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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Multidimensional Processing and Visual Rendering of Complex 3D Biomedical Images

The proposed technology uses advanced image analysis techniques to maximize the resolution and utility of medical imaging methods being used during spaceflight. We utilize COTS technology for medical imaging, but our applications require higher resolution assessment of the medical images than is routinely applied with nominal system software. By leveraging advanced data reduction and multidimensional imaging techniques utilized in analysis of Planetary Sciences and Cell Biology imaging, it is possible to significantly increase the information extracted from the onboard biomedical imaging systems. Year 1 focused on application of these techniques to the ocular images collected on ground test subjects and ISS crewmembers. Focus was on the choroidal vasculature and the structure of the optic disc. Methods allowed for increased resolution and quantitation of structural changes enabling detailed assessment of progression over time. These techniques enhance the monitoring and evaluation of crew vision issues during space flight.

Sams, Clarence F.↗
NASA NTRS2026

Radiation-Conditioned Ti–hBN Coatings for Space Mechanisms: Bridging Tribology and Irradiation Effects from Laboratory to Low-Earth Orbit

The longevity of tribological components in extraterrestrial environments is challenged by abrasive regolith, extreme temperatures, and ionizing radiation. This study evaluates the performance of vacuum plasma sprayed (VPS) Ti-2vol.% hexagonal boron nitride (hBN) coatings applied to joint mechanisms such as rod and slot, ball and socket, and hinge assemblies, fabricated from Al6061 and Ti6Al4V for planetary structures. Coated and uncoated samples were tested under vacuum with lunar regolith simulant (JSC-1A), followed by environmental exposures including electron radiation and thermal cycling. Results show that coated samples consistently outperformed uncoated counterparts across all configurations. Wear depth in coated components was reduced by over 80%, as confirmed by 3D profilometry and surface imaging. SEM analysis revealed severe abrasion and localized plastic deformation in uncoated surfaces, while coated samples retained structural integrity due to synergy of hard protective phases formed during deposition and retained hBN providing solid lubrication in the sliding surface. Coated and exposed samples exhibited smoother actuation profiles and reduced friction, indicating a beneficial "conditioning" effect from environmental exposure. The coatings-maintained performance even on complex geometries with minimal delamination. These findings demonstrate the potential of VPS Ti-hBN coatings to extend the operational life of lunar mechanisms, bridging laboratory-scale tribology with flight-relevant mechanical applications.

tribology↗
NASA NTRS2014

Multi-Purpose Crew Vehicle Camera Asset Planning: Imagery Previsualization

Using JSC-developed and other industry-standard off-the-shelf 3D modeling, animation, and rendering software packages, the Image Science Analysis Group (ISAG) supports Orion Project imagery planning efforts through dynamic 3D simulation and realistic previsualization of ground-, vehicle-, and air-based camera output.

Beaulieu, K.↗
NASA NTRSDate not supplied

3d Particle Geometry Characterization of Apollo Regolith Samples Via Laser Diffraction and Dynamic Image Analysis

Establishing a fundamental understanding of regolith mechanics hinges on the characterization of the foundational physical parameters that dictate granular particle interactions both on micro- and macro-scales. Specifically, particle size distribution, particle geometry and grain density govern the geotechnical properties, including compressibility, shear strength, hydraulic conductivity (i.e., permeability), void ratio (i.e., porosity), and thermal conductivity of the lunar regolith. The absence of terrestrial geologic processes on the lunar surface, a lack of water, clay minerals, and organic material in lunar soils, and the narrow compositional range in terms of mineral diversity of the regolith produces a unique problem when attempting to project terrestrial soil mechanics concepts to the lunar surface. Additionally, the occurrence of agglutinates, exclusive to the lunar surface, further disassociates the physical behavior (e.g., particle crushability, flowability, etc.) of regolith from that observed in terrestrial soils.

R. Kovtun↗
DOE OSTI2024

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

3D instance segmentation↗
DOE OSTI2024

Utah FORGE: Southwestern Utah Magnetotelluric (MT) Data

This comprehensive magnetotellurics (MT) dataset, which covers southwestern Utah, integrates 600 sites from various surveys, including those from the Utah FORGE, SubTER, and Play Fairway projects, all of which are linked below. The core of this dataset is the use of a 3D finite element (FE) algorithm for inversion imaging, allowing for detailed analysis of the MT data. Provided here are all .edi files, the 3D finite element mesh (with a readme file), graphic slices, node graphics, resistivity graphics, and reports on the acquisition areas and inversion.

15 GEOTHERMAL ENERGY↗
NASA NTRS1992

Cartography of asteroids and comet nuclei from low resolution data

High resolution images of non-spherical objects, such as Viking images of Phobos and the anticipated Galileo images of Gaspra, lend themselves to conventional planetary cartographic procedures: control network analysis, stereophotogrammetry, image mosaicking in 2D or 3D, and airbrush mapping. There remains the problem of a suitable map projection for bodies which are extremely elongated or irregular in shape. Many bodies will soon be seen at lower resolution (5-30 pixels across the disk) in images from speckle interferometry, the Hubble Space Telescope, ground-based radar, distinct spacecraft encounters, and closer images degraded by smear. Different data with similar effective resolutions are available from stellar occultations, radar or lightcurve convex hulls, lightcurve modeling of albedo variations, and cometary jet modeling. With such low resolution, conventional methods of shape determination will be less useful or will fail altogether, leaving limb and terminator topography as the principal sources of topographic information. A method for shape determination based on limb and terminator topography was developed. It has been applied to the nucleus of Comet Halley and the jovian satellite Amalthea. The Amalthea results are described to give an example of the cartographic possibilities and problems of anticipated data sets.

Stooke, Philip J.↗
DOE OSTI2023

Multi-Scale Characterization of Porosity and Cracks in Silicon Carbide Cladding after Transient Reactor Test Facility Irradiation

Silicon carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuel (ATF) cladding for light water reactor applications. The morphology of fabrication defects, including the size and shape of voids, is one of the key challenges that impacts cladding performance and guarantees reactor safety. Therefore, quantification of defects’ size, location, distribution, and leak paths is critical to determining SiC CMC in-core performance. This research aims to provide quantitative insight into the defect’s distribution under multi-scale characterization at different length scales before and after different Transient Reactor Test Facility (TREAT) irradiation tests. A non-destructive multi-scale evaluation of irradiated SiC will help to assess critical microstructural defects from production and/or experimental testing to better understand and predict overall cladding performance. X-ray computed tomography (XCT), a non-destructive, data-rich characterization technique, is combined with lower length scale electronic microscopic characterization, which provides microscale morphology and structural characterization. This paper discusses a fully automatic workflow to detect and analyze SiC-SiC defects using image processing techniques on 3D X-ray images. Following the XCT data analysis, advanced characterizations from focused ion beam (FIB) and transmission electron microscopy (TEM) were conducted to verify the findings from the XCT data, especially quantitative results from local nano-scale TEM 3D tomography data, which were utilized to complement the 3D XCT results. In this work, three SiC samples (two irradiated and one unirradiated) provided by General Atomics are investigated. The irradiated samples were irradiated in a way that was expected to induce cracking, and indeed, the automated workflow developed in this work was able to successfully identify and characterize the defects formation in the irradiated samples while detecting no observed cracking in the unirradiated sample. These results demonstrate the value of automated XCT tools to better understand the damage and damage propagation in SiC-SiC structures for nuclear applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Utilizing 3D-DIC on Mars 2020 Rover Wheel Assembly: Test-Analysis Correlation

Following the successful implementation of full-field photogrammetry, more specifically three-dimensional Digital Image Correlation (3D-DIC), on the Mars 2020 Heat Shield Structural Failure Review assessment, 3D-DIC was selected as one of the primary measurement techniques for the Mars 2020 Rover wheel assembly qualification test at the NASA Jet Propulsion Laboratory (JPL). To validate the Rover wheel landing loads simulations, it was extremely important to have high confidence in the wheel models. Due to the large deformations and strains that the wheel would be subject to during landing, traditional instrumentation such as linear variable displacement transducers (LVDTs), electrical-resistance strain gages and string potentiometers, would not be sufficient on their own to provide all the necessary validation data. Therefore, the NASA Engineering and Safety Center (NESC) provided the 3D-DIC expertise and support to measure the high deformation and strain in the wheel flexures and qualify the overall structural response of the Mars 2020 rover wheel assembly. There were two key objectives for the photogrammetry technique: (1) monitor the wheel response in real-time, guarding against anomalous behavior and failure, and (2) provide test data for test-analysis correlation to validate and/or improve the high-fidelity computational model. The contents of this paper will focus on the challenges of applying 3D-DIC to the Mars 2020 Rover wheel assembly and how these challenges were overcome. Examples of test-analysis correlation during the stiffness characterization and structural qualification will be presented and discussed in detail. Experimental results were compared with the analysis and showed excellent agreement between the predicted behavior and helped validate the high-fidelity models.

Mars 2020 Rover Wheel↗
DOE OSTI2022

Neutron tomography of porous aluminum electrodes used in electrocoagulation of groundwater

In this work, neutron computed tomography (CT) is employed to investigate the dissolution of porous aluminum electrodes during electrocoagulation (EC). Porous electrodes were chosen in efforts to reduce electric power requirements by using larger surface-area electrodes, having both inner and outer surface, for the EC process. Neutron CT allowed 3D reconstruction of the porous electrodes, and image analysis provided the volume of each electrode vs. thickness, which can indicate whether the inner surface is effectively involved in EC reactions. For the anode, the volume decreased uniformly throughout the thickness of the electrode, indicating that both the outer and inner surface participated in electrochemical dissolution, while the volume of the cathode increased uniformly vs. thickness, indicating deposition of material on both the outer and inner surface. The attenuation coefficient vs. thickness, increased for both anode and cathode, indicating surface chemistry changes. For the anode, the attenuation coefficient increased slightly but uniformly, probably due to aluminum oxide formation on the surface of the anode. For the cathode, the attenuation coefficient increased more than for the anode and nonuniformly. The higher increase in the attenuation coefficient for the cathode is due to precipitation of aluminum hydroxide on the electrode surface, which added hydrogen. Image analysis also showed that, although the attenuation coefficient increased throughout the thickness of the electrode, most of the hydroxide deposition occurred on the outer surface. Energy analysis showed that porous electrodes can be used to reduce process energy requirements by as much as 4 times compared to solid electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Stereo Imaging Velocimetry Technique Using Standard Off-the-Shelf CCD Cameras

Stereo imaging velocimetry is a fluid physics technique for measuring three-dimensional (3D) velocities at a plurality of points. This technique provides full-field 3D analysis of any optically clear fluid or gas experiment seeded with tracer particles. Unlike current 3D particle imaging velocimetry systems that rely primarily on laser-based systems, stereo imaging velocimetry uses standard off-the-shelf charge-coupled device (CCD) cameras to provide accurate and reproducible 3D velocity profiles for experiments that require 3D analysis. Using two cameras aligned orthogonally, we present a closed mathematical solution resulting in an accurate 3D approximation of the observation volume. The stereo imaging velocimetry technique is divided into four phases: 3D camera calibration, particle overlap decomposition, particle tracking, and stereo matching. Each phase is explained in detail. In addition to being utilized for space shuttle experiments, stereo imaging velocimetry has been applied to the fields of fluid physics, bioscience, and colloidal microscopy.

McDowell, Mark↗
NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗
NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface. In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha-Ray↗
NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗
NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗
NASA NTRSDate not supplied

Skin Friction Measurement on a Swept NACA 0015 Wing Using Oil Film Interferometry

An application of oil film interferometry (OFI) to measure skin friction on the 3D flow over an NACA 0015 airfoil with 30° of sweep is presented. Local flow direction must be known to apply OFI to complex 3D flows such as this. A recently developed surface flow vector extraction method was integrated into the OFI analysis algorithm. Surface flow vectors were used to calculate the surface streamlines and hence provided local flow direction to the OFI analysis. After calibration using photogrammetry, the surface flow vectors also enabled decomposition of the skin friction coefficient into different components (such as chordwise and spanwise). The presented method successfully measured skin friction at locations with different flow characteristics such as high skin friction regions, strong spanwise flow regions, and 3D flow separated regions. In the current OFI analysis, multiple interferogram images acquired at different times were analyzed for the same location. The multiple interferogram analysis enabled averaging, which reduced the scatter the skin friction data. The results presented herein demonstrate the capability of the current approach, thereby extending OFI skin friction measurements to complex 3D flows.

Skin friction measurement↗
NASA NTRSDate not supplied

Skin Friction Measurement on a Swept NACA 0015 Wing Using Oil Film Interferometry

An application of oil film interferometry (OFI) to measure skin friction on the 3D flow over an NACA 0015 airfoil with 30° of sweep is presented. Local flow direction must be known to apply OFI to complex 3D flows such as this. A recently developed surface flow vector extraction method was integrated into the OFI analysis algorithm. Surface flow vectors were used to calculate the surface streamlines and hence provided local flow direction to the OFI analysis. After calibration using photogrammetry, the surface flow vectors also enabled decomposition of the skin friction coefficient into different components (such as chordwise and spanwise). The presented method successfully measured skin friction at locations with different flow characteristics such as high skin friction regions, strong spanwise flow regions, and 3D flow separated regions. In the current OFI analysis, multiple interferogram images acquired at different times were analyzed for the same location. The multiple interferogram analysis enabled averaging, which reduced the scatter the skin friction data. The results presented herein demonstrate the capability of the current approach, thereby extending OFI skin friction measurements to complex 3D flows.

Skin friction measurement↗