since the proposed module uses simple copper tubes in its construction over the wicked structure used in commercial module, the novel 3D PHP module provides additional economic and mass production capability.

3D pulsating heat pipe↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2024

Particle-wall heat transfer in narrow-channel bubbling fluidized beds for thermal energy storage

Robust metal oxide particles can provide low-cost and stable thermal energy storage (TES) to temperatures up to 1200 °C or higher. The transfer of heat into and out of the particles in cost-effective high-temperature particle heat exchangers remains a principal challenge to implementing particle-based TES. The present work expands on prior studies of particle-wall heat transfer in narrow-channel fluidized beds operated in the bubbling fluidization regime. Batch-mode experiments with various oxide particles over a range of temperatures and airflow rates indicate that particle-wall heat transfer increases with higher bed temperature and decreasing particle size. Measured particle-wall heat transfer coefficients in a 12 mm deep channel are fit to a Nusselt number correlation proportional to a non-monotonic function of excess fluidization velocity. Particle-wall heat transfer coefficients rise rapidly with excess fluidization velocities until reaching a maximum at intermediate air velocities due to a trade-off between enhanced transverse particle mixing and decreasing particle volume fraction with increased fluidization velocity. The heat transfer coefficient reaches a maximum of ≈ 400% of values without fluidization for oxide particles ranging in diameter d p from 159 to 408 μm. For the smallest particles tested, composed of olivine sand, particle-wall heat transfer coefficients peak above 1100 Wm –2 K –1 at 450 °C. 100-h tests at 500 °C and near-optimal heat transfer fluidization velocities indicated minimal wall wear or oxide scaling. Furthermore, high particle-wall heat transfer coefficients suggest narrow channel fluidized beds as a potential pathway for reducing the required surface area and cost of high-temperature particle heat exchangers, as needed in large-scale thermal energy storage applications.

14 SOLAR ENERGY↗
DOE OSTI2024

Net lithium deposition and dominant self-sputtering in lithium tokamak experiment-β with a liquid lithium wall

We observed enhanced net lithium deposition and lithium erosion, possibly dominated by physical sputtering of lithium by lithium-ion bombardment, on the outer plasma-facing surface in the Lithium Tokamak eXperiment-β (LTX-β) during liquid lithium wall operations. Silicon crystal samples with micro-trenches (30 μm × 30 μm × 2–7 μm deep) were exposed to hydrogen plasmas in LTX-β for solid and liquid lithium wall operations. Post-mortem analysis using X-ray photoelectron spectroscopy combined with argon ion sputtering measured net lithium deposition of 8.2 or 21 nm on the silicon crystal surface exposed for ~ 50 repeated shots of ~ 50-ms hydrogen plasma discharges during the liquid lithium wall operations at a vessel temperature of 475 K. Energy dispersive X-ray spectroscopy measured oxygen concentration patterns on the micro-trench floors, which were due to oxidized lithium deposition. Using the inhomogeneous oxygen concentration pattern caused by an ion-shadowing effect associated with the micro-trench’s geometric structure, we determined a polar incident ion direction of 68.4 ± 1.6° referenced to the surface normal direction. This observation was well-explained by the hypothesis that self-sputtering of Li was a dominant lithium erosion source in addition to lithium sputtering by hydrogen bombardment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Versatile tape-drive target for high-repetition-rate laser-driven proton acceleration

We present the development and characterization of a high-stability, multi-material, multi-thickness tape-drive target for laser-driven acceleration at repetition rates of up to 100 Hz. The tape surface position was measured to be stable on the sub-micrometre scale, compatible with the high-numerical aperture focusing geometries required to achieve relativistic intensity interactions with the pulse energy available in current multi-Hz and near-future higher repetition-rate lasers ( > kHz). Long-term drift was characterized at 100 Hz demonstrating suitability for operation over extended periods. The target was continuously operated at up to 5 Hz in a recent experiment for 70,000 shots without intervention by the experimental team, with the exception of tape replacement, producing the largest data-set of relativistically intense laser–solid foil measurements to date. This tape drive provides robust targetry for the generation and study of high-repetition-rate ion beams using next-generation high-power laser systems, also enabling wider applications of laser-driven proton sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

X-ray characterization of the Icarus ultrafast x-ray imager

Ultrafast x-ray imagers developed at Sandia National Laboratories are a transformative diagnostic tool in inertial confinement fusion and high energy density physics experiments. The nanosecond time scales on which these devices operate are a regime with little precedent, and applicable characterization procedures are still developing. This paper presents pulsed x-ray characterization of the Icarus imager under a variety of illumination levels and timing modes. Results are presented for linearity of response, absolute sensitivity, variation of response with gate width, and image quality.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Observation of synergy between lower hybrid waves at two frequencies in EAST

Synergistic effects between two frequencies of lower hybrid (LH) waves—operating at 2.45 and 4.6 GHz—were observed in experiment on EAST for the first time. At low density (ne,lin≈2.0×1019 m−3), simultaneous injection of a 65/35 mix of 2.45/4.6 GHz power achieved an lower hybrid current drive efficiency that was 25% higher than what should be expected from the linear combination of the two sources. The experiment was interpreted with time-dependent simulations, using the equilibrium and transport solver TRANSP, coupled with the ray-tracing code GENRAY and the Fokker–Planck solver CQL3D. For each discharge, profiles of current and hard x-rays from simulation and measurement agree within uncertainties. An examination of the electron distribution function indicates that the LH synergy is supported by the increased width of the LH resonance plateau in the simultaneous injection case compared to independent injection.

Choi, W. (ORCID:0000000268912300)↗
DOE OSTI2024

Overview of results from the 2023 DIII-D negative triangularity campaign

Negative triangularity (NT) is a potentially transformative configuration for tokamak-based fusion energy with its high-performance core, edge localized mode (ELM)-free edge, and low-field-side divertors that could readily scale to an integrated reactor solution. Previous NT work on the TCV and DIII-D tokamaks motivated the installation of graphite-tile armor on the low-field-side lower outer wall of DIII-D. A dedicated multiple-week experimental campaign was conducted to qualify the NT scenario for future reactors. During the DIII-D NT campaign, high confinement ($H_\mathrm{98y,2}\gtrsim$ 1), high current ($q_\mathrm{95} \lt$ 3), and high normalized pressure plasmas ($\beta_\mathrm{N} \gt$ 2.5) were simultaneously attained in strongly NT-shaped discharges with average triangularity $\delta_\mathrm{avg}$ = −0.5 that were stably controlled. Experiments covered a wide range of DIII-D operational space (plasma current, toroidal field, electron density and pressure) and did not trigger an ELM in a single discharge as long as sufficiently strong NT was maintained; in contrast, to other high-performance ELM-suppression scenarios that have narrower operating windows. These strong NT plasmas had a lower outer divertor X-point shape and maintained a non-ELMing edge with an electron temperature pedestal, exceeding that of typical L-mode plasmas. Also, the following was achieved during the campaign: high normalized density ($n_\mathrm{e}$/$n_\mathrm{GW}$ of at least 1.7), particle confinement comparable to energy confinement with $Z_\mathrm{eff}\sim2$ , a detached divertor without impurity seeding, and a mantle radiation scenario using extrinsic impurities. These results are promising for a NT fusion pilot plant but further questions on confinement extrapolation and core-edge integration remain, which motivate future NT studies on DIII-D and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Extending the low-recycling, flat temperature profile regime in the lithium tokamak experiment-β (LTX-β) with ohmic and neutral beam heating

Abstract Recent experiments in the lithium tokamak experiment- β (LTX- β ) have extended the duration, performance, operating conditions, and diagnosis of the flat-temperature profile, low-recycling regime first observed in LTX. As expected, Li retains hydrogen and suppresses edge neutral cooling, allowing increased edge electron temperature, roughly equal to the core T e . Flat temperature profiles had been obtained transiently in LTX, as the plasma density decayed following the cessation of edge gas puffing. Careful control over the fueling in LTX- β has now been shown to sustain the flat T e profile and hot edge unique to the low-recycling regime for multiple confinement times in high performance discharges with decaying or steady density. With low density, the flat T e profile is also seen to extend into the scrape-off layer. Neutral beam heating is observed in target discharges with relatively flat electron temperature profiles ( T edge ∼ T core /2), though beam heating is stronger in discharges with higher fueling, higher density, and depressed edge T e . Beam heating produces additional peaking of the T e profile, without degradation of the energy confinement time. Neutral beam heating of target discharges with relatively flat electron temperature profiles similarly results in broad beam heated temperature profiles. Energy confinement in LTX- β generally compares favorably to ohmic and H-mode scalings, frequently exceeding them by factors of 2–4. New and improved diagnostics in LTX- β enable better characterization of this unique regime, including measurements of ion temperature and high field side Thomson scattering profiles. As an initial step toward characterizing turbulence with no T e gradient and roughly equal density and pressure gradient, core fluctuation spectra have been measured in peaked T e discharges using far-forward scattering and fluctuation reflectometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Technical design report for the CODEX- β demonstrator

The CODEX-β apparatus is a demonstrator for the proposed future CODEX-b experiment, a long-lived-particle detector foreseen for operation at IP8 during HL-LHC data-taking. The demonstrator project, intended to collect data in 2025, is described, with a particular focus on the design, construction, and installation of the new apparatus.

Dark Matter detectors (WIMPs, axions, etc.)↗
DOE OSTI2025

Machine learning for single-ended event reconstruction in PROSPECT experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in 76 Ge to achieve a half-life sensitivity of 10 27 (10 28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

artificial intelligence↗
DOE OSTI2024

First constraints on WIMP-nucleon effective field theory couplings in an extended energy region from LUX-ZEPLIN

Following the first science results of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time projection chamber operating from the Sanford Underground Research Facility in Lead, South Dakota, USA, we report the initial limits on a model-independent nonrelativistic effective field theory describing the complete set of possible interactions of a weakly interacting massive particle (WIMP) with a nucleon. These results utilize the same 5.5 t fiducial mass and 60 live days of exposure collected for the LZ spin-independent and spin-dependent analyses while extending the upper limit of the energy region of interest by a factor of 7.5 to 270 keV. No significant excess in this high energy region is observed. Using a profile-likelihood ratio analysis, we report 90% confidence level exclusion limits on the coupling of each individual nonrelativistic WIMP-nucleon operator for both elastic and inelastic interactions in the isoscalar and isovector bases. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A passively pumped vacuum package sustaining cold atoms for more than 200 days

Compact cold-atom sensors depend on vacuum technology. One of the major limitations to miniaturizing these sensors is the active pumps—typically ion pumps—that are required to sustain the low pressure needed for laser cooling. Although passively pumped chambers have been proposed as a solution to this problem, technical challenges have prevented successful operation at the levels needed for cold-atom experiments. The authors present the first demonstration of a vacuum package successfully independent of ion pumps for more than a week; their vacuum package is capable of sustaining a cloud of cold atoms in a magneto-optical trap (MOT) for greater than 200 days using only non-evaporable getters and a rubidium dispenser. Measurements of the MOT lifetime indicate that the package maintains a pressure of better than 2×10−7 Torr. This result will significantly enable the development of compact atomic sensors, including those sensitive to magnetic fields, where the absence of an ion pump will be advantageous.

Little, Bethany J. (ORCID:0000000332892798)↗
DOE OSTI2021

Observation of synergy between lower hybrid waves at two frequencies in EAST

Synergistic effects between two frequencies of lower hybrid (LH) waves—operating at 2.45 and 4.6 GHz—were observed in experiment on EAST for the first time. At low density (n_e,lin ≈ 2.0 × 10^19m^−3), simultaneous injection of a 65/35 mix of 2.45 GHz/4.6 GHz power achieved an LHCD efficiency that was 25% higher than what should be expected from the linear combination of the two sources. The experiment was interpreted with time-dependent simulations, using the equilibrium and transport solver TRANSP, coupled with the ray-tracing code GENRAY and the Fokker-Planck solver CQL3D. For each discharge, profiles of current and hard x-ray from simulation and measurement agree within uncertainties. An examination of the electron distribution function indicates that the LH synergy is supported by the increased width of the LH resonance plateau in the simultaneous injection case compared to independent injection.

Lower hybrid synergy↗
DOE OSTI2024

Ultra-intense femtosecond laser interactions with aligned nanostructures

The interaction of ultrafast laser pulses of relativistic intensity with high aspect ratio nanostructures can efficiently and volumetrically heat matter to an ultra-high-energy-density regime encountered in the center of stars and within the core of fusion capsules compressed by the world’s largest lasers. It also generates gigantic quasi-static electromagnetic fields that accelerate particles to very high energy. Here, we present an overview of the physics and applications of these dense relativistic plasmas that can be created with pulses of relatively modest energy from lasers that can operate at a high repetition rate. Recent nanowire array experiments produced near-solid density plasmas with an extreme degree of ionization (e.g., Au +72 ), converted ultrafast pulses of laser light into intense x-ray flashes with record efficiency, and accelerated ions to MeV energies, efficiently driving micro-scale fusion reactions that generate flashes of quasi-monoenergetic neutrons. These plasmas also serve as a platform for advancing the understanding of atomic processes in extreme environments and open a new pathway to laser-driven fusion energy. The irradiation of nanostructures at intensities of > 1 x 10 22 W cm –2 is predicted to lead to an extreme ultra-high energy density plasma regime characterized by terabar pressures that is virtually unexplored.

Optics↗