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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Exposure to a Rotating Virtual Environment During Treadmill Locomotion Causes Adaptation in Heading Direction

The goal of the present study was to investigate the adaptive effects of variation in the direction of optic flow, experienced during linear treadmill walking, on modifying locomotor trajectory. Subjects (n = 30) walked on a motorized linear treadmill at 4.0 kilometers per hour for 24 minutes while viewing the interior of a 3D virtual scene projected onto a screen 1.5 in in front of them. The virtual scene depicted constant self-motion equivalent to either 1) walking around the perimeter of a room to one s left (Rotating Room group) 2) walking down the center of a hallway (Infinite Hallway group). The scene was static for the first 4 minutes, and then constant rate self-motion was simulated for the remaining 20 minutes. Before and after the treadmill locomotion adaptation period, subjects performed five stepping trials where in each trial they marched in place to the beat of a metronome at 90 steps/min while blindfolded in a quiet room. The subject's final heading direction (deg), final X (for-aft, cm) and final Y (medio-lateral, cm) positions were measured for each trial. During the treadmill locomotion adaptation period subject's 3D torso position was measured. We found that subjects in the Rotating Room group as compared to the Infinite Hallway group: 1) showed significantly greater deviation during post exposure testing in the heading direction and Y position opposite to the direction of optic flow experienced during treadmill walking 2) showed a significant monotonically increasing torso yaw angular rotation bias in the direction of optic flow during the treadmill adaptation exposure period. Subjects in both groups showed greater forward translation (in the +X direction) during the post treadmill stepping task that differed significantly from their pre exposure performance. Subjects in both groups reported no perceptual deviation in position during the stepping tasks. We infer that viewing simulated rotary self-motion during treadmill locomotion causes adaptive modification of sensory-motor integration in the control of position and trajectory during locomotion which functionally reflects adaptive changes in the integration of visual, vestibular, and proprioceptive cues. Such an adaptation in the control of position and heading direction during locomotion due to the congruence of sensory information demonstrates the potential for adaptive transfer between sensorimotor systems and suggests a common neural site for the processing and self-motion perception and concurrent adaptation in motor output. This will result in lack of subjects perception of deviation of position and trajectory during the post treadmill step test while blind folded.

Ruttley, T↗
DOE OSTI2024

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Zheng, Weijian↗
NASA NTRS2005

Exposure to a Rotating Virtual Environment During Treadmill Locomotion Causes Adaptation in Heading Direction

The goal of the present study was to investigate the adaptive effects of variation in the direction of optic flow, experienced during linear treadmill walking, on modifying locomotor trajectory. Subjects (n = 30) walked on a motorized linear treadmill at 4.0 km/h for 24 minutes while viewing the interior of a 3D virtual scene projected onto a screen 1.5 m in front of them. The virtual scene depicted constant self-motion equivalent to either 1) walking around the perimeter of a room to one s left (Rotating Room group) 2) walking down the center of a hallway (Infinite Hallway group). The scene was static for the first 4 minutes, and then constant rate self-motion was simulated for the remaining 20 minutes. Before and after the treadmill locomotion adaptation period, subjects performed five stepping trials where in each trial they marched in place to the beat of a metronome at 90 steps/min while blindfolded in a quiet room. The subject s final heading direction (deg), final X (for-aft, cm) and final Y (medio-lateral, cm) positions were measured for each trial. During the treadmill locomotion adaptation period subject s 3D torso position was measured. We found that subjects in the Rotating Room group as compared to the Infinite Hallway group: 1) showed significantly greater deviation during post exposure testing in the heading direction and Y position opposite to the direction of optic flow experienced during treadmill walking 2) showed a significant monotonically increasing torso yaw angular rotation bias in the direction of optic flow during the treadmill adaptation exposure period. Subjects in both groups showed greater forward translation (in the +X direction) during the post treadmill stepping task that differed significantly from their pre exposure performance. Subjects in both groups reported no perceptual deviation in position during the stepping tasks. We infer that 3 viewing simulated rotary self-motion during treadmill locomotion causes adaptive modification of sensory-motor integration in the control of position and trajectory during locomotion which functionally reflects adaptive changes in the integration of visual, vestibular, and proprioceptive cues. Such an adaptation in the control of position and heading direction during locomotion due to the congruence of sensory information demonstrates the potential for adaptive transfer between sensorimotor systems and suggests a common neural site for the processing and self-motion perception and concurrent adaptation in motor output. This will result in lack of subjects perception of deviation of position and trajectory during the post treadmill step test while blind folded.

Mulavara, A. P.↗
NASA NTRS2003

Mars Data Visualization and E/PO with Marsoweb

Marsoweb is a collaborative web environment that has been developed for the Mars research community to better visualize and analyze Mars orbiter data. Its goal is to enable online data discovery by providing an intuitive, interactive interface to data from the Mars Global Surveyor and other orbiters. Recently Marsoweb has served a prominent role as a resource center for the site selection process for the Mars Explorer Rover 2003 missions. In addition to hosting a repository of landing site memoranda and workshop talks, it includes a Java-based interface to a variety of datamaps and images. This interface enables the display and numerical querying of data, and allows data profiles to be rendered from user-drawn cross-sections. High-resolution Mars Orbiter Camera (MOC) images (currently, over 100,000) can be graphically perused; browser-based image processing tools can be used on MOC images of potential landing sites. An automated VRML atlas allows users to construct 'flyovers' of their own regions-of-interest in 3D. These capabilities enable Marsoweb to be used for general global data studies, in addition to those specific to landing site selection. As of September 2002, over 70,000 distinct users from NASA, USGS, academia, and the general public have accessed Marsoweb.

Gulick, V. C.↗
NASA NTRS2019

Cluster Analysis of Spectroscopic Line Profiles and EUV Emission in RMHD Simulations and Observations of the Solar Atmosphere

Spatially-resolved observations from the IRIS, SDO/AIA, and other space mission and ground-based telescopes, coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar atmosphere. To better understand the dynamical and thermodynamic properties in the simulation data and their connection to observations, it is essential to determine similarities in the behaviors of the synthesized and observed emission. However, the complexity of observational data and physical processes makes comparison of observations and modeling results difficult. In this work, we show the initial results of application of K-Means clustering (unsupervised machine learning) algorithm to two different problems: 1) recognition of the typical spectroscopic line profiles observed by IRIS during solar flares and their typical dynamic behavior; 2) recognition of shocks and heating events in synthetic AIA emission data obtained from StellarBox quiet-Sun simulations. The average silhouette width technique for the KMeans algorithm is utilized in different ways to obtain optimal numbers of clusters. We discuss application of the emission clustering to visualizations of the computational volume, understanding its evolutionary trends and behavior patterns, and inversion (reconstruction) of physical properties of the solar atmosphere from synthesizes emission data.

Sadykov, Viacheslav↗
NASA NTRSDate not supplied

Evaluation of Markerless Motion Capture for Monitoring Sensorimotor Performance

BACKGROUND Astronauts returning from long-duration exposure to microgravity frequently exhibit alterations in sensorimotor function leading to postural imbalance, impaired locomotion, and operational challenges to manual control. Mission duration and individual responses often influence both the severity of performance decrements and the variability in adaptation timelines. Postflight disruptions during functional tasks are often detected through body-worn inertial measurement unit (IMU) devices. While IMU sensors are relatively compact, the long-term wear may lead to discomfort, displacement of the sensors on the body, and restrictions in movement or crew behavior. Although IMU data offers valuable insights from a research standpoint, interpreting changes in pre- and post-flight measures can be difficult for crew support personnel beyond the research domain, which can hinder the application for medical assessments and rehabilitation. Finally, the availability of inertial sensors in-flight is limited. There is a need for unobtrusive monitoring tools to improve our ability to monitor adaptation following gravitational transitions in various postflight evaluations and rehabilitation settings. Markerless motion capture (MMC) is an evolving unobtrusive technology that builds upon decades of research with marker-based motion capture systems to provide 3D human pose estimation from multiple synchronized 2D camera views using deep learning algorithms. Markerless technology can revolutionize how data is captured pre- and post-flight and potentially in-flight during intravehicular activity by enabling pose estimation of multiple crew members from onboard camera hardware. METHODS The following presents the initial evaluation of a state-of-the-art commercial-off-the-shelf MMC system, Theia Markerless, compared to IMU devices during various ground-based functional tasks and environmental conditions. The featured functional tasks include assessments from Human Research Program (HRP) funded studies such as Sensorimotor Standard Measures and Sensorimotor Assessments. Synchronous data collected using both motion capture and IMUs are analyzed for six male and female subjects of varying anthropometry. The analysis includes limited assessments of clothing, capture volume configurations, and the tool's sensitivity to detecting performance changes after a spaceflight analog centrifuge exposure. The development of visualization tools to enhance the application of the pose estimation output is also presented. RESULTS Initial results demonstrate comparable root mean square error (RMSE) to existing literature evaluating markerless and marker-based motion capture systems. Considering the relative functional range of motion of the cervical spine, normalized error values for the markerless system’s accuracy of the head was 0.032 in pitch, 0.025 in roll, and 0.018 in yaw plane of motion across a subset of functional tasks. The raw RMSE values were 3.49, 2.25, and 2.81 degrees respectively. For the torso, results suggest normalized errors of 0.469 in pitch, 0.191 in roll, and 0.307 in yaw planes of motion and raw RMSE values of 3.52, 1.53, and 3.07 degrees respectively. The data suggests the functional demands of a particular task influences the estimation accuracy of the MMC system where more dynamic motion and cases where subjects are not upright may introduce diminished tracking accuracy. DISCUSSION The following work lays the foundation for future implementations leveraging markerless motion capture to assess the time course of recovery and provide insight for rehabilitation protocols to enhance crew readiness for the resumption of daily activities. These tools offer effective methods for anonymizing sensitive crew data, facilitating numerous applications across research, medical, and rehabilitation groups. Collaborations with the Anthropometry and Biomechanics Facility will provide further comparisons of the Markerless system to a marker-based system. ACKNOWLEDGEMENT</ This work is supported by NASA’s Exploration Systems Development Mission Directorate Mars Campaign Office Crew Health Countermeasures.

Hannah M. Weiss↗
DOE OSTI2026

In-Situ Visualization of a Growing Brittle Crack in Aluminum Oxynitride Using Synchrotron X-Rays and the Double-Cleavage Drilled Compression Geometry

Brittle fracture is difficult to study in situ due to the speed of a growing crack and the often-catastrophic nature of failure in brittle materials. As a result, the influence of microstructural considerations, such as orientation, grain boundary locations, and strain field, on the crack path remains poorly understood. Presented in this study is a method addressing this knowledge gap, which utilizes the double-cleavage drilled compression geometry to achieve quasi-stable fracture in aluminum oxynitride (AlON). Synchrotron X-ray micro-computed tomography is used to characterize the crack shape and length, while high-energy diffraction microscopy provides information on the strains, orientations, and shapes of grains in the microstructure surrounding the crack tip. During testing, the crack grew in discrete and irregular jumps while the fracture toughness falls within reported ranges. The crack in AlON is found to have no greater tendency to crack intergranularly as compared to transgranularly, and grains which are cracked transgranularly do not display a trend in orientation or stress when compared to those around which the crack followed a grain boundary. The high resolution of the crack path and microstructural data provides a path forward for modeling and understanding 3D brittle fracture.

Gorske, Sara F.↗
DOE OSTI2026

High-speed quantitative X-ray multi-contrast imaging with deep learning based modulated pattern analysis

The advent of X-ray multi-contrast imaging methods, providing absorption, phase, and dark-field images, holds tremendous promise for complementary and non-destructive visualization of inner structures within materials and bio-samples. However, the low efficiency in measuring and analyzing X-ray modulated patterns has hindered their application in high-resolution in situ imaging. In this work, the Enhanced Scanning Pattern-based Imaging Neural Network (ESPINNet) is introduced as a powerful tool for achieving high-speed, high-resolution quantitative imaging. ESPINNet is faster than correlation-based speckle tracking methods such as XSVT and UMPA, and provides a balanced performance in terms of resolution and speed for data collection by using fewer scanning images. In comparison with our previously developed neural network, ESPINNet introduces the capability to generate dark-field images, further enhancing its versatility. By leveraging scanning patterns, ESPINNet significantly improves resolution and measurement precision. Furthermore, its adaptability to various modulation patterns, including those produced by sandpaper, coded masks, or gratings, ensures broad applicability. These features enable real-time 2D and 3D multi-contrast imaging, positioning ESPINNet as a transformative solution for applications in materials science and biomedical research, particularly for high-speed and in situ measurements.

X-ray at-wavelength metrology↗
DOE OSTI2024

High redshift LBGs from deep broadband imaging for future spectroscopic surveys

Lyman break galaxies (LBGs) are promising probes for clustering measurements at high redshift, z > 2, a region only covered so far by Lyman-α forest measurements. Here, in this paper, we investigate the feasibility of selecting LBGs by exploiting the existence of a strong deficit of flux shortward of the Lyman limit, due to various absorption processes along the line of sight. The target selection relies on deep imaging data from the HSC and CLAUDS surveys in the g, r, z and u bands, respectively, with median depths reaching 27 AB in all bands. The selections were validated by several dedicated spectroscopic observation campaigns with DESI. Visual inspection of spectra has enabled us to develop an automated spectroscopic typing and redshift estimation algorithm specific to LBGs. Based on these data and tools, we assess the efficiency and purity of target selections optimised for different purposes. Selections providing a wide redshift coverage retain 57% of the observed targets after spectroscopic confirmation with DESI, and provide an efficiency for LBGs of 83 ± 3%, for a purity of the selected LBG sample of 90 ± 2%. This would deliver a confirmed LBG density of ~ 620 deg$^{-2}$ in the range 2.3 < z < 3.5 for a r-band limiting magnitude r < 24.2. Selections optimised for high redshift efficiency retain 73% of the observed targets after spectroscopic confirmation, with 89 ± 4% efficiency for 97 ± 2% purity. This would provide a confirmed LBG density of ~ 470 deg$^{-2}$ in the range 2.8 < z < 3.5 for a r-band limiting magnitude r < 24.5.A preliminary study of the LBG sample 3d-clustering properties is also presented and used to estimate the LBG linear bias. A value of b$_{LBG}$ = 3.3 ± 0.2 (stat.) is obtained for a mean redshift of 2.9 and a limiting magnitude in r of 24.2, in agreement with results reported in the literature.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

Optical Navigation for Autonomous Approach of Small Unknown Bodies

State of the practice in navigation around small celestial bodies heavily relies on ground sup- port and human skill, in particular, for perception-based operations such as optical navigation and mapping. This leads to longer duration and more complex mission operations and sub- sequently higher cost. Furthermore, it imposes limitations for certain missions such as fast fly-bys or multi-agent operations. In this work, we present an autonomous navigation strat- egy suitable for approaching small unexplored bodies. During the approach, we estimate the body’s physical properties as well as the spacecraft’s relative trajectory and associated un- certainties. The autonomous navigation strategy, which is solely based on optical measure- ments, begins as soon as the body becomes resolved in the navigation camera and terminates at the start of proximity operations, when the spacecraft makes its first trajectory correction to stay in the vicinity of the body. Our strategy uses multiple image-processing algorithms: light-curve analysis for estimating the target body’s rotation rate, Shape-from-Silhouette for reconstructing the 3D shape and estimating its rotation pole, and feature tracking tailored to Small-Body images for estimating relative navigation parameters. We used the Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) developed by the Jet Propulsion Laboratory to evaluate the feasibility of this multi-phase navigation strategy using simulated images of an approach trajectory. We used the Rosetta mission data to generate photorealistic images to characterise the performance of this approach. This work is based on the assumptions that the spacecraft attitude is known, the body is a principal-axis rotator, a-priori estimates of ephemerides and scale are available, and the body is observed from a zero sun phase only during initial approach. Preliminary results show orbit determination performance that is on par with the human navigation from the Rosetta mission; albeit with a 1% bias in spacecraft-target radial distance estimate. The bias error is likely due to the robustness and accuracy of the visual tracking under dynamic lighting conditions and per- spective changes, which decrease accuracy.

Villa, Jacopo↗
NASA NTRS2012

NASA Tech Briefs, November 2012

The topics include: Visual System for Browsing, Analysis, and Retrieval of Data (ViSBARD); Time-Domain Terahertz Computed Axial Tomography NDE System; Adaptive Sampling of Time Series During Remote Exploration; A Tracking Sun Photometer Without Moving Parts; Surface Temperature Data Analysis; Modular, Autonomous Command and Data Handling Software with Built-In Simulation and Test; In-Situ Wire Damage Detection System; Amplifier Module for 260-GHz Band Using Quartz Waveguide Transitions; Wideband Agile Digital Microwave Radiometer; Buckyball Nucleation of HiPco Tubes; FACT, Mega-ROSA, SOLAROSA; An Integrated, Layered-Spinel Composite Cathode for Energy Storage Applications; Engineered Multifunctional Surfaces for Fluid Handling; Polyolefin-Based Aerogels; Adjusting Permittivity by Blending Varying Ratios of SWNTs; Gravity-Assist Mechanical Simulator for Outreach; Concept for Hydrogen-Impregnated Nanofiber/Photovoltaic Cargo Stowage System; DROP: Durable Reconnaissance and Observation Platform; Developing Physiologic Models for Emergency Medical Procedures Under Microgravity; Spectroscopic Chemical Analysis Methods and Apparatus; Low Average Sidelobe Slot Array Antennas for Radiometer Applications; Motion-Corrected 3D Sonic Anemometer for Tethersondes and Other Moving Platforms; Water Treatment Systems for Long Spaceflights; Microchip Non-Aqueous Capillary Electrophoresis (MicronNACE) Method to Analyze Long-Chain Primary Amines; Low-Cost Phased Array Antenna for Sounding Rockets, Missiles, and Expendable Launch Vehicles; Mars Science Laboratory Engineering Cameras; Seismic Imager Space Telescope; Estimating Sea Surface Salinity and Wind Using Combined Passive and Active L-Band Microwave Observations; A Posteriori Study of a DNS Database Describing Super critical Binary-Species Mixing; Scalable SCPPM Decoder; QuakeSim 2.0; HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler; MPST Software: MoonKommand

Source record↗
DOE OSTI2025

Visualizing Size-Dependent Dynamics of CeO 2-δ {100}-Supported CoO x Nanoparticles Under CO 2 Hydrogenation Conditions

Carbon dioxide is a major greenhouse gas. In order to optimize processes focused on its chemical valorization, one needs detailed information about the effects of CO 2 and/or CO 2 /H 2 mixtures on the structure and morphology of metal/oxide catalysts. Here, in this study, the evolution of a catalyst with cobalt supported on CeO 2 -cube nanostructures under CO 2 hydrogenation conditions was investigated by using a set of in situ characterization techniques (X-ray absorption fine structure, X-ray diffraction, diffuse reflectance infrared Fourier transform spectroscopy, and environmental transmission electron microscopy (TEM)). The {100} facets of the ceria support displayed an unexpectedly high stability due to strong interactions with the aggregates of cobalt oxide. A significant influence of interfacial bonding between CoO x and CeO 2-δ {100} is evident through a clear preference in the orientation of CoO x nanoparticles (NPs) with respect to the substrate. For initially reduced Co/CeO 2 -cube nanostructures, a kinetically controlled oxidation of cobalt upon the introduction of CO 2 was observed during the early stages of CO 2 hydrogenation. Environmental TEM revealed the size-dependent morphological behavior of cobalt oxide NPs due to strong interactions with the CeO 2 {100} surface. When the environment was switched from H 2 to a mixture of H 2 and CO 2 at 250 °C, small CoO x NPs (in the largest dimension < 2.5 nm) rapidly transformed from a pyramidal three-dimensional (3D) form to a planar, monatomic layer attached to the concurrently oxidized CeO 2-δ {100} surface. This maximizes the number of sites available for the binding of CO 2 or reaction intermediates. The shape transformation reflected the oxophilic character of cobalt and strong metal–support interactions. The removal of CO 2 from the gas phase led to a reduction of the cobalt oxide NPs by hydrogen and a reversible two-dimensional → 3D transformation. In contrast, no significant morphological changes, apart from further oxidation, were observed for big CoO x NPs (in the largest dimension > 3 nm). These trends are not seen for nanoparticles of noble metals. The observed morphological and structural changes in the small CoO x NPs affected the stability of reaction intermediates and modified the selectivity of the CoO x /CeO 2 catalyst system for methane production.

36 MATERIALS SCIENCE↗
NASA NTRS2009

NASA Tech Briefs, March 2009

Topics covered include: Improved Instrument for Detecting Water and Ice in Soil; Real-Time Detection of Dust Devils from Pressure Readings; Determining Surface Roughness in Urban Areas Using Lidar Data; DSN Data Visualization Suite; Hamming and Accumulator Codes Concatenated with MPSK or QAM; Wide-Angle-Scanning Reflectarray Antennas Actuated by MEMS; Biasable Subharmonic Membrane Mixer for 520 to 600 GHz; Hardware Implementation of Serially Concatenated PPM Decoder; Symbolic Processing Combined with Model-Based Reasoning; Presentation Extensions of the SOAP; Spreadsheets for Analyzing and Optimizing Space Missions; Processing Ocean Images to Detect Large Drift Nets; Alternative Packaging for Back-Illuminated Imagers; Diamond Machining of an Off-Axis Biconic Aspherical Mirror; Laser Ablation Increases PEM/Catalyst Interfacial Area; Damage Detection and Self-Repair in Inflatable/Deployable Structures; Polyimide/Glass Composite High-Temperature Insulation; Nanocomposite Strain Gauges Having Small TCRs; Quick-Connect Windowed Non-Stick Penetrator Tips for Rapid Sampling; Modeling Unsteady Cavitation and Dynamic Loads in Turbopumps; Continuous-Flow System Produces Medical-Grade Water; Discrimination of Spore-Forming Bacilli Using spoIVA; nBn Infrared Detector Containing Graded Absorption Layer; Atomic References for Measuring Small Accelerations; Ultra-Broad-Band Optical Parametric Amplifier or Oscillator; Particle-Image Velocimeter Having Large Depth of Field; Enhancing SERS by Means of Supramolecular Charge Transfer; Improving 3D Wavelet-Based Compression of Hyperspectral Images; Improved Signal Chains for Readout of CMOS Imagers; SOI CMOS Imager with Suppression of Cross-Talk; Error-Rate Bounds for Coded PPM on a Poisson Channel; Biomorphic Multi-Agent Architecture for Persistent Computing; and Using Covariance Analysis to Assess Pointing Performance.

Source record↗
NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

Khlopenkov, Konstantin V.↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
NASA NTRS2007

GRMHD and GRPIC Simulations

We have developed a new three-dimensional general relativistic magnetohydrodynamic (GRMHD) code by using a conservative, high-resolution shock-capturing scheme. The numerical fluxes are calculated using the HLL approximate Riemann solver scheme. The flux-interpolated constrained transport scheme is used to maintain a divergence-free magnetic field. We have performed various 1-dimensional test problems in both special and general relativity by using several reconstruction methods and found that the new 3D GRMHD code shows substantial improvements over our previous code. The simulation results show the jet formations from a geometrically thin accretion disk near a nonrotating and a rotating black hole. We will discuss the jet properties depended on the rotation of a black hole and the magnetic field configuration including issues for future research. A General Relativistic Particle-in-Cell Code (GRPIC) has been developed using the Kerr-Schild metric. The code includes kinetic effects, and is in accordance with GRMHD code. Since the gravitational force acting on particles is extreme near black holes, there are some difficulties in numerically describing these processes. The preliminary code consists of an accretion disk and free-falling corona. Results indicate that particles are ejected from the black hole. These results are consistent with other GRMHD simulations. The GRPIC simulation results will be presented, along with some remarks and future improvements. The emission is calculated from relativistic flows in black hole systems using a fully general relativistic radiative transfer formulation, with flow structures obtained by GRMHD simulations considering thermal free-free emission and thermal synchrotron emission. Bright filament-like features protrude (visually) from the accretion disk surface, which are enhancements of synchrotron emission where the magnetic field roughly aligns with the line-of-sight in the co-moving frame. The features move back and forth as the accretion flow evolves, but their visibility and morphology are robust. We would like to extend this research using GRPIC simulations and examine a possible new mechanism for certain X-ray quasi-periodic oscillations (QPOs) observed in blackhole X-ray binaries.

Nishikawa, K.-I.↗
DOE OSTI2024

Nanoscale elemental and morphological imaging of nitrogen-fixing cyanobacteria

Nitrogen-fixing cyanobacteria bind atmospheric nitrogen and carbon dioxide using sunlight. This experimental study focused on a laboratory-based model system, Anabaena sp., in nitrogen-depleted culture. When combined nitrogen is scarce, the filamentous prokaryotes reconcile photosynthesis and nitrogen fixation by cellular differentiation into heterocysts. To better understand the influence of micronutrients on cellular function, 2D and 3D synchrotron X-ray fluorescence mappings were acquired from whole biological cells in their frozen-hydrated state at the Bionanoprobe, Advanced Photon Source. To study elemental homeostasis within these chain-like organisms, biologically relevant elements were mapped using X-ray fluorescence spectroscopy and energy-dispersive X-ray microanalysis. Higher levels of cytosolic K + , Ca 2+ , and Fe 2+ were measured in the heterocyst than in adjacent vegetative cells, supporting the notion of elevated micronutrient demand. P-rich clusters, identified as polyphosphate bodies involved in nutrient storage, metal detoxification, and osmotic regulation, were consistently co-localized with K + and occasionally sequestered Mg 2+ , Ca 2+ , Fe 2+ , and Mn 2+ ions. Machine-learning-based k-mean clustering revealed that P/K clusters were associated with either Fe or Ca, with Fe and Ca clusters also occurring individually. In accordance with XRF nanotomography, distinct P/K-containing clusters close to the cellular envelope were surrounded by larger Ca-rich clusters. The transition metal Fe, which is a part of nitrogenase enzyme, was detected as irregularly shaped clusters. The elemental composition and cellular morphology of diazotrophic Anabaena sp. was visualized by multimodal imaging using atomic force microscopy, scanning electron microscopy, and fluorescence microscopy. This paper discusses the first experimental results obtained with a combined in-line optical and X-ray fluorescence microscope at the Bionanoprobe.

Anabaena sp↗
DOE OSTI2025

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

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

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗