Search NASA⌕ Search

SEARCH · Search NASA

Results for “Processing maps”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75

Evaluating the effectiveness of program data features for guiding memory management

Recent trends have led to the adoption of larger and more complex memory systems, often with multiple tiers of memory performance within the same platform. To utilize complex memory systems efficiently, current data management strategies must be altered to map usage demands to the underlying hardware. Applications, as the generators of memory accesses, are well-suited to guide this process, but building and maintaining separate source code versions for different memory systems is not feasible in most cases. One potential solution is to employ automated program profiling and analysis to facilitate the production of application-based guidance. By attaching memory usage information to static or lightweight program features, compilers and runtime systems can generate fine-grained guidance without additional efforts from users or developers. Recent works have employed this approach with some success, but it is not clear which program features are most useful for guiding data management.This work evaluates the effectiveness of using different program data features to predict memory usage and guide memory management. It employs a custom set of simulation tools, based in the Intel® Pin framework, to collect and analyze the usage characteristics of application data associated with common program data features, such as allocation sites, types, and context. The results show that even relatively simple features, such as object size, are useful for selecting data with similar usage properties, but finer-grained features, such as the instructions that access a particular object, are often much more effective. Additionally, this work evaluates the performance of using different data features and different program inputs to guide data placement on a heterogeneous memory platform with a limited amount of high performance memory.

Effler, T. Chad↗

Pauling’s rules for oxide-based minerals: A re-examination based on quantum mechanical constraints and modern applications of bond-valence theory to Earth materials

Since their introduction in 1929, Pauling’s five rules have been used by scientists from many disciplines to rationalize and predict stable arrangements of atoms and coordination polyhedra in crystalline solids; amorphous materials such as silicate glasses and melts; nanomaterials, poorly crystalline solids; aqueous cation and anion complexes; and sorption complexes at mineral-aqueous solution interfaces. The predictive power of these simple yet powerful rules was challenged recently by George et al. (2020), who performed a statistical analysis of the performance of Pauling’s five rules for about 5000 oxide crystal structures. They concluded that only 13% of the oxides satisfy the last four rules simultaneously and that the second rule has the most exceptions. They also found that Pauling’s first rule is satisfied for only 66% of the coordination environments tested and concluded that no simple rule linking ionic radius to coordination environment will be predictive due to the variable quality of univalent radii. We address these concerns and discuss quantum mechanical calculations that complement Pauling’s rules, particularly his first (radius sum and radius ratio rule) and second (electrostatic valence rule) rules. We also present a more realistic view of the bonded radii of atoms, derived by determining the local minimum in the electron density distribution measured along trajectories between bonded atoms known as bond paths, i.e., the bond critical point (rc). Electron density at the bond critical point is a quantum mechanical observable that correlates well with Pauling bond strength. Moreover, a metal atom in a polyhedron has as many bonded radii as it has bonded interactions, resulting in metal and O atoms that may not be spherical. O atoms, for example, are not spherical in many oxide-based crystal structures. Instead, the electron density of a bonded oxygen is often highly distorted or polarized, with its bonded radius decreasing systematically from ~1.38 Å when bonded to highly electropositive atoms like sodium to 0.64 Å when bonded to highly electronegative atoms like nitrogen. Bonded radii determined for metal atoms match the Shannon (1976) radii for more electropositive atoms, but the match decreases systematically as the electronegativities of the M atoms increase. As a result, significant departures from the radius ratio rule in the analysis by George et al. (2020) is not surprising. We offer a modified, more fundamental version of Pauling’s first rule and demonstrate that the second rule has a one-to-one connection between the electron density accumulated between the bonded atoms at the bond critical point and the Pauling bond strength of the bonded interaction. Pauling’s second rule implicitly assumes that bond strength is invariant with bond length for a given pair of bonded atoms. Many studies have since shown that this is not the case, and Brown and Shannon (1973) developed an equation and a set of parameters to describe the relation between bond length and bond strength, now redefined as bond valence to avoid confusion with Pauling bond-strength. Brown (1980) used the valence-sum rule, together with the path rule and the valence-matching principle, as the three axioms of bond-valence theory (BVT), a powerful method for understanding many otherwise elusive aspects of crystals and also their participation in dynamic processes. We show how a priori bond-valence calculations can predict unstrained bond-lengths and how bond-valence mapping can locate low-Z atoms in a crystal structure (e.g., Li) or examine possible diffusion pathways for atoms through crystal structures. In addition, we briefly discuss Pauling’s third, fourth, and fifth rules, the first two of which concern the sharing of polyhedron elements (edges and faces) and the common instability associated with structures in which a polyhedron shares an edge or face with another polyhedron and contains high-valence cations. The olivine [α-(MgxFe1–x)2SiO4] crystal structure is used to illustrate the distortions from hexagonal close-packing of O atoms caused by metal-metal repulsion across shared polyhedron edges. We conclude by discussing several applications of BVT to Earth materials, including the use of BVT to: (1) locate H+ ions in crystal structures, including the location of protons in the crystal structures of nominally anhydrous minerals in Earth’s mantle; (2) determine how strongly bonded (usually anionic) structural units interact with weakly bonded (usually cationic) interstitial complexes in complex uranyl-oxide and uranyl-oxysalt minerals using the valence-matching principle; (3) calculate Lewis acid strengths of cations and Lewis base strengths of anions; (4) determine how (H2O) groups can function as bond-valence transformers by dividing one bond into two bonds of half the bond valence; (5) help characterize products of sorption reactions of aqueous cations (e.g., Co2+ and Pb2+) and oxyanions [e.g., selenate (Se6+O4)2- and selenite (Se4+O3)2-] at mineral-aqueous solution interfaces and the important role of protons in these reactions; and (6) help characterize the local coordination environments of highly charged cations (e.g., Zr4+, Ti4+, U4+, U5+, and U6+) in silicate glasses and melts.

Geochemistry & Geophysics↗

New High-Resolution Electron Scattering Capability

This project explored a new capability for studying collisions of electrons and molecules with unprecedented accuracy by combining high electron-energy resolution with velocity mapped imaging of electrons. Low-energy electrons were produced within a supersonic beam by photoionization of metastable krypton using a dye laser to generate electrons with tunable kinetic energy and a narrow energy spread. A new configuration for electron imaging optics was developed to enable scattering of electrons in a zero-field environment with subsequent rapidly pulsed velocity mapped imaging of the electrons. Development of this new capability will significantly enhance DOE/NNSA's ability to perform basic research on processes relevant to plasmas in atmospheric re-entry and neutron generation for weapons systems and provide fundamental understanding of electron-driven chemistry important to solar energy conversion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform

n this presentation we will discuss recent results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia Labs developed Whet stone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.

97 MATHEMATICS AND COMPUTING↗

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗

A New Route Toward Atomically Flat and Defect-Free Ge/SiGe Planar Heterostructures

Germanium-based planar heterostructures are emerging as versatile platforms for realizing quantum devices. In particular, planar Ge/SiGe quantum wells (QWs) host hole states with exceptionally high mobility and strong, electrically tunable spin–orbit interactions, enabling full electrical control of quantum information. A key requirement for these systems is the growth of microscopic, defect-free, atomically flat Ge QW heterostructures on relaxed or reverse-graded SiGe buffer layers, as well as on commercial Ge substrates. While several physical deposition techniques have demonstrated high-quality planar Ge/SiGe QWs, a major challenge remains: minimizing defect density typically requires high growth temperatures, which are incompatible with standard CMOS process flows. Here, we present a convenient low-temperature process for realizing high-quality planar SiGe/Ge heterostructures using a combination of thermal and electron-beam evaporation. We systematically map the effects of ex-situ and in-situ substrate preparation protocols, growth temperature, and post-deposition annealing conditions, and correlate these parameters with surface roughness and defect density. We find that in-situ oxide desorption conditions and post-annealing parameters have the most pronounced impact on improving surface quality. Under optimized conditions, we achieve atomically smooth surfaces with root-mean-square roughness σrms​ ≤ 10 Å and negligible defect density. Interestingly, thermally evaporated Ge layers exhibit oriented triangular crystallites that elongate upon post-annealing in the presence of high Ge vapor pressure. These results demonstrate that this simple, low-temperature deposition approach is a viable and effective route for achieving high-quality Ge QW heterostructures, with strong potential for scalable quantum computing and sensing applications.

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗

A New Route Toward Atomically Flat and Defect-Free Ge/SiGe Planar Heterostructures

Germanium-based planar heterostructures are emerging as versatile platforms for realizing quantum devices. In particular, planar Ge/SiGe quantum wells (QWs) host hole states with exceptionally high mobility and strong, electrically tunable spin–orbit interactions, enabling full electrical control of quantum information. A key requirement for these systems is the growth of microscopic, defect-free, atomically flat Ge QW heterostructures on relaxed or reverse-graded SiGe buffer layers, as well as on commercial Ge substrates. While several physical deposition techniques have demonstrated high-quality planar Ge/SiGe QWs, a major challenge remains: minimizing defect density typically requires high growth temperatures, which are incompatible with standard CMOS process flows. Here, we present a convenient low-temperature process for realizing high-quality planar SiGe/Ge heterostructures using a combination of thermal and electron-beam evaporation. We systematically map the effects of ex-situ and in-situ substrate preparation protocols, growth temperature, and post-deposition annealing conditions, and correlate these parameters with surface roughness and defect density. We find that in-situ oxide desorption conditions and post-annealing parameters have the most pronounced impact on improving surface quality. Under optimized conditions, we achieve atomically smooth surfaces with root-mean-square roughness σrms​ ≤ 10 Å and negligible defect density. Interestingly, thermally evaporated Ge layers exhibit oriented triangular crystallites that elongate upon post-annealing in the presence of high Ge vapor pressure. These results demonstrate that this simple, low-temperature deposition approach is a viable and effective route for achieving high-quality Ge QW heterostructures, with strong potential for scalable quantum computing and sensing applications.

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗

Lifecycle of US Hydropower Facilities: How data at every stage drives growth

This product highlights data across all stages of a hydropower facility’s lifecycle. These data enable collaborative discussion on what is needed to enhance current facility performance, reduce operations and maintenance costs, streamline the permitting process, foster new project development, and address environmental concerns with a determined focus on mitigation. This product is an ArcGIS Story Map that contains an array of interactive maps.

13 HYDRO ENERGY↗

GAS DYNAMICS AND OUTFLOW IN THE BARRED STARBURST GALAXY NGC 1808 REVEALED WITH ALMA

NGC 1808 is a nearby barred starburst galaxy with an outflow from the nuclear region. To study the inflow and outflow processes related to star formation and dynamical evolution of the galaxy, we have carried out {sup 12}CO (J=1−0) mapping observations of the central r ∼ 4 kpc of NGC 1808 using the Atacama Large Millimeter/submillimeter Array. Four distinct components of molecular gas are revealed at high spatial resolution of 2″ (∼100 pc): (1) a compact (r < 200 pc) circumnuclear disk (CND), (2) r ∼ 500 pc ring, (3) gas-rich galactic bar, and (4) spiral arms. Basic geometric and kinematic parameters are derived for the central 1 kpc region using tilted-ring modeling. The derived rotation curve reveals multiple mass components that include (1) a stellar bulge, (2) a nuclear bar and molecular CND, and (3) an unresolved massive (∼10{sup 7} M {sub ⊙}) core. Two systemic velocities, 998 km s{sup −1} for the CND and 964 km s{sup −1} for the 500 pc ring, are revealed, indicating a kinematic offset. The pattern speed of the primary bar, derived by using a cloud-orbit model, is 56 ± 11 km s{sup −1} kpc{sup −1}. Noncircular motions are detected associated with a nuclear spiral pattern and outflow in the central 1 kpc region. The ratio of the mass outflow rate to the star formation rate is M-dot {sub out}/SFR∼0.2 in the case of optically thin CO (1–0) emission in the outflow, suggesting low efficiency of star formation quenching.

79 ASTRONOMY AND ASTROPHYSICS↗

Toward a Multimodal-Deep Learning Retrieval System for Monitoring Nuclear Proliferation Activities

As the amount of available data increases, the human ability to locate, process, and analyze it is strained and eventually overwhelmed. To address this challenge for nonproliferation analysts, the authors have been creating a large-scale multimodal retrieval system to help analysts triage and search open source science, technology, and news data for indicators of nuclear proliferation capabilities and activities. The system relies on a set of deep neural networks (DNNs) trained to evaluate conceptual similarities across data modalities, such as text, image, and video. These DNNs can be used to search and prioritize data, according to a nuclear fuel cycle (NFC) process template, that are conceptually closest to the seed query items regardless of data modality. The DNNs have been trained to map conceptually related words, sentences, and images to nearby points in a multimodal feature space, enabling intra- and intermodal retrieval via nearest-neighbor calculations to seed query points. The authors evaluate the system’s ability to retrieve NFC-related data that have been purposely hidden in collections of unrelated background data. They demonstrate quantitative and qualitative results for text-to-image, image-to-image, and image-to-video retrievals. This paper discusses data challenges confronting nonproliferation analysts, describes the DNNs designed to retrieve multimodal data that are proximal in the semantic space, and demonstrates their effectiveness by applying the DNNs to nonproliferation-specific multimodal data sets.

97 MATHEMATICS AND COMPUTING↗

Mapping SIEM Vulnerabilities in STIG

SIEM (Security Information and Event Management) tools monitor network traffic and allow users to quickly detect problems in their networks. Because of the valuable information processed by SIEM tools, it is important to understand their vulnerabilities. STIG (Structured Threat Intelligence Graph) is an application created at INL used to visualize data related to cyber threats. Using STIG can allow users to understand vulnerabilities related to their SIEM products and how to protect their systems.

99 GENERAL AND MISCELLANEOUS↗

Revealing the structure of light pseudoscalar mesons at the electron–ion collider

The questions of how the bulk of the Universe's visible mass emerges and how it is manifest in the existence and properties of hadrons are profound, and probe the heart of strongly interacting matter. Paradoxically, the lightest pseudoscalar mesons appear to be key to a further understanding of the emergent mass and structure mechanisms. These mesons, namely, the pion and kaon, are the Nambu-Goldstone boson modes of quantum chromodynamics (QCD). Unravelling their partonic structure and the interplay between emergent and Higgs-boson mass mechanisms is a common goal of three interdependent approaches - continuum QCD phenomenology, lattice-regularised QCD, and the global analysis of parton distributions - linked to experimental measurements of hadron structure. Experimentally, the anticipated electron-ion collider will enable a revolution in our ability to study pion and kaon structures, accessed by scattering from the 'meson cloud' of the proton through the Sullivan process. With the goal of enabling a suite of measurements that can address these questions, we examine key reactions that identify the critical detector-system requirements needed to map tagged pion and kaon cross-sections over a wide range of kinematics. The excellent prospects for extracting pion structural, functional, and form-factor data are outlined, and similar prospects for kaon structures are discussed in the context of a worldwide programme. Finally, the successful completion of the programme outlined herein will deliver deep, far-reaching insights into the emergence of pions and kaons, their properties, and their role as QCD's Goldstone boson modes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Photoinduced surface voltage mapping study for large perovskite single crystals

Using a series of illumination sources, including white light (tungsten-halogen lamp), 445-nm, 532-nm, 635-nm, and 730-nm lasers, the surface photovoltage (SPV) images were mapped for centimeter-sized CH{sub 3}NH{sub 3}PbX{sub 3} (X = Cl, Br, I) perovskite single crystals using Kelvin probe force microscopy. The significant SPV signals were observed to be wavelength-dependent. We attribute the appreciable SPV to the built-in electric field in the space charge region. This study shines light into the understanding of photoinduced charge generation and separation processes at nanoscale to help advance the development of perovskite solar cells, optoelectronics, laser, photodetector, and light-emitting diode (LED).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗

Geomorphic mapping and permafrost occurrence on the Koyukuk River floodplain near Huslia, Alaska

Permafrost occurrence measured in-situ and geomorphic maps drawn by hand using high-resolution satellite imagery for the Koyukuk River floodplain near Huslia, Alaska. The dataset was collected to characterize permafrost extent and rates of formation and degradation due to river processes and climate change. Permafrost occurrence and thickness of the active layer was measured using a 1 or 2 m long permafrost probe or through direct observation (coring, digging trench, exposed ice). Measurements were from June 27 - July 8, 2018 and September 26 - October 1, 2022 and are saved as a csv file. Geomorphic maps delineate floodplain landforms, the relative age of floodplain deposits, and prior paths of the river produced by connecting oxbow lakes. Maps are saved as georeferenced shapefiles that can be imported into QGIS or ArcGIS software.

54 ENVIRONMENTAL SCIENCES↗

OPTIMIZING CENTRIFUGAL BARREL POLISHING FOR MIRROR FINISH SRF CAVITY AND RF TESTS AT JEFFERSON LAB

We performed Centrifugal Barrel Polishing (CBP) on a 1.3 GHz fine grain TESLA single cell cavity and 1.5 GHz fine grain CEBAF high gradient superconducting radio frequency (SRF) single cell cavity following a modified recipe originally developed at Fermi National Accelerator Lab (FNAL). We were able to obtain a mirror like surface similar to that obtained at FNAL, while reducing the number of CBP steps and total processing time. This paper will discuss the change in surface and subsequent cavity performance post CBP, after a 800 C bake (no pre-bake chemistry) and minimal controlled electro-polishing (10 micron). In addition to Q vs. E{sub ACC} thermometry mapping with preheating characteristics and optical inspection of the cavity after CBP will also be shown.

43 PARTICLE ACCELERATORS↗

Conversion Reaction Mechanisms in Lithium Ion Batteries: Study of the Binary Metal Fluoride Electrodes

Materials that undergo a conversion reaction with lithium (e.g., metal fluorides MF{sub 2}: M = Fe, Cu, ...) often accommodate more than one Li atom per transition-metal cation, and are promising candidates for high-capacity cathodes for lithium ion batteries. However, little is known about the mechanisms involved in the conversion process, the origins of the large polarization during electrochemical cycling, and why some materials are reversible (e.g., FeF{sub 2}) while others are not (e.g., CuF{sub 2}). In this study, we investigated the conversion reaction of binary metal fluorides, FeF{sub 2} and CuF{sub 2}, using a series of local and bulk probes to better understand the mechanisms underlying their contrasting electrochemical behavior. X-ray pair-distribution-function and magnetization measurements were used to determine changes in short-range ordering, particle size and microstructure, while high-resolution transmission electron microscopy (TEM) and electron energy-loss spectroscopy (EELS) were used to measure the atomic-level structure of individual particles and map the phase distribution in the initial and fully lithiated electrodes. Both FeF{sub 2} and CuF{sub 2} react with lithium via a direct conversion process with no intercalation step, but there are differences in the conversion process and final phase distribution. During the reaction of Li{sup +} with FeF{sub 2}, small metallic iron nanoparticles (<5 nm in diameter) nucleate in close proximity to the converted LiF phase, as a result of the low diffusivity of iron. The iron nanoparticles are interconnected and form a bicontinuous network, which provides a pathway for local electron transport through the insulating LiF phase. In addition, the massive interface formed between nanoscale solid phases provides a pathway for ionic transport during the conversion process. These results offer the first experimental evidence explaining the origins of the high lithium reversibility in FeF{sub 2}. In contrast to FeF{sub 2}, no continuous Cu network was observed in the lithiated CuF{sub 2}; rather, the converted Cu segregates to large particles (5-12 nm in diameter) during the first discharge, which may be partially responsible for the lack of reversibility in the CuF{sub 2} electrode.

Wang, Feng↗

The Arabidopsis CstF64-Like RSR1/ESP1 Protein Participates in Glucose Signaling and Flowering Time Control

Mechanisms for sensing and regulating metabolic processes at the cellular level are critical for the general physiology and development of living organisms. In higher plants, sugar signaling is crucial for adequate regulation of carbon and energy metabolism and affects virtually every aspect of development. Although many genes are regulated by sugar levels, little is known on how sugar levels are measured by plants. Several components of the sugar signaling network have been unraveled and demonstrated to have extensive overlap with hormone signaling networks. Here we describe the reduced sugar response1-1 (rsr1-1) mutant as a new early flowering mutant that displays decreased sensitivity to abscisic acid. Both hexokinase1 (HXK1)-dependent and glucose phosphorylation-independent signaling is reduced in rsr1-1. Map-based identification of the affected locus demonstrated that rsr1- 1 carries a premature stop codon in the gene for a CstF64-like putative RNA processing factor, ESP1, which is involved in mRNA 3 -end formation. The identification of RSR1/ESP1 as a nuclear protein with a potential threonine phosphorylation site may explain the impact of protein phosphorylation cascades on sugar-dependent signal transduction. Additionally, RSR1/ESP1 may be a crucial factor in linking sugar signaling to the control of flowering time.

3-O-methylglucose↗