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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Overview of physics results from the ADITYA-U tokamak and future experiments

The ADITYA upgrade (ADITYA-U), a medium-sized (R 0 = 75 cm, a = 25 cm) conventional tokamak facility in India, has been consistently producing experiments findings by usingcircular and shaped-plasmas. Recognizing the plasma parameters aligning closely with the design parameters of circular limited plasmas, ADITYA-U shifted its focus toward exploring the operational regime for experimentation on saw-tooth and MHD phenomena. Moreover, ADITYA-U has made consistent advancements toward conducting preliminary plasma shaping experiments through the activation of top and bottom divertor coils utilizing hydrogen as well as deuterium fuels. Confinement is improved by a factor of ~1.5 in D 2 plasmas when compared to H 2 plasmas of ADITYA-U. Further, ADITYA-U operations emphasize preventing disruptions and runaway electrons (REs) to ensure safe operations for future fusion devices. Significant suppression of REs has been achieved in ADITYA-U with the application of pulsed localized vertical magnetic field (LVF) perturbation, thereby establishing the technique's independence from the tokamak device. The successful RE mitigation requires a critical threshold of LVF pulse magnitude, which is approximately 1% of the toroidal magnetic field, and a minimum duration of ~5 ms. Apart from this, several novel findings have been achieved in the ADITYA-U experiments, including the modification of sawtooth duration through gas-puff, the emergence of MHD-induced geodesic acoustic mode-like oscillations, the propagation of fast heat pulses induced by MHD activity, the control of RE dynamics through Gas-puffs, the propagation of pinch-driven cold-pulses, the transport and core accumulations of argon impurities, the mass dependency of plasma toroidal rotation and the detection of 'RICE' scaling, as well as the characterization of edge plasma using wall conditioning methods, such as glow discharge cleaning using a combination of Ar-H 2 mixture, localized wall cleaning by electron cyclotron resonant plasma, and the development of machine learning-based disruption predictions, will be discussed in this paper.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Detection of a two-phonon mode in a cuprate superconductor via polarimetric resonant inelastic x-ray scattering

Recent improvements in the energy resolution of resonant inelastic x-ray scattering experiments (RIXS) at the Cu-L 3 edge have enabled the study of lattice, spin, and charge excitations. Here, we report on the detection of a low-intensity signal at 140 meV, twice the energy of the bond-stretching (BS) phonon mode, in the cuprate superconductor Bi 2 ⁢Sr 2 CaCu 2 ⁢O 8+x (Bi-2212). Ultrahigh-resolution polarimetric RIXS measurements allow us to resolve the outgoing polarization of the signal and identify this feature as a two-phonon excitation. Further, here we study the connection between the two-phonon mode and the BS one-phonon mode by constructing a joint density of states toy model that reproduces the key features of the data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Automating the detection of hydrological barriers and fragmentation in wetlands using deep learning and InSAR

The loss of hydrological connectivity and fragmentation of natural wetlands is a widespread driver of wetland degradation. Understanding where and how natural connectivity is impaired is essential for managing, protecting and remediating these ecosystems. Wetland Interferometric Synthetic Aperture Radar (Wetland InSAR) can provide information on surface flow orientation in wetlands at a high spatial resolution, which can be used for barrier detection. However, the broad application of this approach is constrained by the labour-intensive manual delineation of barriers based on mapped water levels. This study presents the first deep learning-based methodology for the automated detection of hydrological barriers. We trained a deep convolutional network to segment edge features of hydrological barriers in 25 image pairs captured by ALOS PALSAR-1 L-Band InSAR between 2006 and 2011. The training dataset consists of manually labelled and delineated barriers showing abrupt changes in water surface elevation and wrapped interferograms with high coherence. We tested this method across three wetland sites: the Everglades and southern Louisiana wetlands (United States) and the Cienaga de Zapata (Cuba). Across these sites, the convolutional network detected hydrological barriers with up to 84% accuracy. The model performed particularly well for linear hydrological barriers such as roads, dikes, and channels. Notably, some barriers impede flow only seasonally, appearing during low water levels and disappearing when water levels rise. Our automated approach to detecting and assessing wetland hydrologic connectivity can be applied more broadly to support the effective management of fragmented wetland ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Securing Inverter Communication: Proactive Intrusion Detection and Mitigation System to Tap, Analyze, and Act

The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.

14 SOLAR ENERGY↗
DOE OSTI2020

Broadband quadrature-squeezed vacuum and nonclassical photon number correlations from a nanophotonic device

We report demonstrations of both quadrature-squeezed vacuum and photon number difference squeezing generated in an integrated nanophotonic device. Squeezed light is generated via strongly driven spontaneous four-wave mixing below threshold in silicon nitride microring resonators. The generated light is characterized with both homodyne detection and direct measurements of photon statistics using photon number–resolving transition-edge sensors. We measure 1.0(1) decibels of broadband quadrature squeezing (~4 decibels inferred on-chip) and 1.5(3) decibels of photon number difference squeezing (~7 decibels inferred on-chip). Nearly single temporal mode operation is achieved, with measured raw unheralded second-order correlations g (2) as high as 1.95(1). Multiphoton events of over 10 photons are directly detected with rates exceeding any previous quantum optical demonstration using integrated nanophotonics. These results will have an enabling impact on scaling continuous variable quantum technology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Effects of trapped charge on the signal formation and detection efficiency for subsequent pulses in irradiated silicon sensors

Envisaged high energy physics experiments like the Future Circular Collider require unprecedented radiation hardness of the detectors, as well as short readout time due to high luminosity and occupancy. Silicon has proven to be extremely radiation hard. Sufficient signals can be recorded even at fluences close to 1 ∙ 1 0 16 n e q / cm 2 . Here, the signal formation was studied in silicon strip sensors, irradiated and annealed until the phenomenon of charge multiplication occurred. ATLAS12EC R0 mini sensors were tested by means of edge TCT measurements at temperatures around –20 °C. It was observed that the detection of subsequent signals separated up to several microseconds is altered by the charge trapped during the preceding pulses. The effects of trapped charge on the electrical configuration of a sensor are well known as a polarization or pumping effect in larger band-gap materials like diamond, but is often neglected for silicon at this relatively high temperature. The investigation of the effect created by trapped charges in silicon sensors using subsequent pulses allows to gain information on important parameters such as de-trapping times. Furthermore, it shows a severe impact on the sensor performance in a pile-up scenario. The irradiation fluence and hence the effective doping concentration, the temperature and the amount of initially created charge have a large impact on this phenomenon. The measurements presented help to characterize this phenomenon; particular attention was paid to the application point of view.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Direction-optimizing Label Propagation and its Application for Community Detection

Label Propagation is a machine learning algorithm typically used for classification. It has also been found to be an effective method for detecting communities in networks. It has two attractive features as a community detection method: it has nearly linear runtime and it requires no \textit{a priori} community information. We propose a new Direction Optimizing Label Propagation Algorithm (DOLPA) that relies on the use of {\em frontiers} and alternates between label {\em push} and label {\em pull} operations to enhance the performance of LPA. Specifically, DOLPA has parameters for tuning the processing order of vertices in a graph. This reduces the number of edges visited and improves the quality of solution. We apply DOLPA to community detection and present the design and implementation of the algorithm as well as its shared-memory parallelization using OpenMP. Empirically, we evaluate our algorithm using synthetic graphs as well as real-world networks. Compared with the state-of-art \textit{Parallel Label Propagation} algorithm, we achieve at least two times the F-Score while reducing the runtime by 50\% for synthetic graphs with overlapping communities. We also compare DOLPA against the state-of-art parallel implementation of the Louvain method using the same graphs and show that DOLPA achieves about three times the F-Score at 10\% the runtime. On real-world graphs, we get a speedup of up to $10\times$ using 64 threads.

Liu, X↗
DOE OSTI2024

3D Heisenberg universality in the van der Waals antiferromagnet NiPS 3

Van der Waals (vdW) magnetic materials are comprised of layers of atomically thin sheets, making them ideal platforms for studying magnetism at the two-dimensional (2D) limit. These materials are at the center of a host of novel types of experiments, however, there are notably few pathways to directly probe their magnetic structure. We confirm the magnetic order within a single crystal of NiPS 3 and show it can be accessed with resonant elastic X-ray diffraction along the edge of the vdW planes in a carefully grown crystal by detecting structurally forbidden resonant magnetic X-ray scattering. We find the magnetic order parameter has a critical exponent of β ~ 0.36, indicating that the magnetism of these vdW crystals is more adequately characterized by the three-dimensional (3D) Heisenberg universality class. We verify these findings with first-principles density functional theory, Monte-Carlo simulations, and density matrix renormalization group calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Excitation and detection of coherent nanoscale spin waves via extreme ultraviolet transient gratings

The advent of free electron lasers has opened the opportunity to explore interactions between extreme ultraviolet (EUV) photons and collective excitations in solids. While EUV transient grating spectroscopy, a noncollinear four-wave mixing technique, has already been applied to probe coherent phonons, the potential of EUV radiation for studying nanoscale spin waves has not been harnessed. Here we report EUV transient grating experiments with coherent magnons in Fe/Gd ferrimagnetic multilayers. Magnons with tens of nanometers wavelengths are excited by a pair of femtosecond EUV pulses and detected via diffraction of a probe pulse tuned to an absorption edge of Gd. The results unlock the potential of nonlinear EUV spectroscopy for studying magnons and provide a tool for exploring spin waves in a wave vector range not accessible by established inelastic scattering techniques.

Science & Technology - Other Topics↗
DOE OSTI2022

Montage electron tomography of vitrified specimens

Cryo-electron tomography provides detailed views of macromolecules in situ. However, imaging a large field of view to provide more cellular context requires reducing magnification during data collection, which in turn restricts the resolution. To circumvent this trade-off between field of view and resolution, we have developed a montage data collection scheme that uniformly distributes the dose throughout the specimen. In this approach, sets of slightly overlapping circular tiles are collected at high magnification and stitched to form a composite projection image at each tilt angle. These montage tilt-series are then reconstructed into massive tomograms with a small pixel size but a large field of view. For proof-of-principle, we applied this method to the thin edge of HeLa cells. Thon rings to better than 10 Å were detected in the montaged tilt-series, and diverse cellular features were observed in the resulting tomograms. These results indicate that the additional dose required by this technique is not prohibitive to performing structural analysis to intermediate resolution across a large field of view. We anticipate that montage tomography will prove particularly useful for lamellae, increase the likelihood of imaging rare cellular events, and facilitate visual proteomics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

FLArE up dark sectors with EM form factors at the LHC forward physics facility

Despite being mostly secluded, dark sector particles may feebly interact with photons via a small mass-dimension 4 millicharge, a mass-dimension 5 magnetic and electric dipole moment, or a mass-dimension 6 anapole moment and charge radius. If sufficiently light, the LHC may produce an intense and collimated beam of these particles in the far forward direction. We study the prospects of searching for such dark sector particles with electromagnetic form factors via their electron scattering signature in the Forward Liquid Argon Experiment (FLArE) at the Forward Physics Facility (FPF). We find that FLArE can provide new probes of sub-GeV dark particles with dipole moments and strong sensitivities for millicharged particles in the 100 MeV to 100 GeV region. This complements other search strategies using scintillation signatures or dark matter direct detection and allows for probing strongly interacting dark matter motivated by the EDGES anomaly. Along with the FORMOSA detector, this leads to a very versatile and leading experimental program in the search for millicharged particles in the FPF.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Rapid Accumulation of Soil Inorganics on Plastics: Implications for Plastic Degradation and Contaminant Fate

As plastics degrade in the environment, chemical oxidation of the plastic surface enables inorganics to adsorb and form inorganic coatings, likely through a combination of adsorption of minerals and in situ mineral formation. The presence of inorganic coatings on aged plastics has negative implications for plastics fate, hindering our ability to recycle weathered plastics and increasing the potential for plastics to adsorb contaminants. Here, inorganic coatings formed on terrestrially weathered polyethylene were characterized using synchrotron spectroscopy and microscopy techniques across spatial scales including optical microscopy, nano-X-ray-fluorescence mapping (nano-XRF), nano-X-ray absorption near edge structure (nano-XANES), and high-energy resolution fluorescence detected-XANES (HERFD-XANES). Results indicate a heterogeneous elemental distribution and speciation which includes inorganics common to soil terrestrial environments including iron oxides and oxyhydroxides, aluminosilicates, and carbonates.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Characterization of just one atom using synchrotron X-rays

Since the discovery of X-rays by Roentgen in 1895, its use has been ubiquitous, from medical and environmental applications to materials sciences. X-ray characterization requires a large number of atoms and reducing the material quantity is a long-standing goal. Here we show that X-rays can be used to characterize the elemental and chemical state of just one atom. Using a specialized tip as a detector, X-ray-excited currents generated from an iron and a terbium atom coordinated to organic ligands are detected. The fingerprints of a single atom, the L 2,3 and M 4,5 absorption edge signals for iron and terbium, respectively, are clearly observed in the X-ray absorption spectra. The chemical states of these atoms are characterized by means of near-edge X-ray absorption signals, in which X-ray-excited resonance tunnelling (X-ERT) is dominant for the iron atom. The X-ray signal can be sensed only when the tip is located directly above the atom in extreme proximity, which confirms atomically localized detection in the tunnelling regime. Finally, our work connects synchrotron X-rays with a quantum tunnelling process and opens future X-rays experiments for simultaneous characterizations of elemental and chemical properties of materials at the ultimate single-atom limit.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Nature of Excitons and Their Ligand-Mediated Delocalization in Nickel Dihalide Charge-Transfer Insulators

The fundamental optical excitations of correlated transition-metal compounds are typically identified with multielectronic transitions localized at the transition-metal site, such as d d transitions. In this vein, intense interest has surrounded the appearance of sharp, below-band-gap optical transitions, i.e., excitons, within the magnetic phase of correlated Ni 2 + van der Waals magnets. The interplay of magnetic and charge-transfer insulating ground states in Ni 2 + systems raises intriguing questions on the roles of long-range magnetic order and of metal-ligand charge transfer in the exciton nature, which inspired microscopic descriptions beyond typical d d excitations. Here we study the impact of charge transfer and magnetic order on the excitation spectrum of the nickel dihalides ( Ni X 2 , X = Cl , Br, and I) using Ni- L 3 edge resonant inelastic x-ray scattering (RIXS). In all compounds, we detect sharp excitations, analogous to the recently reported excitons, and assign them to spin-singlet multiplets of octahedrally coordinated Ni 2 + stabilized by intra-atomic Hund’s exchange. Additionally, we demonstrate that these excitons are dispersive using momentum-resolved RIXS. Our data evidence a ligand-mediated multiplet dispersion, which is tuned by the charge-transfer gap and independent of the presence of long-range magnetic order. This reveals the mechanisms governing nonlocal interactions of on-site d d excitations with the surrounding crystal or magnetic structure, in analogy to ground-state superexchange. These measurements thus establish the roles of magnetic order, self-doped ligand holes, and intersite-coupling mechanisms for the properties of d d excitations in charge-transfer insulators. Published by the American Physical Society 2024

2-dimensional systems↗
DOE OSTI2023

Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.

Aird, Jeanie A.↗
DOE OSTI2025

Prelude to Simulation Diagnostics in MOOSE: Diagnosing Meshes

As finite element simulations increase in complexity, so does the need for high quality meshes. Without proper diagnostics, issues pertaining to poor mesh quality or improper mesh construction may go undetected, causing simulations to fail unexpectedly. This paper details the new mesh diagnostic system available in MOOSE, which provides users with the tools to inspect mesh quality for both externally and internally generated meshes. This system can detect issues arising from element volume size, non-conformal elements, intersecting edges, missing sidesets, as well as other common mesh issues. As meshes increase in size and complexity, such issues become more difficult to diagnose through visual inspection alone.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Graph neural networks for detecting anomalies in scientific workflows

Identifying and addressing anomalies in complex, distributed systems can be challenging for reliable execution of scientific workflows. We model these workflows as directed acyclic graphs (DAGs), where the nodes and edges of the DAGs represent jobs and their dependencies, respectively. We develop graph neural networks (GNNs) to learn patterns in the DAGs and to detect anomalies at the node (job) and graph (workflow) levels. We investigate workflow-specific GNN models that are trained on a particular workflow and workflow-agnostic GNN models that are trained across the workflows. Our GNN models, which incorporate both individual job features and topological information from the workflow, show improved accuracy and efficiency compared to conventional learning methods for detecting anomalies. While joint trained with multiple scientific workflows, our GNN models reached an accuracy more than 80% for workflow level and 75% for job level anomalies. In addition, we illustrate the importance of hyperparameter tuning method in our study that can significantly improve the metric(s) measure of evaluating the GNN models. Finally, we integrate explainable GNN methods to provide insights on job features in the workflow that cause an anomaly.

97 MATHEMATICS AND COMPUTING↗